Introduction to Mathematics With Maple

Introduction to Mathematics With Maple
IflTRODUCTIOII TO
MATtlFMATIC5
WITH
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InTRODUCTlOn TO
M A T t l f MAT10
N E W JERSEY * L O N O O N
-
SINGAPORE * B E l J l N G
S H A N G H A I * HONG KONG
TAIPEI * C H E N N A I
Published by
World Scientific Publishing Co. Pte. Ltd.
5 Toh Tuck Link, Singapore 596224
USA ofice: Suite 202, 1060 Main Street, River Edge, NJ 07661
UK ofice: 57 Shelton Street, Covent Garden, London WCZH 9HE
British Library Cataloguing-in-PublicationData
A catalogue record for this book is available from the British Library.
INTRODUCTION TO MATHEMATICS WITH MAPLE
Copyright 0 2004 by World Scientific Publishing Co. Pte. Ltd.
All rights reserved. This book, or parts thereoJ may not be reproduced in any form or by any means,
electronic or mechanical, includingphotocopying, recording or any information storage and retrieval
system now known or to be invented, without written permission from the Publisher.
For photocopying of material in this volume, please pay a copying fee through the Copyright
Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to
photocopy is not required from the publisher.
ISBN 98 1-238-931-8
ISBN 98 1-256-009-2 (pbk)
Printed by FuIsland Offset Printing (S) Pte Ltd, Singapore
To
Narelle, Helen and Aia
for their understanding and support
while we were writing this book.
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Preface
I attempted mathematics, and even went during the summer of 1828 with a private tutor to Barmouth, but I got
on very slowly. The work was repugnant to me, chiefly
from my not being able to see any meaning in the early
steps in algebra. This impatience was very foolish, and in
after years I have deeply regretted that I did not proceed
far enough at least to understand something of the great
leading principles of mathematics, for men thus endowed
seem to have an extra sense.
Charles Darwin, Autobiography (1876)
Charles Darwin wasn’t the last biologist to regret not knowing more
about mathematics. Perhaps if he had started his university education at
the beginning of the 21St century, instead of in 1828, and had been able
to profit from using a computer algebra package, he would have found the
material less forbidding.
This book is about pure mathematics. Our aim is to equip the readers
with understanding and sufficiently deep knowledge to enable them to use it
in solving problems. We also hope that this book will help readers develop
an appreciation of the intrinsic beauty of the subject!
We have said that this book is about mathematics. However, we make
extensive use of the computer algebra package Maple in our discussion.
Many books teach pure mathematics without any reference to computers,
whereas other books concentrate too heavily on computing, without explaining substantial mathematical theory. We aim for a better balance:
we present material which requires deep thinking and understanding, but
we also fully encourage our readers to use Maple to remove some of the
laborious computations, and to experiment. To this end we include a large
number of Maple examples.
vii
viii
Introduction to Mathematics with Maple
Most of the mathematical material in this book is explained in a fairly
traditional manner (of course, apart from the use of Maple!). However,
we depart from the traditional presentation of integral by presenting the
Kurzweil-Henstock theory in Chapter 15.
Outline of the book
There are fifteen chapters. Each starts with a short abstract describing the
content and aim of the chapter.
In Chapter 1, “Introduction”, we explain the scope and guiding philosophy of the present book, and we make clear its logical structure and the
role which Maple plays in the book. It is our aim to equip the readers with
sufficiently deep knowledge of the material presented so they can use it in
solving problems, and appreciate its inner beauty.
In Chapter 2, “Sets”, we review set theoretic terminology and notation
and provide the essential parts of set theory needed for use elsewhere in
the book. The development here is not strictly axiomatic-that would
require, by itself, a book nearly as large as this one-but gives only the most
important parts of the theory. Later we discuss mathematical reasoning,
and the importance of rigorous proofs in mathematics.
In Chapter 3, “Functions”, we introduce relations, functions and various notations connected with functions, and study some basic concepts
intimately related to functions.
In Chapter 4, “Real Numbers)’, we introduce real numbers on an axiomatic basis, solve inequalities, introduce the absolute value and discuss
the least upper bound axiom. In the concluding section we outline an alternative development of the real number system, starting from Peano’s
axioms for natural numbers.
In Chapter 5, “Mathematical Induction”, we study proof by induction and prove some important inequalities, particularly the arithmeticgeometric mean inequality. In order to employ induction for defining new
objects we prove the so-called recursion theorems. Basic properties of powers with rational exponents are also established in this chapter.
In Chapter 6, “Polynomials”, we introduce polynomials. Polynomial
functions have always been important, if for nothing else than because, in
the past, they were the only functions which could be readily evaluated. In
this chapter we define polynomials as algebraic entities rather than func-
Preface
ix
tions and establish the long division algorithm in an abstract setting. We
also look briefly at zeros of polynomials and prove the Taylor Theorem for
polynomials in a generality which cannot be obtained by using methods of
calculus.
In Chapter 7, “Complex Numbers”, we introduce complex numbers,
that is, numbers of the form a bz where the number z satisfies z2 = -1.
Mathematicians were led to complex numbers in their efforts of solving algebraic equations, that is, of the form a,xn
un-lxn-’
a0 = 0,
with the arc real numbers and n a positive integer (this problem is perhaps
more widely known as finding the zeros of a polynomial). Our introduction
follows the same idea although in a modern mathematical setting. Complex
numbers now play important roles in physics, hydrodynamics, electromagnetic theory and electrical engineering, as well as pure mathematics.
In Chapter 8, “Solving Equations”, we discuss the existence and uniqueness of solutions to various equations and show how to use Maple to find
solutions. We deal mainly with polynomial equations in one unknown, but
include some basic facts about systems of linear equations.
In Chapter 9, “Sets Revisited”, we introduce the concept of equivalence
for sets and study countable sets. We also briefly discuss the axiom of
choice.
In Chapter 10, “Limits of Sequences”, we introduce the idea of the limit
of a sequence and prove basic theorems on limits. The concept of a limit is
central to subsequent chapters of this book. The later sections are devoted
to the general principle of convergence and more advanced concepts of limits
superior and limits inferior of a sequence.
In Chapter 11, “Series”, we introduce infinite series and prove some basic
convergence theorems. We also introduce power series-a very powerful tool
in analysis.
In Chapter 12, “Limits and Continuity of Functions”, we define limits of
functions in terms of limits of sequences. With a function f continuous on
an interval we associate the intuitive idea of the graph f being drawn without lifting the pencil from the drawing paper. The mathematical treatment
of continuity starts with the definition of a function continuous at a point;
this definition is given here in terms of a limit of a function at a point. We
develop the theory of limits of functions, study continuous functions, and
particularly functions continuous on closed bounded intervals. At the end
of the chapter we touch upon the concept of limit superior and inferior of
a function.
In Chapter 13, “Derivatives”, we start with the informal description
+
+
+ +
X
Introduction to Mathematics with Maple
of a derivative as a rate of change. This concept is extremely important
in science and applications. In this chapter we introduce derivatives as
limits, establish their properties and use them in studying deeper properties
of functions and their graphs. We also extend the Taylor Theorem from
polynomials to power series and explore it for applications.
In Chapter 14, “Elementary Functions”, we lay the proper foundations
for the exponential and logarithmic functions, and for trigonometric functions and their inverses. We calculate derivatives of these functions and use
these for establishing important properties of these functions.
In Chapter 15, “Integrals”, we present the theory of integration introduced by the contemporary Czech mathematician J. Kurzweil. Sometimes
it is referred to as Kurzweil-Henstock theory. Our presentation generally
follows Lee and Vfbornf (2000, Chapter 2).
The Appendix contains some examples of Maple programs. Finally, the
book concludes with a list of References, an Index of Maple commands used
in the book, and a general Index.
Notes on notation
Throughout the book there are a number of ways in which the reader’s
attention is drawn to particular points. Theorems, lemmas’ and corollaries
are placed inside rectangular boxes with double lines, as in
Theorem 0.1 (For illustrative purposes only!) This theorem is
referred to only in the Preface, and can safely be ignored when reading
the rest of the book.
Note that these are set in slanting font, instead of the upright font used in
the bulk of the book.
Definitions are set in the normal font, and are placed within rectangular
boxes, outlined by a single line and with rounded corners, as in
Defintion 0.1 (What is mathematics?) There are almost as
many definitions of what mathematics is as there are professional mathematicians living at the time.
‘A lemma is sometimes known as an auxiliary theorem. It does not have the same
level of significance as a theorem, and is usually proved separately to simplify the proof
of the related theorem(s).
xi
Preface
The number before the decimal point in all of the above is the number
of the chapter: numbers following the decimal point label the different
theorems, definitions, examples, etc., and are numbered consecutively (and
separately) within each chapter. Corollaries are labeled by the number of
the chapter, followed by the number of the theorem to which the corollary
belongs then followed by the number of the individual corollary for that
theorem, as in
Corollary 0.1.1 (Also for illustrative purposes only!) Since
Theorem 0.1 is referred to only in the Preface, any of its corollaries
can also be ignored when reading the rest of the book.
Corollary 0.1.2 (Second corollary for Theorem 0.1)
j u s t as helpful as the first corollary for the theorem!
This is
Corollaries are set in the same font as theorems and lemmas.
Most of the theorems, lemmas and corollaries in this book are provided
with proofs. All proofs commence with the word “Proof.” flush with the
left margin. Since the words of a proof are set in the same font as the rest
of the book, a special symbol is used to mark the end of a proof and the
resumption of the main text. Instead of saying that a proof is complete, or
words to that effect, we shall place the symbol 0 at the end of the proof,
and flush with the right margin, as follows:
Proof. This is not really a proof. Its main purpose is to illustrate the
occurrence of a small hollow square, flush with the right margin, to indicate
the end of a proof.
0
Up to about the middle of the 20th century it was customary to use
the letters ‘q.e.d.’ instead of 0,q.e.d. being an abbreviation for quod
erut demonstrundum, which, translated from Latin, means which was to
be proved.
To assist the reader, there are a number of Remarks scattered throughout the book. These relate to the immediately preceding text. There are
also Examples of various kinds, used to illustrate a concept by providing a
(usually simple) case which can show the main distinguishing points of a
concept. Remarks and Examples are set in sans serif font, like this, to help
distinguish them.
Introduction to Mathematics with Maple
xii
Remark 0.1 The first book published on calculus was Sir Isaac Newton's
Philosophiae Naturalis Principia Mathernatica (Latin for The Mathematical
Principles of Natural Philosophy), commonly referred to simply as Principia.
As might be expected from the title, this was in Latin. We shall avoid the use
of languages other than English in this book.
Since they use a different font, and have additional spacing above and
below, it is obvious where the end of a Remark or an Example occurs, and
no special symbol is needed t o mark the return to the main text.
Scissors in the margin
Obviously, we must build on some previous knowledge of our readers. Chapter 1 and Chapter 2 summarise such prerequisites. Chapter 1 contains also
a brief introduction to Maple. Starting with Chapter 3 we have tried to
make sure that all proofs are in a strict logical order. On a few occasions
we relax the logical requirements in order to illustrate some point or to
help the reader place the material in a wider context. All such instances
are clearly marked in the margin (see the outer margin of this page), the
beginning by scissors pointing into the book, the end by scissors opening
outwards. The idea here is to indicate readers can skip over these sections
if they desire strict logical purity. For instance, we might use trigonometric
functions before they are properly introduced,2 but then this example will
be scissored.
Exercises
There are exercises to help readers to master the material presented. We
hope readers will attempt as many as possible. Mathematics is learned
by doing, rather than just reading. Some of the exercises are challenging,
and these are marked in the margin by the symbol 0.
We do not expect
that readers will make an effort to solve all these challenging problems,
but should attempt at least some. Exercises containing fairly important
additional information, not included in the main body of text, are marked
by 0.
We recommend that these should be read even if no attempt is made
to solve them.
2Rather late in Chapter 14
*
*
Preface
xiii
Acknowledgments
The authors wish to gratefully acknowledge the support of Waterloo Maple
Inc, who provided us with copies of Maple version 7 software. We thank the
Mathematics Department at The University of Queensland for its support
and resourcing. We also thank those students we have taught over many
years, who, by their questions, have helped us improve our teaching.
Our greatest debt is to our wives. As a small token of our love and
appreciation we dedicate this book to them.
Peter Adams
K e n Smith
Rudolf Vy’borny’
The University of Queensland, February 2004.
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Contents
Preface
vii
1. Introduction
1.1
1.2
1.3
1.4
1.5
1.6
1
Ouraims . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introducing Maple . . . . . . . . . . . . . . . . . . . . . .
1.2.1 What is Maple? . . . . . . . . . . . . . . . . . . . . .
1.2.2 Starting Maple . . . . . . . . . . . . . . . . . . . . .
1.2.3 Worksheets in Maple . . . . . . . . . . . . . . . . . .
1.2.4 Entering commands into Maple . . . . . . . . . . . .
1.2.5 Stopping Maple . . . . . . . . . . . . . . . . . . . . .
1.2.6 Using previous results . . . . . . . . . . . . . . . . .
1.2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . .
Help and error messages with Maple . . . . . . . . . . . .
1.3.1 Help . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Error messages . . . . . . . . . . . . . . . . . . . . .
Arithmetic in Maple . . . . . . . . . . . . . . . . . . . . .
1.4.1 Basic mathematical operators . . . . . . . . . . . . .
1.4.2 Special mathematical constants . . . . . . . . . . . .
1.4.3 Performing calculations . . . . . . . . . . . . . . . . .
1.4.4 Exact versus floating point numbers . . . . . . . . .
Algebra in Maple . . . . . . . . . . . . . . . . . . . . . . .
1.5.1 Assigning variables and giving names . . . . . . . . .
1.5.2 Useful inbuilt functions . . . . . . . . . . . . . . . . .
Examples of the use of Maple . . . . . . . . . . . . . . . .
2 . Sets
1
4
4
4
5
6
8
8
9
9
9
10
11
11
12
12
13
18
18
20
26
33
xv
Introduction to Mathematics with Maple
xvi
2.1
2.2
2.3
2.4
2.5
Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Union, intersection and difference of sets . . . . . . .
2.1.2 Sets in Maple . . . . . . . . . . . . . . . . . . . . . .
2.1.3 Families of sets . . . . . . . . . . . . . . . . . . . . .
2.1.4 Cartesian product of sets . . . . . . . . . . . . . . . .
2.1.5 Some common sets . . . . . . . . . . . . . . . . . . .
Correct and incorrect reasoning . . . . . . . . . . . . . . .
Propositions and their combinations . . . . . . . . . . . .
Indirect proof . . . . . . . . . . . . . . . . . . . . . . . . .
Comments and supplements . . . . . . . . . . . . . . . . .
2.5.1 Divisibility: An example of an axiomatic theory . . .
3. Functions
3.1
3.2
3.3
3.4
3.5
3.6
3.7
63
Relations . . . . . . . . . . . . . . . . . . . . . . . . . . .
63
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . .
68
Functions in Maple . . . . . . . . . . . . . . . . . . . . . .
75
3.3.1 Library of functions . . . . . . . . . . . . . . . . . . . 75
3.3:2 Defining functions in Maple . . . . . . . . . . . . . . 77
3.3.3 Boolean functions . . . . . . . . . . . . . . . . . . . .
80
3.3.4 Graphs of functions in Maple . . . . . . . . . . . . . 81
Composition of functions . . . . . . . . . . . . . . . . . . . 89
Bijections . . . . . . . . . . . . . . . . . . . . . . . . . . .
90
Inverse functions . . . . . . . . . . . . . . . . . . . . . . .
91
Comments . . . . . . . . . . . . . . . . . . . . . . . . . . .
95
4 . Real Numbers
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
97
Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Order axioms . . . . . . . . . . . . . . . . . . . . . . . . .
Absolute value . . . . . . . . . . . . . . . . . . . . . . . .
Using Maple for solving inequalities . . . . . . . . . . . . .
Inductive sets . . . . . . . . . . . . . . . . . . . . . . . . .
The least upper bound axiom . . . . . . . . . . . . . . . .
Operation with real valued functions . . . . . . . . . . . .
Supplement. Peano axioms. Dedekind cuts . . . . . . . .
5 . Mathematical Induction
5.1
5.2
33
35
38
42
43
43
45
47
51
53
56
Inductive reasoning . . . . . . . . . . . . . . . . .
Aimhigh! . . . . . . . . . . . . . . . . . . . . . .
97
100
105
108
112
114
120
121
129
.....
.....
129
135
xvii
Contents
Notation for sums and products . . . . . . . . . . . . . . .
5.3.1 Sums in Maple . . . . . . . . . . . . . . . . . . . . .
5.3.2 Products in Maple . . . . . . . . . . . . . . . . . . .
5.4 Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5 Inductive definitions . . . . . . . . . . . . . . . . . . . . .
5.6 The binomial theorem . . . . . . . . . . . . . . . . . . . .
5.3
5.7
5.8
5.9
5.10
5.11
Roots and powers with rational exponents . . . . . . . . .
Some important inequalities . . . . . . . . . . . . . . . . .
Complete induction . . . . . . . . . . . . . . . . . . . . . .
Proof of the recursion theorem . . . . . . . . . . . . . . .
Comments . . . . . . . . . . . . . . . . . . . . . . . . . . .
6 . Polynomials
6.1 Polynomial functions . . . . . . . . . . . . . . . . . . . . .
6.2 Algebraic viewpoint . . . . . . . . . . . . . . . . . . . . .
6.3 Long division algorithm . . . . . . . . . . . . . . . . . .
6.4 Roots of polynomials . . . . . . . . . . . . . . . . . . . . .
6.5 The Taylor polynomial . . . . . . . . . . . . . . . . . . . .
6.6 Factorization . . . . . . . . . . . . . . . . . . . . . . . . .
136
141
143
145
146
149
152
157
161
164
166
167
.
167
169
175
178
180
184
7. Complex Numbers
7.1 Field extensions . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Complex numbers . . . . . . . . . . . . . . . . . . . . . . .
7.2.1 Absolute value of a complex number . . . . . . . . .
7.2.2 Square root of a complex number . . . . . . . . . . .
7.2.3 Maple and complex numbers . . . . . . . . . . . . . .
7.2.4 Geometric representation of complex numbers.
Trigonometric form of a complex number . . . . . . .
7.2.5 The binomial equation . . . . . . . . . . . . . . . . .
199
202
8. Solving Equations
207
General remarks . . . . . . . . . . . . . . . . . . . . . . .
Maple commands solve and f solve . . . . . . . . . .
Algebraic equations . . . . . . . . . . . . . . . . . . . . . .
8.3.1 Equations of higher orders and f solve . . . . . .
8.4 Linear equations in several unknowns . . . . . . . . . .
8.1
8.2
8.3
9 . Sets Revisited
191
191
194
196
197
199
207
. . 209
213
. . 225
. . 228
231
Introduction to Mathematics with Maple
xviii
9.1
Equivalent sets
........................
231
239
10. Limits of Sequences
10.1
10.2
10.3
10.4
10.5
10.6
10.7
10.8
....................
....................
Limits of sequences in Maple . . . . . . . . . . . . . . . .
Monotonic sequences . . . . . . . . . . . . . . . . . . . . .
Infinite limits . . . . . . . . . . . . . . . . . . . . . . . . .
Subsequences . . . . . . . . . . . . . . . . . . . . . . . . .
Existence theorems . . . . . . . . . . . . . . . . . . . . . .
Comments and supplements . . . . . . . . . . . . . . . . .
The concept of a limit
Basic theorems . . . .
11. Series
11.1
11.2
11.3
11.4
11.5
11.6
11.7
11.8
279
Definition of convergence . . . . . . . . . . . . . . . . . .
Basic theorems . . . . . . . . . . . . . . . . . . . . . . . .
Muple and infinite series . . . . . . . . . . . . . . . . . .
Absolute and conditional convergence . . . . . . . . . .
Rearrangements . . . . . . . . . . . . . . . . . . . . . . . .
Convergence tests . . . . . . . . . . . . . . . . . . . . . . .
Power series . . . . . . . . . . . . . . . . . . . . . . . . . .
Comments and supplements . . . . . . . . . . . . . . . .
11.8.1 More convergence tests. . . . . . . . . . . . . . . .
11.8.2 Rearrangements revisited . . . . . . . . . . . . . . .
11.8.3 Multiplication of series. . . . . . . . . . . . . . . . .
11.8.4 Concluding comments . . . . . . . . . . . . . . . .
. 279
285
. 289
.
.
.
.
.
.
12. Limits and Continuity of Functions
290
295
297
300
303
303
306
307
309
313
12.1 Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.1.1 Limits of functions in Maple . . . . . . . . . . . . . .
12.2 The Cauchy definition . . . . . . . . . . . . . . . . . . . .
12.3 Infinite limits . . . . . . . . . . . . . . . . . . . . . . . . .
12.4 Continuity at a point . . . . . . . . . . . . . . . . . . . . .
12.5 Continuity of functions on closed bounded intervals . . . .
12.6 Comments and supplements . . . . . . . . . . . . . . . . .
13. Derivatives
13.1 Introduction . . . . . . . . . . . . . .
13.2 Basic theorems on derivatives . . .
239
249
255
257
263
267
268
275
313
319
322
329
332
338
353
357
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357
362
Contents
13.3 Significance of the sign of derivative. . . . . . . . . . . . .
13.4 Higher derivatives . . . . . . . . . . . . . . . . . . . . . . .
13.4.1 Higher derivatives in Maple . . . . . . . . . . . . . .
13.4.2 Significance of the second derivative . . . . . . . . .
13.5 Mean value theorems . . . . . . . . . . . . . . . . . . . . .
13.6 The Bernoulli-1’Hospital rule . . . . . . . . . . . . . . . .
13.7 Taylor’s formula . . . . . . . . . . . . . . . . . . . . . . . .
13.8 Differentiation of power series . . . . . . . . . . . . . . . .
13.9 Comments and supplements . . . . . . . . . . . . . . . . .
14. Elementary Functions
14.1
14.2
14.3
14.4
14.5
14.6
14.7
369
380
381
382
388
391
394
398
401
407
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . .
407
The exponential function . . . . . . . . . . . . . . . . . . . 408
The logarithm . . . . . . . . . . . . . . . . . . . . . . . . .
411
The general power . . . . . . . . . . . . . . . . . . . . . .
415
Trigonometric functions . . . . . . . . . . . . . . . . . . . 418
Inverses to trigonometric functions. . . . . . . . . . . . . . 425
430
Hyperbolic functions . . . . . . . . . . . . . . . . . . . . .
15. Integrals
431
15.1 Intuitive description of the integral . . . . . . . . . . . . .
15.2 The definition of the integral . . . . . . . . . . . . . . . .
15.2.1 Integration in Maple . . . . . . . . . . . . . . . . . .
15.3 Basic theorems . . . . . . . . . . . . . . . . . . . . . . . .
15.4 Bolzano-Cauchy principle . . . . . . . . . . . . . . . . . .
15.5 Antiderivates and areas . . . . . . . . . . . . . . . . . . .
15.6 Introduction to the fundamental theorem of calculus . . .
15.7 The fundamental theorem of calculus . . . . . . . . . . . .
15.8 Consequences of the fundamental theorem . . . . . . . . .
15.9 Remainder in the Taylor formula . . . . . . . . . . . . . .
15.10 The indefinite integral . . . . . . . . . . . . . . . . . . . .
15.11 Integrals over unbounded intervals . . . . . . . . . . . . .
15.12 Interchange of limit and integration . . . . . . . . . . . . .
15.13 Comments and supplements . . . . . . . . . . . . . . . . .
Appendix A
A.l
xix
Muple Programming
Some Maple programs
A.l.l Introduction . . .
....................
....................
431
438
443
445
450
455
457
458
469
477
481
488
492
498
501
501
501
Introduction to Mathematics with Maple
xx
A .1.2 The conditional statement . . . . . . . . . . . . . . . 502
A.2
A.1.3 The while statement . . . . . . . . . . . . . . . . . . 504
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . .
506
References
511
Index of Maple commands used an this book
513
Index
519
Chapter 1
Introduction
In this chapter we explain the scope and guiding philosophy of the present book, we try to make clear its logical
structure and the role which Maple plays in the book. We
also wish to orientate the readers on the logical structure
which forms the basis of this book.
1.1
Our aims
Mathematics can be compared to a cathedral. We wish to visit a small
part of this cathedral of human ideas of quantities and space. We wish
to learn how mathematics can be built. Mathematics spans a very wide
spectrum, from the simple arithmetic operations a pupil learns in primary
school to the sophisticated and difficult research which only a specialist
can understand after years of long and hard postgraduate study. We place
ourselves somewhere higher up in the lower half of this spectrum. This can
also be roughly described as where University mathematics starts. In natural sciences the criterion of validity of a theory is experiment and practice.
Mathematics is very different. Experiment and practice are insufficient for
establishing mathematical truth. Mathematics is deductive, the only means
of ascertaining the validity of a statement is logic. However, the chain of
logical arguments cannot be extended indefinitely: inevitably there comes a
point where we have to accept some basic propositions without proofs. The
ancient Greeks called these foundation stones axioms, accepted their validity without questioning and developed all their mathematics therefrom. In
modern mathematics we also use axioms but we have a different viewpoint.
The axiomatic method is discussed later in section 2.5. Ideally, teaching
of mathematics would start with the axioms, however, this is hopelessly
1
Introduction to Mathematics with Maple
2
impractical at any level of instruction.
We begin our serious work in Chapter 3. New concepts are introduced
by rigorous definitions and theorems are proved. We believe that in our
exposition, set theoretical language and notation is not only convenient but
desirable. This poses a problem in that set theory should be established
axiomatically and we are in no position to do so here. So we patched it up;
Chapter 2 contains all that the reader needs, but not at a rigorous level.
Hence scissors in the margin1 appear only after Chapter 2. We hope that
at some later stage the reader will fill in this gap but we also give a warning
not to do so now: axiomatic set theory is more difficult than anything we
do here.
We shall aid our computations by a computer program called Maple.
This program is supposed to be user friendly, the name can be thought of
as an acronym for MAthematics and PLEasure, or MAthematical Programming LanguagE. The truth is that Maple was developed at the University
of Waterloo in Canada, and the creators of Maple wanted to give it a name
with a distinctive Canadian flavour. Ever since computers were invented
they have been very powerful instruments for solving problems which required large scale numerical calculations. However, in the the last decades
there were programs invented that can manipulate a variety of symbolic
expressions and operate on them. These programs have various names; we
shall call them computer algebra systems. For instance, such a program
can provide you with a partial quotient and remainder of division of two
polynomials. Maple, if properly asked, will tell you that x6 1 divided by
x2 + x 1 is x4 - x3 x - 1 with remainder 2. It can also give you an
approximation of
+
+
+
1- x 2
1 i-2 2
+
by a polynomial of fourth degree as 1- 2x2 2x4. Maple is one of the computer algebra systems; others include MACSYMA, MuMath, MATLAB,
and Mathematica. Maple is now widely used in engineering, education and
research; clearly its knowledge is useful. This book is not about Maple,
however when we arrive at some point where Maple can be usefully employed we show the reader how to do it. We hope that after readers finish
reading this book it will be easy for them to adjust to another computer
algebra system, if they need to. Some very basic facts about how to use
Maple are explained in the rest of this Chapter. Maple ‘knows’ more mathlThe notation of using scissors in the margin is explained in the preface.
Introduction
3
ematics than we can ever hope of managing, but this is not always an
advantage. It is like having a servant who is better educated than oneself.
It can give us an answer in terms of more advanced mathematics than we
know. When we encounter this we shall show how to overcome it. Clearly,
we cannot use Maple when building our theory; this would be circuitous.
Hence readers should understand that scissors are applied automatically
whenever Maple appears. Maple has proved itself as a very powerful tool;
it helped solved research problems which were intractable by paper and
pencil manipulation. However, computer algebra systems are only human
inventions and can fail. As an example we present a graph of the function
f , where f(x) = z sin7x, as produced by Maple (see Figure 1.1).
+
~
~~
Fig. 1.1 Graph of x
~~
+ sin 72
The graph, because of its irregularity, is clearly wrong. Here it is easy
to spot the error, but on another occasion it can be more insidious. It
is therefore necessary to be careful and critical when looking at output
from a computer algebra system. Results are not necessarily correct simply
4
Introduction to Mathematics with Maple
because they come from the computer. The use of computer in mathematics
does not free anybody from the need of thinking.
1.2
1.2.1
Introducing Maple
What is Maple?
Maple is a powerful mathematical computer program, designed to perform
a wide variety of mathematical calculations and operations. It can do simple calculations, matrix operations, graphing, and even symbolic manipulations, such as finding the derivative or integral of a function. It can also
solve a variety of equations such as finding zeros of a polynomial or to solve
linear as well as some nonlinear systems of equations.
Maple is a mathematical computer program, designed
to perform a variety of mathematical calculations and
operations on symbolic as we11 as numeric entities.
1.2.2
Starting Maple
We presume that the readers can handle basic tasks on a computer, be it
their own PC or a large computer at an educational institution with a Unix
operating system. In particular, we presume that the readers know how
to handle files and directories and do some simple editing of a file. The
differences between Maple use on a P C or on a Unix or Linux machine
are minimal and we shall comment only on the essential ones. Before you
can start Maple you must be sure that Maple is installed on your machine.
At a large educational institution this will be almost automatic, for your
home PC you can obtain Maple from a scientific software supplier or from
Waterloo Maple at
http://www.maplesoft.com/sales/student_iro.html.
Once the P C has has finished booting, you can begin your Maple session.
To start Maple, use the mouse to point at the Maple for Windows icon,
and double click the left-hand mouse button. Alternatively, on a Unix or
Linux machine you give the command maple after you login.2
2There is also the command maple, which will open Maple on a Unix machine without
the graphical interface. We do not recommend to use it because of the limited display.
However it can be useful, for example, if you are connected to a Unix machine through
Introduction
5
When Maple starts, a window appears with a number of menus at the
top (such as File, Edit, Format, and so on). Near the top left-hand side
of the screen is the Maple prompt, which is a > character, followed by a
flashing cursor, which is a I character.
To run Maple, double click the left-hand mouse button
on the Maple for Windows icon.
On a Unix or Linux machine give the command xmaple.
1.2.3
Worksheets in Maple
An important concept in Maple is that of a worksheet. During a Maple
session, the computer screen will display your commands (and comments),
together with Maple output, graphs and other pictures. All of this material
is collectively called a worksheet.
Usually, when you use Maple, you will be wanting to enter a new set of
commands. By default, Maple opens an empty worksheet as soon as you
run the program. Any commands you enter will be executed, thus forming
your worksheet.
When you have finished using Maple, you will be given the option of
saving your worksheet to a file. Sometimes you will not need to do this,
and will not save a copy of the worksheet. However, if you have not finished
your work, or if you wish to use the results at a later date, you may decide
to save the worksheet to a floppy or hard disk on your computer. Save files
from Maple in the usual way; note that the standard filename extension for
a Maple worksheet file is .mw or .mws.
If a worksheet has already been saved during a previous Maple session
then you can load a copy of that worksheet into Maple. That is, rather
than starting a new worksheet, you can reload an existing worksheet, and
modify it as required.
To load an existing worksheet, you first need to know the name of the
worksheet, and where it has been saved (for example, on a floppy disk, or
on the PC network). Next, use the File menu, near the top of the screen,
and seIect the Open menu option. Again this is done in the standard way.
Once the existing worksheet has been loaded, you can modify and execute
commands, add comments, and perform your choice of Maple operations.
Of course, when you have finished you may choose to save the modified
a modem. Most often in such a case the graphics are not available anyhow. If you use
Linux then xmaple can be applied only if Xwindows is being used.
6
Introduction to Mathematics with Maple
worksheet so you can use it again.
A Maple worksheet is the name given to the text and
graphics from a Maple session.
Maple opens a new, empty worksheet by default, but you
can load an existing worksheet using the File menu.
You will be asked if you wish to save your current
worksheet before you exit Maple.
1.2.4 Entering commands into Maple
Whether you are creating a new worksheet, or reloading an existing one,
you will probably want to enter some new commands into your worksheet.
Whenever Maple displays the flashing I cursor, it is waiting for you to
type a command. You can use menu items to get Maple to do the things
you want, but you will also often need to enter your commands as text. This
is done by typing your desired command, and (almost always!) ending the
command with a semicolon (;) or a colon ( :). Press the Enter (or Return)
key to execute the command.
If you end the command with a semicolon, then Maple performs the
command, and displays the result. If you end the command with a colon,
then Maple performs the command, but no result is displayed. The colon
is useful for intermediate calculations, but most of the time you will use a
semicolon. Sometimes you might forget the semicolon or colon, and press
the enter key. Nothing will happen, until you enter a semicolon (on the
next line).
~
~
Most Maple commands end with a semicolon or colon,
followed by the enter key.
Don’t forget!
But if you do forget, type the semicolon or colon
on the next line.
Using arithmetic in Maple is discussed in detail in Section 1.4, but the
following examples should be fairly clear. For example, you might wish to
evaluate the sum 1+3 5. Your Maple session will appear as the following:
+
1+3+5;
7
Introduction
9
~~~
The command entered into Maple is 1+3+5; (note the semicolon!), and
the answer from Maple is 9. Maple displays your command near the lefthand side of the screen, and the result is displayed in the centre.
When using Maple commands, please note the following points:
0
0
0
0
0
0
Maple is case sensitive. Most of the in-built Maple functions are
in lowercase letters. Be careful to choose the correct case for
your commands!
Maple usually ignores spaces in your input commands.
Be careful to spell the commands correctly, and use the required
punctuation.
Be particularly careful to use the correct brackets, in the correct
places.
If you make any mistakes while typing your command, use the
backspace key to correct the error. You can also highlight something and remove it by using Cut from the Edit menu.
Sometimes, a command you type may take a long time to execute. You can interrupt execution (and return to the Maple
prompt) by typing Control-C, sometim.eswritten C t r l - C , (that
is, you press the c o n t r o l key, at the same time as pressing the
C key).
Note that you can enter multiple commands on the same line, provided
you use a semicolon or colon at the end of each command. For example, to
evaluate 1 3 5, 21° and 64 x 123, enter the following commands. Note
the use of * for multiplication and for exponentiation.
+ +
>
1+3+5; 2-10; 64*123;
9
1024
7872
Introduction to Mathematics with Maple
8
If you forgot the semicolon and hit the enter key, you can complete the
command on the next line by typing semicolon and the enter key there.
64
1.2.5
Stopping Maple
When you have finished, you can quit Maple in a number of ways. The
easiest is to select the Exit command in the File menu.
I
1.2.6
To exit Maple, select Exit from the File menu!
I
Using previous results
Often, you will want to use the results of immediately previous calculations
in subsequent calculations. The percentage character % refers to the previous result, two %% refers to the second-last result, and %%% refers to the third
last result. In older versions of Maple the double quote character 'I is used
instead of %.To find out which of the % or It to use on your machine type
?ditto at the Maple prompt. To evaluate 24, then 24 x 5 , then 24 x 5 + 2 4 :
16
80
96
Introduction
9
Type %, %% and %%% to use the results of
the three most recent calculations.
Sometimes, you will need to use many previous results, not just the
last three. This can be done via the history function. You can use the
help command to learn more about the history command. Using help is
discussed in detail in Section 1.3, but typing ?history will give you the
required informat ion.
1.2.7
Summary
In summary:
0
0
0
double click on the Maple for Windows icon or give the command
xmaple
type your commands, ended with a semicolon or colon
make sure the syntax, spelling and punctuation are correct
exit using Exit from the File menu
1.3 Help and error messages with Maple
1.3.1
Help
Maple contains a great deal of on-line help. This can be accessed via the
help command, usually entered as the question mark ? character.
Use the ? command for helpful information.
Use ?name for help on the command name.
You can use the ? command in a number of ways. The following
table summarises the more common uses (some of which will only become
important after you have used Maple more extensively).
Introduction to Mathematics with Maple
10
Command
~~
?
?name
?index
?library
?datatypes
?expressions
?st at ement s
Help Topic
General help
Help on the topic name, or list
all topics which begin with name
An index of available help topics
Standard library functions
Basic data types
Maple expressions
Maple statements
Other useful help information can be accessed via the commands info,
usage, related. These commands all give more information about a specified function. A very convenient way of using help is to open the Help
menu by clicking on Help located in the upper right corner of the screen.
If you know exactly the name you are looking for choose Topic Search
otherwise use Full Text Search from the Help menu.
1.3.2
Error messages
When Maple encounters an error, it usually displays an error message.
These error messages are intended to be helpful: please read them, and
think about what is being said.
The following examples show some error messages from Maple. In each
case, the message is reasonably simple to understand. In the first case, we
have tried to divide by 0, which is (of course) impossible. In the second
case, the expression which has been entered is not valid: we have missed
out a number or a variable.
>
1/0;
Error, numeric exception: division by zero.
>
3*4+;
Error,
’;’
unexpected.
Note that in this case, the Maple input cursor will be positioned in the
command you entered, at the place where Maple thinks the error occurred.
In the next two examples we failed to type * to indicate multiplication.
The results are surprising.
Introduction
>
11
(3+2)4;
Error, unexpected number
>
4(3+2);
4
In the first instance Maple gave the correct error message. However, in
the second example Maple gave a wrong answer and did not issue an error
message. The moral of this is simple, always try to enter your command
precisely and carefully. A wrong input can return a wrong result without
any warning.
Pay attention to the error messages!
Maple tries to make them meaningful, often with a
pointer to the location of the problem.
1.4
1.4.1
Arithmetic in Maple
Basic mathematical operators
As many of the previous examples have shown, Maple can be used for arithmetical calculations, and supports all of the usual mathematical operations.
Furthermore, Maple follows the usual rules regarding order of calculations.
For example, multiplication is evaluated before addition, and so on. The
following table summarises many of the common arithmetical operations.
Mat hematical
Operation
Description
Addition
Subtraction
Multiplication
Division
Exponentiation
Factorial
Square root
Mathematical
example
1+3+5
9-5-3
2 x 5
8/2
25
5!
J25
usage
1+3+5
9-5-3
2*5
8/2
2-5
5!
sqrt (25)
Result
9
1
10
4
32
120
5
Introduction to Mathematics with Maple
12
1.4.2 Special mathematical constants
Maple also knows a number of useful mathematical constants, which can
be used in your calculations. Two of the most important ones are shown
in the following table. Note the capital letter in P i .
I
I Mathematical I
name
7i-
1
e
I
Description
Area of the unit circle
Exponential constant
Maple
usage
1 Approximate 1
Pi
I exp(1) I
value
3.141592
2.7182818
I
~~
Maple supports all of the common mathematical
operations, and special mathematical constants.
1.4.3 Performing calculations
When performing calculations, you must be sure to:
0
0
0
0
0
include suitable brackets. For example, when using sqrt, whatever you want to take the square root of must be enclosed in
round brackets.
match up your opening and closing brackets. For example,
sqrt (4)+5 is not the same as sqrt (4+5).
always use an asterisk character * for multiplication. To evaluate
2x&, you must enter 2*x*sqrt (4*x).
ensure that you have entered your expressions with appropriate
precedence. For example, 4*3+5 is different to 4* (3+5). When
in doubt, use brackets.
enter divisions correctly, using a / character. To evaluate
-I-6 , you would enter (2*5+6) /(3*2).
3x2
Always put the multiplication sign in!
Always use brackets for function arguments!
Be careful with precedence rules in calculations!
Ensure that your brackets match up,
and are correctly located!
Consider the following Maple session. Note that if a # character appears
anywhere in a line, then the rest of the line is regarded as a comment by
Introduction
13
Maple, and is ignored. You should use comments to make your calculations
more readable.
>
>
>
>
# F o r g e t t i n g b r a c k e t s around f u n c t i o n arguments
# g i v e s an e r r o r
# Try t o f i n d t h e square r o o t of 4
s q r t 4;
Error, unexpected number.
>
>
# The argument of t h e square r o o t f u n c t i o n
>
# Evaluate t h e square r o o t of 4
>
sqrt(4);
# needs t o be placed i n parentheses
2
>
>
# Evaluate ( t h e square r o o t of 4)+5;
sqrt(4)+5;
7
>
# Evaluate t h e square r o o t of (4+5)
>
sqrt(4+5);
3
>
>
# Evaluate (2*5+6) divided by (3*2)
(2*5+6) / (3*2) ;
1.4.4 Exact versus floating point numbers
The Muple session given in the previous section shows that Maple evaluates
8
(2 * 6 6)/(3 * 2) to -, rather than to 2.66667. This is important. Muple
3
will (within the limitations of the current version of the software) give you
the exact result rather than an approximation. The number of digits may
be large but Muple will produce all the digits.
+
14
Introduction t o Mathematics with Maple
>
(9^9)^9;
1966270504755529136180759085269121
1628310345094421476692731541553\
7966391196809
Note the backslash at the end of the line telling you that the number
continues on the next line.
If you want an approximation you must ask for it. One way to do this
is to type the numbers in your command with decimal points. Any number
which contains a decimal point is called in Maple a floating point number,
FP number for short. The name refers to the way in which these numbers
are stored and handled by Maple.3 For sake of brevity we shall refer to
numbers without decimal points as exact numbers. For example, integers
and fractions are exact numbers. So are fi and T . If only exact numbers
enter a computation, Maple will produce an exact result. It is important
1
to realize that for Maple, - and .1 are distinct entities, not because of
10
their appearance, but because of the way they are treated by Maple. If
you enter .1 into Maple you give tacitly some instructions which are absent
1
when entering -. Here is our first example with FP numbers.
10
>
I . ~ + s q r(3.0)
t
;
2.732050808
It is sufficient to write
>
l + s q r t (3.0) ;
2.732050808
~~
~
3Neither here nor anywhere else do we attempt t o explain the internal workings of
Maple.
Introduction
15
However
1.0
+&
produces a correct result but not the one you wanted.
You can control the precision of the decimal output via the D i g i t s
command (note: capital D!), which allows you to view and set the number
of significant figures:
>
Digits;
10
>
s q r t (132.);
11.48912529
>
Digits:=20; s q r t ( 1 3 2 . ) ;
Digits
:= 20
11.489125293076057320
Use D i g i t s to custamise the number of significant figures
given in the answer to a calculation.
The next two examples show why exact arithmetic is preferable. In the
first example we set digits to three. This is not essential; a similar thing
can happen with any number of digits.
>
Digits:=3: 1/(22-7*sqrt (2)-7*sqrt (3)) ;
1
22 - 7 f i - 7 fi
Introduction to Mathematics with Maple
16
>
1/(22-7*sqrt (2.0)-7*sqrt (3.0)) ;
Error,'divisionby zero
We did not ask for division by zero, however, within the precision of
three decimals the denominator is approximately equal to zero and this
causes Maple to issue an error message. If the above number appeared
somewhere in a long calculation the process would stop. This would not
happen with exact arithmetic. Increasing the number of digits helps, but
the result using Maple's default precision of ten digits is correct only to
seven digits.
>
Digits :4 0 :1/ (22-7*sqrt (2.0)-7*sqrt (3.0)) ;
-41.92768397
-41.92768468330373949420182744528363166911107461864767765
In the next example one would expect that after taking the root and exponentiation the original number would be returned: however, calculation
with floating point numbers is not exact!
>
27.0'(1/8);
1.509803648
26.99999993
The imperfection of the last two examples is not some fault in Maple's
design, it is instead inherent in the nature of approximate calculations. We
would encounter the same phenomena if we performed the calculations with
paper and pencil.
If all of the entries in a calculation are exact, you can still obtain a
decimal point approximation, using the evalf 0 function. Evalf stands
Introduction
17
for EVALuate with Floating point arithmetic. The first argument to the
evalf () function must be the calculation you want to be evaluated. Optionally, a second argument tells Maple how many significant digits are
required.
>
evalf ( s q r t (132) ;
11.48912529
>
>
# Evaluate t h e square r o o t with 30 s i g n i f i c a n t d i g i t s .
evalf ( s q r t (132) ,301 ;
11.4891252930760573197012229364
>
sqrt(l32);
>
>
>
# Recall t h a t
% g i v e s t h e r e s u l t of
# t h e most r e c e n t l y e n t e r e d computation.
evalf(%);
11.48912529
You can use the c o n v e r t 0 function to convert a FP number to an
exact fraction. More generally, you can use convert () to convert numbers
or expressions to many different formats (such as binary, hexadecimal, and
so on). Type ?convert; for more information on this useful command.
>
# Give P i t o 20 s i g n i f i c a n t d i g i t s ,
>
>
# and w r i t e t h e r e s u l t as an exact f r a c t i o n .
convert (evalf(Pi,2O) , f r a c t i o n ) ;
103993
33102
Use evalf () t o produce a floating point result.
Use c o n v e r t 0 t o convert from one format t o another.
Introduction to Mathematics with Maple
18
Algebra in Maple
1.5
In the previous examples, Maple has largely been used as a calculator: an
expression is entered, and Maple calculates an answer. However, Maple is
much more powerful than this. You can use any letters or combinations of
letters (except for system constants such as P i ) as variables, and you can
then manipulate algebraic expressions in many ways. For example, you can
find sums and products, expand and factorise expressions, and so on.
1.5.1
Assigning variables and giving names
The assignment operator := is used to assign values to variables. Then
whenever you use that variable, Maple will replace it with the expression or
value which has been assigned to the variable. This allows interim results
to be calculated, and then used in subsequent calculations.
~~~
>
>
>
# Assign the values 5-2 and (3'3)/4
# to the variables a1 and a2
a1 :=5^2; a2 :=3^3/4;
a1 : = 2 5
27
4
a2 := -
>
>
# Look at the values of a1 and a2
al; a2;
25
27
4
-
>
>
# Use the values of a1 and a2
al*a2;
675
4
Introduction
19
Here are two more examples with assigning algebraic expressions rather
than numbers.
>
u:=2*x+4;
u:=2~+4
3x+6
~
>
v:=x-2-1;
v : = x 2 -1
>
z:=x-I;
z:=x-l
x2 - 1
When a value is assigned to a variable, this value remains assigned until
it is explicitly cleared. You must remember this: if you have assigned a value
to a variable, each time you use that variable in a subsequent expression,
the assigned value will be substituted into the expression. To clear the
values of variables, you can use the restart command, which completely
clears all variables (having the same result as if you exit Maple, then started
it again). To clear the values of individual variables, you assign the variable
its own name, with the right-hand side enclosed in single quotes. It is a good
habit to begin a second worksheet with the restart command, otherwise
the variables from the previous worksheet would be still assigned.
~
>
>
~
~
~
~~
~~~
Assign a value t o the variable a2
a2:=27/4;
#
~~
~
Introduction to Mathematics with Maple
20
27
a2 := -
4
>
>
>
# Clear t h e variable a2,
# f o r g e t t i n g its current value.
a2:=’a2’;
a2 := a2
The assignment operator can be used to name almost any object which
Maple recognizes. This can save a lot of typing by recalling the object by
its assigned name either alone or as an argument of a command.
>
p :=Pin3+5! ;
p := T 3
>
eq: =xA2+5=p;
eq := x2
>
+ 120
+ 5 = r3+ 120
p01y:=xn5+x+1;
poly := x5
>
+x + 1
f a c t o r (poly) ;
2.(
+ + 1)(2
- x2
+ 1)
Use := to assign values to variables.
Use r e s t a r t to clear the values of all variables.
Clear an individual variable by assigning the variable’s
name to it, for example, x I : = ’ x l ’ ; .
Use the assignment operator := to name mathematical
objects.
1.5.2
Useful inbuilt functions
Maple provides you with a large number of very useful inbuilt functions.
These can be used to manipulate expressions which you have defined. There
is a command simplify. However, simplification can mean one thing on one
occasion and something altogether different on another. Which is simpler,
Introduction
+
21
+
x4 - 1 or (x 1)(x - 1)(x2 l)? Maple simplifies only when there cannot
be any doubt: x is undoubtedly simpler than II: 0. Therefore the decision
how to manipulate is often left with the user: Maple provides a range of
options: expand, factor, normal, collect, sort and combine.
+
1.5.2.1 expand ()
The expando function is used to distribute products over sums:
>
>
Define A=x^2-1 and B=x^2+1
A:=x^2-1; B:=x^2+1;
#
A := x2 - 1
B := x2 + 1
>
>
## Evaluate A*B
A*B;
( x2 - 1 )( x2
>
>
+ 1)
Expand the previous result
expand(%);
#
x4 - 1
>
>
Expand (x-2-1) (~^2+1)-(~-1)^2
expand((x"2-1)*(x'2+1)-(x-l~^2);
#
x4 - 2 - x2 + 2 2
> # E x p a d (x+Y+z)-2- (x-y-Z) -2
> expand((x+y+z)^2-(x-y-z)^2);
4xy+4xz
>
>
Expand (1+2x)^6
expand((l+2*x)^6) ;
#
22
Introduction to Mathematics with Maple
1.5.2.2 factor()
The factor0 function is used to factor a polynomial with integer, rational or algebraic number coefficients. factor0 is often used to reduce
complicated expressions to a more manageable size.
>
>
# Assign xn2-1 to
f, in other words name xn2-1 as f
f:=x^2-1;
f :z2-l
>
>
# Factorise f
factor(f);
(2-l)(z+l)
>
>
# Factorise xn4-xn2+2x-2
factor(xn4-xn2+2*x-2) ;
( z - 1 ) (z3
+z2
+2)
1.5.2.3 simplify()
The simplify() function is used to write a complicated expression in a
simpler form. Often Maple leaves the expression unchanged and leaves the
decision how to proceed with the user.
>
>
# Assign (sin x)^2 + (cos x1-2 to T
T:=sin(x)^2+cos (x)^2;
T
>
simplify(T) ;
:= sin( z ) 2
+ cos( x
) ~
Introduction
23
1
Note that the Maple command sin(x)-2 is evaluated as (sin(x))^2 and
not as sin( (x) -2). Here Maple follows the standard rules of precedence
in evaluating functions before applying other mathematical operations. If
you are in any doubt, insert additional parentheses to avoid possible errors.
>
>
#
>
>
#
>
c-CI;
>
>
# Simplify the previous result
Assign (x+1)^3 to C
C:=(x+1)^3;
Assign (x-1)^3 to Cl
C1:=(~-1)^3;
simplify(%) ;
6x2+2
1.5.2.4 normal()
The normal (1 function is used to write expressions in normalised form;
that is, a numerator over a common denominator:
22-1
x(2-1)
Introduction t o Mathematics with Maple
24
> # Normalise l/(x-I) + x/(x-1)^2
> normal(i/(x-I) + x/(x-I)^2) ;
22-1
1.5.2.5 collect ()
The collect() function is used to collect coefficients of like powers of a
specified variable; this variable must be indicated, otherwise Maple does
not know what to collect. In the following example, the final expression
has been collected into coefficients of the variable x:
>
>
# Define f as (x+(x-y)^2)^2
f :=(x+(x-y)-2)-2;
f
>
:= (x
- y)2)2
expand(f);
x2 + 2 x3 - 4 x2 y + 2 x y2
>
+(x
+ x4 - 4 x3 y + 6 x2y2 - 4 x y3 + y4
collect (%,XI ;
x4 + ( 2 - 4 y ) x3 + ( -4 y
+ 1 + 6 y2 ) x2 + ( 2 y2 - 4Y3 ) X
Y4
1.5.2.6 sort()
The sort (1 function is used to sort the terms of an expression into descending order of power of a specified variable.
>
>
# Assign xA5-3x^3+x^7 to f
f:=x-5-3*x-3+xn7;
f :=x5-3x3+x7
>
sort(f,x);
25
Introduction
x7+x5-3x3
>
>
x^3y+x^4y^4+~^2+~^6y^2
to g
g:=xn3*y+x*4*y^4+xn2+xn6*yA2;
# Assign
g := x3 y
>
+ x4 y4 + x2 + x6
y2
sort(g,y);
x4 y4
+ x6 y2 + x3 y + x2
1.5.2.7 combine()
The combine command reduces a given expression into a single term, if
possible, or transforms it into a more compact form. Sometimes, similarly
as with simplify, it leaves the original expression unchanged, leaving the
user with the option to try another command. The following examples
illustrate the use of combine.
>
combine(sqrt (12) *sqrt (3)) ;
6
>
combine(sqrt(3+sqrt(5))*sqrt(3-~qrt(S)));
>
expand(%);
2
cos(2 x)
The combine command reduces integer powers of sin and cos into linear
expressions in terms of sin and cos of multiple arguments. The result is
often more extensive than the original expression.
Introduction t o Mathematics with Maple
26
1
5
- COS(32)
16
16
combine(cos(x)n2+sin(x)n3) ;
- C O S ( ~2)
>
1
2
- cos(2 z)
+
+ -85
COS(Z)
+ -21 - -41 sin(3 z) + -43 sin(z)
1.5.2.8 subs()
The subs command makes a substitution, it can be used to substitute a
value or an expression. It does not simplify the result, it leaves it to the
user to choose an appropriate command for further processing.
>
subs(x=l,sqrt(xn2+ll*x+24)) ;
J36
>
simplify(%) ;
6
>
~~b~(~=t-3,~^2+6*~+1);
(t - 3)2 + 6 t - 17
>
expand(%);
t2 - 8
combine(), expand(), factor(), simplify(),
normal 0,collect (>, sort () and subs ()
are all useful for manipulating mat hematical expressions.
Examples of the use of Maple
1.6
Example 1.1 (simplify; rationalize; factor)
some quite complicated expressions
>
simplify(sqrt (2*sqrt (221)+3O)) ;
a+Im
Maple can simplify
Introduction
27
On some similar occasions Muple does not simplify, the reason being that
there is another command more appropriate: for example, rationalize is one
such command.
1
2 + f i
>
rationalize (%I;
2-fi
~~
Another example of the same kind is:
>
>
simplify(2*a-2+5*a*b+2*bn2);
2 a2
+ 5 a b + 2 b2
(2a
+ b) (a + 2b)
factor(%);
Example 1.2 (solve) Maple can solve equations for you. The basic command is solve. We shall learn more about solving equations and the command
solve in Chapter 8, but here is a simple example.
1
eq:=-+x+l
>
1
=1
2-1
solve(eq);
La,1 + J z
We trust that the readers do not need Muple to solve such simple equations,
the next example is more sophisticated. We use Maple to solve a Diophantine
equation, this means that the solution must be in integers.
Example 1.3 (Diophantine equation)
An example of a Diophantine
equation is
1 2 3 4 5 6 7 ~- 7 6 5 4 3 2 1 ~= 1357924680.
The command for solving a Diophantine equation is isolve.
Introduction t o Mathematics with Maple
28
>
isolve(l234567*x-765432l*y=l357924680);
( X = 4484627
+ 7654321-Nl , y = 723149 + 1234567-Nl }
The entry -Nl indicates an arbitrary integer, there are infinitely many solutions. The amount of work with pencil and paper t o find this solution would
be considerable. Maple’s answer is instantane~us.~
It is easy t o see that the
Diophantine equation 3 x - 2 y = nl where n is a given integer, has a solution
(among infinitely many others) x = n, y = n. Maple’s answer is not very
he1pfuI:
>
isolve (3*x-2*y=n);
( x = - N l , n = 3 _ N l -2-N2, y = - N 2 )
This means that if we choose x arbitrarily and y also then
- 2 y , which is correct but not helpful. What can we do with
12345673: 7654321y = n? As we might expect, blind use of isolve will
n = 3x
+
not help.
>
isolve (1234567*~-765432l*y=n)
;
(x = -Nl, y = -N2, n = 1234567-Nl
-
7654321-N2)
However we can solve the equation
>
isolve (1234567*~-765432l*v=l)
;
(U = 661375
and then clearly
x
+ 1234567-Nl , u = 4100528 + 7654321-Nl }
= un and y = u n
*Maple will even tell you how long it took t o solve an equation or to execute another
command. See time in the Help file.
Introduction
29
We have seen that Maple can save a lot of tedious work. However, even
in a situation when Maple is unable to solve the problem directly, it can be
used with advantage.
At the beginning of this chapter we said that Maple can divide poly1 by x2 x 1. There are two
nomials: we mentioned dividing x6
commands, rem to give the remainder and quo to give the quotient. The
following Maple session shows how to use these commands. Note that three
entries in brackets are required in both commands.
+
>
+ +
rem(x^6+1 ,xn2+x+1,x) ;
2
>
quo(xn6+l,xn2+x+l,x);
x4 - x3 -l-x - 1
The third argument in both commands is important as the next example
shows.
>
rern(u^2+~^2,~-2*v,u)
;
5 v2
>
rem(u^2+vA2,u-2*v,v) ;
5
4
- u2
Maple can also find the remainder and quotient for division of integers.
The commands are irem and iquo.
>
irem(987654,13) ;
5
>
iquo(987654,13) ;
75973
Introduction to Mathematics with Maple
30
So far all the commands we have used were available directly on the command line. These commands reside in the main part of Maple called the
kernel. More specialized or not very frequently used commands are grouped
in packages. This helps with efficiency and speed of Maple. The list of packages can be obtained by first clicking on Help (located in the upper right
corner of the screen) and choosing Topic Search from the menu which
opens. In the dialog window which then appears fill in packages and click
OK. Another window opens and there you can click on index,package.
There are literally dozens of packages. In this section we shall use only
two, the number theory package and the finance package. To obtain the
list of commands available in a package issue a command with(package) .
For instance, if we issue a command with(geometry1 Maple will produce
a long list of various commands relating to geometry, and make these commands available. The first command in the list is Apollonius. Using Help
will tell us how to use this command to solve the problem of finding a circle
which is tangent to three given circles, a problem formulated and solved by
Apollonius from Perge in third century BC. In a similar fashion we can employ any command from the above list after the command with(geometry)
has been issued. If we know the name of the command and the package we
can use the command on the command line. For instance, we can find all
positive divisors of a number using the number theory package numtheory
and the command numtheory [divisors], or the number of positive divisors using the command numtheoryCtau1 (note the use of [ and I).
>
numtheory[divisors] (144) ;
(1, 2, 3, 4, 6, 8, 9, 12, 16, 18, 24, 36, 48, 72, 144)
>
numtheory [tau] ( 144);
15
~
Alternatively, we can activate the command
>
with(numtheory, divisors) ;
[divisors]
and then use the short form of the command thus
>
divisors (144) ;
Introduction
31
(1, 2, 3, 4, 6, 8, 9, 12, 16, 18, 24, 36, 48, 72, 144)
Mathematics for finance is contained in the finance package.
Example 1.4 (Mortgage) A 35 year-old man is confident t h a t he can
save $12,000 a year. He wishes to buy a house and stop payments a t 55, when
he plans to retire. The current interest rate is 6%. In order to find out how
much he can borrow he uses the command annuity from the finance package.
From a mathematical point of view, mortgage and annuity are the same. A
company advances the money for the mortgage and the consumer pays back
installments; in an annuity, in contrast, the individual makes a down payment
and receives regular income from a finance institution.
>
w i t h ( f inance,annuity) ;
[annuity]
>
a~nuity(12000,0.06,20);
137639.0546
This amount seems insufficient for a house of the desired standard and
quality. The man decides to extend the life of the mortgage to see what
happens.
~
~~
>
~~
~
~-
annuity(12000,0.06,30);
165177.9738
This is a sufficient amount. However, paying the mortgage for ten more
years is not an attractive idea. To see by how much the installments must be
increased for a mortgage of $165,000, use the following commands.
>
solve(annuity(x, .06,20)=165000);
14385.45190
Clearly, as long as it is affordable, it is far better to increase the cash
payments rather than to increase the life of the mortgage.
The Help file contains details of how to adapt the annuity for a more realistic
situation of monthly or fortnightly payments.
Introduction to Mathematics with Maple
32
Exercises
@ Exercise 1.6.1
The command time(X) will tell you how much time
(in seconds) was needed by Maple to execute X. Assign a, b, c to be
2100750,3100550,and 101ootrespectively. Solve the Diophantine equation
ax by = c. Determine how much time was needed. [Hint: On the authors' machine using Maple 7 it was .039 sec].
+
+ x4 + 1.
Exercise 1.6.2
Use Maple to factorize xl'
Exercise 1.6.3
Find the quotient and remainder for
(1) 987654321, 123456789;
(2) 100000, 17.
Exercise 1.6.4
(1)
(2)
Find the quotient and the remainder for the polynomials
+ 52 + 7) x4 + 1;
X2O + 1, x9 + 2x + 7.
X1O
Exercise 1.6.5
What are the monthly installments for a mortgage of
$200,000 over a period of 40 years a t interest rate of 6.5%?
Chapter 2
Sets
In this chapter we review set theoretic terminology and notation.
Later we discuss mathematical reasoning.
2.1
Sets
The word set as used in mathematics means a collection of objects. It is
customary to denote sets by capital letters like M , M I , etc., below. At this
stage the concept of a set is best illuminated by examples, so we list a few
sets and name them for ease of reference.
Table 2.1
Some examples of sets
M I , the set of all rational numbers greater than 1;
Mz, the set of all positive even integers;
M3, the set of all buildings on the St Lucia campus of
the
the
M5, the
M6, the
M7, the
M4,
University of Queensland;
set of all readers of this book;
set of all persons who praise this book;
set of all persons who condemn this book;
set of all even numbers between 1 and 5.
In these examples we have used the word ‘all’ to make it doubly clear that,
for instance, every rational number greater than 1 belongs to M I . But
usually, in mathematics, if someone mentions the set of rational numbers
greater than 1 he or she means the set of all such numbers, and this is
33
34
Introduction to Mathematics with Maple
automatically understood. Thus with a similar understanding we would
say that M2 is the set of positive even integers.
If an object x belongs to the set M , we say that x is an element of M
or that x lies in M , and we denote this by writing x E M . If z is not an
element of M we write z @ M . A set M is defined by specifying which
elements lie in M .
One way of describing a set is to list its elements, separated by commas,
and enclose them in braces (curly brackets); for example, M7 = {2,4},
M2 ={2,4,6,8 ,... }.
It is worth pointing out that the elements in a set can be other sets.
Thus the set A = (2, {x,y}} has two elements; one is the number 2,
and the other is a set with two elements, x and y. These could be some
mathematical entities, or they could be sets, whose elements are other sets,
whose elements . .. . Furthermore, the order in which the elements are
listed inside {. . . } is irrelevant; thus M7 = {4,2} is the same set as Ad7
defined in Table 2.1. Also, any repetition of elements is ignored, so that
we could equally well write M7 = {4,4,2,2,2,4,2,4}. The essential part of
the definition of M7 is that both 2 and 4 must lie inside {. . .}, and nothing
else is allowed there.
A finite set has only a finite number of elements. Thus M3 and M4
are finite, whereas M I and M2 are infinite (that is, they are not finite).
Two sets M and N are considered to be equal if they consist of the same
elements. If all elements of a set A belong to a set B then we say that A is a
part of B , or that A is a subset of B. This is denoted by A c B. Obviously
A c A always. The relation A c B is often referred to as an inclusion.
Thus in the above list it is obvious that Ad2 c M I . If all people were honest
and rational it would be also true that M5 C M4 and M6 C M4; but in the
world as it is this is not certain. If A c B and A is not equal to B we say
that A is a proper subset of B. This relation between A and B is denoted
by A 2 B.
It is also convenient to introduce the empty set. This is a set which
has no elements, and we regard it as a finite set. The symbol 8 is used to
denote the empty set. We could also write 8 = {}. At the first encounter
the empty set may look a little strange but the introduction of the empty
set has advantages; for example, in the above list M4, M5 and M6 need not
contain any elements at all.' The introduction of the empty set allows us
to talk freely about M4, Ms and M6 without worrying whether or not they
lBut since you have read this footnote i t seems that M4 contains at least one element.
Sets
35
contain an element. We also agree that @ c M for any set M .
If M c N and N c M then clearly M and N consist of the same
elements and hence M = N . In particular cases later, where we prove that
M = N we often do so by showing that M c N and N c M .
We now introduce some further notation. If P is a certain property then the set of all elements having the property P is denoted by
{x; x has property P } ; for example,
M2 = {x; x is a positive even integer},
M7 = {x; x E M 2 , 1 < x < 5).
2.1.1
Union, intersection and diflerence of sets
Given two sets M and N we can define a new set, denoted by M U N ,
consisting of all elements belonging to either M or N (or both). The
set M U N is called the union of M and N (see Figure 2.l), and clearly
M U N =N UM.
MuN
Fig. 2.1 Union of two sets
We define another new set by taking elements common to both M and N ;
these form a set called the intersection of M and N which is denoted by
M n N (see Figure 2.2). Clearly M n N = N n M . Using the notation
36
Introduction t o Mathematics with Maple
introduced above we have M U N = {x; x E M or x E N } and M n N =
{x; x E M and x E N } . If M n N = 8 then we say that M and N are
disjoint.
Fig. 2.2 Intersection of two sets
Yet another set which can be formed from two sets is the difference. If
M and N are sets then the difference M \ N is, by definition, the set of
elements lying in M but not in N , that is, M \ N = {x; x E M , x 6 N } .
This is illustrated in Figure 2.3. It is clear that M \ N # N \ M , unless
M = N , in which case M \ N = 8.
Fig. 2.3 Difference of two sets
37
Sets
Given three sets L , M and N we can form two new sets A = ( L u M ) u N and
B = L u ( M U N ) . For example, for B we form first the union M U N = Q
and then use Q to form L U Q. It is easy to see that each of the new sets
A,B consists of those elements belonging to at least one of the sets L , M ,
N . Hence ( L U M ) U N = L U ( M U N ) and we write simply L U M U N .
The same convention is used for intersections; for example, the intersection
of four sets A,B,C , D may be denoted simply by A n B n C n D.
The corresponding ideas for 5 sets, or 25, or any finite number, presents
no new difficulties but some compact notation is needed. Thus A1 n A2 n
nAi,
4
A3
nA4 is denoted by
seven sets
B1,
B2,
i=l
. . . , B7. The
u
u
7
and in the same way,
Bi is the union of the
i=l
notation is extended to refer to an infinite
00
number of sets so that
Ci is the set of those elements contained in at
i=l
00
least one of the sets C1
C2?C,,
(7 Dk is the set of those elements
. , . and
k=l
i
contained in all the sets Dk,k = 1, 2, 3,
. . . . For example, if Ci is the set
u
00
of positive proper fractions with denominator i then the union
Ci is the
i=l
set of rational numbers between 0 and 1. And if Dk is the set of integers less
00
than or equal to I% then the intersection
0Dk consists of the set of negative
k=l
nDk
00
integers, together with 0 and 1, that is,
= (1, 0,-1, -2, -3,
. . .}.
k= 1
Exercises
Exercise 2.1.1
Use the definitions
AUB={x; xEAorxEB},
AnB={x; xEAandxEB},
A\B = { x ; x E A, x
4 B},
to prove ( A nB ) U ( A \ B ) = A .
The set of subsets of A is called the power set of A .
For example, the power set of (1, 2 ) is {{}, {I}, {2}, (1, 2)}. Use Maple
to create the power set of (1, 2 , 3, a, b, M } . [Hint: Use Help to find the
command combinat [powerset J .]
@ Exercise 2.1.2
Introduction to Mathematics with Maple
38
2.1.2
Sets in Maple
Notation for sets in Maple is the same as in set theory. The empty set
is denoted by {I (the common mathematical symbol for the empty set, 8,
cannot be entered from the keyboard). Instead of the symbols U, n, \ we
write union, intersect, minus, respectively. Hence
A
>
B:={23,29,31,37,41,43,47,53,59,61,67,71};
B
>
:= (19, 25, 31, 37, 43, 49, 55)
:= (23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71)
A union B;
(19, 23, 25, 29, 31, 37, 41, 43, 47, 49, 53, 55, 59, 61, 67, 71)
>
A intersect
B;
{31,37, 43)
>
A minus
B;
{ 19, 25,49, 55)
> B minus A ;
(23, 29, 41, 47, 53, 59, 61, 67, 71)
>
({a,b} union {x});
({1,2,x} minus {I});
Sets
39
In ordinary life there is no distinction between 1 and 1.0. We know that
Maple makes a distinction, consequently
>
({I}intersect
{I-o});
({I} union (1.0));
0
2.1.2.1
Expression sequences, lasts
An expression is a basic entity in Maple; roughly speaking, it is a string of
characters which can be entered into Maple. An expression sequence is an
ordered n-tuple of expressions separated by commas. For instance j o h ,
I, Kai !Hy%, set, x+l/x, 1, 1 is an expression sequence. An expression
sequence enclosed in square brackets constitutes a list. Order and repetition
of expressions in lists or expression sequences do matter: [I, 21 , [2, 13
and [I, 2, 11 are all distinct. Sets, lists and expression sequences are all
similar: the difference lies in the way Maple handles them. You can make
an intersection of two sets but not of two lists. You can combine two lists
into a new list in which the first element of the second list follows the last
element of the first list. The following are some examples of using sets, lists
and expression sequences in Maple.
>
# First we name a few sets, lists and
>
>
>
# expression sequences.
A:={a,b,c}; B:={john,3! ,x!,Kathy};
C := [x,y ,z , z ,z] ;Z :=I, a, I,b;
A
:= { a , b, c}
Introduction to Mathematics with Maple
40
B
:= (6,x!,john,
Kathy}
c := [x,y, 2 , 2 , 21
Z := 1, a , 1, b
>
>
#
>
convert(A,list) ;convert(C,set);
>
>
>
#
>
>
# the nops command counts the number of elements
>
>
# the command convert changes a set into a list
or vice-versa
enclosing an expression sequence in appropriate
symbols converts the sequence to a list or set,
# respectively.
#
in a list or a set,
but cannot be applied to an expression sequence.
nops (B) ;nops (C) ;
#
#
4
5
Sets
#
the n-th element can be extracted from a
# list or an expression sequence by
# attaching [n] to the list
or expression sequence, respectively.
Note that a set does not have an n-th element.
# Maple will return a result, giving what it thinks
# is the n-th element, and this need not agree
# with what you might guess is the n-th element.
AC21 ;B[11 ;Z[31;
##
#
b
6
1
The command op(2,A) has the same effect as A[2].
# However, the op command is more powerful
# and more flexible.
# It can extract all elements, or some chosen
## elements from a list or a set.
## Some examples on the use of op follow.
op(2. .4,C); op(B); [op(A), op(3. .4,B)1;
#
6 , x!,john, Kathy
[a, b, c, john, Kathy]
>
>
>
The command seq can be used to generate
# an expression sequence according to some formula.
seq(i-3 ,i=2..5) ;seq(nA2+n+7 ,n=O..3) ;
#
8, 27, 64, 125
41
Introduction to Mathematics with Maple
42
7, 9, 13, 19
>
{seq(nn2+n+7,n=l. .30)}intersect{seq(nn2+n+ll,n=1. -30));
(13)
Exercises
Combine the lists L = [l,3,5] and M = [I,2,3,4] into
Exercise 2.1.3
a single list using the op command.
Exercise 2.1.4
Find the number of distinct elements in a list by first
converting it to a set.
Exercise 2.1.5
Find the number of elements in the power set of A,
where A has 17 elements. [Hint: See Exercise 2.1.2. Use nops!]
2.1.3
Families of sets
Instead of saying that A is a set of sets we prefer to say that A is a system or
a family of sets. Later we shall encounter situations where to each element
l of a set L there is associated a set CZ. In such a situation we say that
the family of sets Cl is indexed by the set L. It is natural to denote this
indexed family by {Cl;l E L } . If {Cl;l E L } is an indexed family then
by
Cl we understand the union of the family Cl, that is, the set of all
u
1EL
elements belonging to at least one set Cl.
Similarly, the intersection
A1 is by definition the set of all elements
n
IEL
belonging to every set Al. A family of sets can be indexed by any set, not
necessarily by a subset of positive integers. If, for instance, A1 is the set of
points on the straight line between the points (1, 0) and ( I , 1) in the plane
and L is the set of real numbers between 0 and 1 then
Al is the interior
u
1EL
of the square with vertices (0, 0), (1, 0), (1, 1) and (0, 1).
43
Sets
2.1.4
Cartesian product of sets
If A and B are sets then the set of ordered pairs of elements, (a, b),2 the
first from A and the second from B , is called the Cartesian product of
A and B and denoted by A x B. Expressing this in symbols, we have
A x B = { ( a , b ) ; a E A , b E B}. Two ordered pairs ( a , b) and (c, d) are
defined to be equal if and only if a = c and b = d. It is natural to define
A XB x C as the set of ordered triplets (a, b, c) with a E A , b E B and c E C.
One would perhaps expect that A x B x C = ( A x B ) x C = A x ( B x C).
However this is not strictly correct since ( A x B ) x C is a set of pairs of
things in which the first thing is a pair of elements and the second thing is a
single element; ( ( a , b), c) is its typical member, whereas a typical member
of A x B x C is a triplet ( a , b, c). In this book we shall not make this fine
distinction between ( A x B ) x C or A x ( B x C) and A x B x C. In the
same way the idea of a Cartesian product of four or any finite number of
sets should be clear. For A x A we write A 2 , and for A x A x - - - x A with
n factors we write An.
Warning: Realise that A x B generally is not equal to B x A .
2.1.5
Some common sets
We assume that the reader is, at least on an intuitive basis, familiar with
real numbers. The theory of real numbers (including, for example, the definition of integers, rationals, etc.) is systematically reviewed in Chapter 4.
For ease of reference we name here some subsets of reals as follows. The
set of all real numbers will be denoted consistently by R.
N is the set of natural numbers, N = (1, 2, 3, . . .};
NO is the set of non-negative integers, No = ( 0 , 1, 2, 3, . . . };
Z is the set of integers, Z= ( 0 , 1, -1, 2, -2, 3, -3, . . .};
{Q
Q= E; p
Z,q E N};3
P is the set of positive reals, P = {x; x E R, x > 0 ) .
Q is the set of rationals,
E
2Using set notation, the ordered pair (a, b) can be written (a, b) = (a,( a , b} }. In
fact this equation can be used t o define an ordered pair.
3A more precise formulation of the definition of Q, using terminology introduced so
far, would read Q = { ( p , q ) ; p E Z,q E N}
44
Introduction to Mathematics with Maple
Exercises
Exercise 2.1.6
Give examples of sets M , N , P , Q such that A4
and neither P c Q nor Q c P.
For the sets listed in Table 2.1 show that M I U
M3nM4=O.
Exercise 2.1.7
Mi,
cN
M2
=
Exercise 2.1.8
In this exercise A, B , C are arbitrary sets. Discover
which of the following relations are correct. Prove the correct ones and give
examples (strictly, counter-examples) to show that the others are false.
(1) A n ( B u C ) = ( A n B ) u ( A n C ) ;
(2) ( A u B ) n C =( A n C ) u ( B u C ) ;
(3) A u ( B n C ) = ( A u B ) n ( A u C ) ;
(4) A U A = A , A n A = A ;
( 5 ) ( A n C ) u B =( A u B ) n ( C u B ) .
Exercise 2.1.9
Let An = {x; x E
N, x a multiple of n}, B1 = (2;x E
u n u Bz,nB1.
00
R,x < l } , L = {x : x E Iw, x > O}. Find
00
An,
n=2
Exercise 2.1.10
@ Exercise 2.1.11
An,
n=2
EL
EL
What can you say about the sets A, B if AUB = AnB?
Prove that
(1) ( A \ B ) n C = ( A n C ) \ ( B n C ) ;
( 2 ) ( A\ B ) u ( A \ C ) = A \ ( B n C ) ;
(3) ( A \ B ) n ( A \ C ) = A \ ( B u C ) .
@ Exercise 2.1.12
Parts (2) and (3) of Exercise 2.1.11 can be generalized
to families of sets. Prove that, if L is an index set, Al a family indexed by
L , and X is any set, then
(These relations are known as de Morgan’s Rules.)
Sets
@ Exercise 2.1.13
Let A1 and
€31
45
be two families of sets indexed by a set
L. Prove that
U A E \ U B l c U(Al\Bl).
1EL
1EL
1EL
Also show by an example that the inclusion cannot, in general, be replaced
by equality.
2.2
Correct and incorrect reasoning
The ability to reason correctly is essential in mathematics. Care is often
needed, as the following fallacious argument shows. Let b be an arbitrary
real number, and let c be the same number, that is, c = b. Multiply this
equation by c and subtract b2, giving
that is,
(C
+ b)(c - b) = b(c
-
b),
(2.1)
and hence, dividing by c - b we have
and therefore c = 0.
We appear to have proved that an arbitrary real number c must be zero.
As this is absurd we must look for an explanation. The error occurred when
passing from Equation (2.1) to Equation (2.2), and involved an incorrect
application of the following theorem.
If yx = xx and z # 0 then y = 2.
We applied this for x = c - b and overlooked the fact that c - b = 0.
Consider the previous theorem again. It is of the form: ‘If ... then
. . . ’. The part of the sentence starting with ‘If’ is called the hypothesis
(or assumption(s) or condition(s)) and the phrase starting with ‘then’ is
called the conclusion (or assertion(s)). The moral of the above example is
that when applying a theorem one must make sure that the hypothesis is
satisfied.
Sometimes a mathematical theorem is not expressed in the form ‘ I f . ..
then ...’, but even with such a variation of form the hypothesis can be
identified. For example the theorem
46
Introduction t o Mathematics with Maple
T: The product of two consecutive integers is divisible by two.
can be rephrased to exhibit the hypothesis and the conclusion more clearly
as
T:If n is an integer then the number n(n + 1) is divisible by 2.
If the conclusion and the hypothesis are interchanged a new theorem
is obtained, which is called the conuerse of the original theorem. The
theorems P and C below are converses of each other.
P: If a right-angled triangle has hypotenuse c and other sides a , b then
a2 + b2 = c2.
C: If the sides of a triangle a, b, c satisfy a2 b2 = c2 then the triangle
+
is right-angled and c is its hypotenuse.
The converse is different in content from the original theorem. In the above
example it happens that both theorems are true. The next example is
different.
E: If an integer is divisible by six then it is an even number.
S: If an integer is even then it is divisible by six.
Clearly S says something quite different from E; moreover S is false while
E is true.
Summary: A piece of mathematical knowledge is usually stated in the
form of a theorem, which has a hypothesis and a conclusion. If these are
interchanged a new theorem is obtained. It is wrong to assume that the
converse holds simply because the original theorem did. If one suspects
that the converse is true and wishes to use it, then one must prove it.
Actually, the converses of many important theorems are true. In such
cases instead of stating the two separate theorems we use the phrase ‘if and
only if’, and thus combine both theorems into one statement. For example,
P and C together read
PC: The triangle with sides a , b, c is right-angled with hypotenuse c if
and only if a2 b2 = c2.
+
The abbreviation ‘iff’ is sometimes used for ‘if and only if’.
Sets
47
Exercises
Exercise 2.2.1
State the following theorems in the form ‘ I f . . . then
(1) The diagonals of a rhombus are perpendicular.
(2) Every algebraic equation of degree one or higher has a solution.
(3) Every two positive integers have a greatest common divisor.
(4) Grandfather’s knee pains whenever it rains.
Exercise 2.2.2
false.
Give an example of a valid theorem whose converse is
Exercise 2.2.3 Give an example of a true theorem with a true converse.
Use the phrase ‘if and only if’ to combine both theorems into one statement.
2.3
Propositions and their combinations
What has been said in the last section is really only a vague introduction
to certain ideas. We now consider them more carefully.
The typical structure of a theorem has already been mentioned: ‘If
something, then some other things’. But what are the ‘things’ in question?
The first point is that they cannot be any phrase or sentence (in English)
like (Hooray!’, (HOWare you?’, ‘Go home!’, ‘The colour seven is tropical.’
They must be capable of being true or false (but not both). We shall call
such sentences propositions.
If A and B are propositions, then each may be true or false, and there
are four possible cases in relation to the truth of both (see Table 2.2).
Table 2.2 Truth and falsity of propositions
I.
11.
111.
IV.
A
A
A
A
is true, and so is B ;
is true, but B is not;
is not true, but B is;
is not true, and neither is B.
Consider the following example.
48
Introduction t o Mathematics with Maple
A: John, the engineering student, achieved top grades.
B: John, the engineering student, has passed the year.
In this example case I1 cannot occur. If A and B are propositions, and case
I1 does not occur, we say that A implies B , or that B follows f r o m A , and we
denote this by A + B. Such a relation between two propositions is called
an implication. We may express the implication A + B in other words by
saying If A, then B; here A is the hypothesis and B is the conclusion. The
reader will remember from Section 2.2 that when A
B then B need not
imply A . In our example, if John has all top grades he passes (so A + B ) ,
but if he passes he need not have all top grades, so B + A is not true.
By forming an implication A + B we combine two propositions A and
B into a new one; i f A then B. This new proposition is true if case I1 in
Table 2.2 does not occur.
After having made precise the meaning of the implication A + B ,
it is time to reflect and realize that in ordinary life many people do not
understand implication the way mathematicians do. For example, our interpretation of A + B means that for a false proposition A the implication
A + B is true no matter what proposition B is. This is clear, since if A
is a false proposition and B is any proposition, the case I1 does not occur
(neither does I). Consequently, i f 3 is less than 1 then all numbers are equal
is a true implication, and so is if 3 is less than 1 then not all numbers are
equal.
Implication is an example of combining two propositions into a new
one. There are many ways of forming new propositions. The negation of a
proposition C is a proposition which is true if C is false and false if C is
true. The negation of C is usually denoted by not C or 1C. For example,
if D is the proposition the number six is even then 1 D is the number six is
not even. The success of many proofs hinges upon a correct formulation of
the proposition 7 C corresponding to a given C. For instance, if C means
every blonde girl has a handsome suitor then 4’means it is not true that
every blonde girl has a handsome suitor or, in other words, there is at least
one blonde girl who either has n o suitor or her suitor is not handsome.
Given two propositions A and B we can form a new proposition A and
B. For example, if A is 16 is even and B is 16 is positive then A and B
means 16 is even and positive. We shall agree that the proposition A and
B is true if both propositions A and B are true. We denote the proposition
A and B by A A B.
The last combination of two propositions A and B we consider is A or
49
Sets
B. It is taken to mean that a t least one of the propositions A and B holds,
that is to say case IV in Table 2.2 does not occur. Instead of A or B we
may sometimes say either A or B. We denote the proposition A or B by
AVB.
Again, in ordinary life, some people may understand the proposition A
or B differentl~.~(They would say ‘either A or B or both’ to cover the
meaning of our ‘ A or B’.) If A is three is greater than zero and B is three
is equal to zero then A V B is three is greater than or equal to zero and is a
true proposition. Clearly, in this example, case I1 occurs, case IV does not
occur, and A or B is true (by our convention).
If A J B and B + A we say that A and B are equivalent and denote
this by writing A H B. Using the terminology from Section 2.2 we say that
A holds if and only if B’holds. The relation A e B is called equivalence.
The truth of the implication A + B is often proved by showing that
4 3 + -A since both implications mean that case I1 of Table 2.2 does not
occur. Similarly, the equivalence A H B is often proved by proving both
A + B and 1 A + 1 B .
for concise and convenient
In this book we shall use the signs 3 or
recordings of many theorems and mathematical statements. For instance
we can state the theorem from Section 2.2 like this:
For real numbers x, y, z the implication
(zy = z z and x
# 0) J y = z
(2.3)
holds (that is, the implication is true). Another example of this kind is
x > 1J
X
> 0.
(2.4)
This certainly looks like a true implication.
Unfortunately, there is a snag in (2.4). Strictly speaking x > 1 is not
a proposition (in terms of our earlier definition); this is because it is not
possible to decide whether or not z > 1 is true unless we know what x
actually is. In order to circumvent this difficulty we specify the precise
meaning of (2.4). We shall say that (2.4) (or a similar ‘implication’, for
example, (2.3) ) holds (is true) if it becomes a true implication after substitution of an arbitrary real number into (2.4) (or numbers z, y, z into (2.3) ).
Sometimes in statements like (2.3) or (2.4) we shall substitute only natural
*As in the question ‘Would you like tea or coffee?’ The implication here is ‘. . . t e a or
coffee but not both’. In mathematical logic this is known as the exclusive or, but will
not be used in this book.
Introduction t o Mathematics with Maple
50
numbers or elements of a certain set, but it will be clear from the context
what substitutions are envisaged.
Now we are in a better position to appreciate the convention that
false A
+
any B.
If we substitute either x = 1/2 or x = -1 into (2.4) then the hypothesis
is false and the conclusion is true in the first case and false in the second.
The implication, however, stays always true.
Exercises
Exercise 2.3.1
Let A be 2 is odd and B be 3 is odd. Decide whether
or not the following implications hold.
(1) A + B;
(2) 1 A 3 4 3 ;
(3) A +
+;
(4) B
3 A;
Exercise 2.3.2
In implication (2.3) in the text substitute
(1) x = 1, y = z = 2;
(2) x = 2, y = 2, z = 3;
(3) x = 0, y = 3, z = 9;
and verify that the resulting implication is true in each case.
@ Exercise 2.3.3
Prove that ( A @ B ) if and only if ( B H A).
*
Exercise 2.3.4
Prove that for positive numbers a and b, ( u 5 b)
(u2 5 b2) by showing ( u 5 b) + (u2 5 b2) and l ( u 5 b) 3 i ( u 2 5 b2).
(Inequalities are systematically treated in Chapter 4, Section 4.2.)
Exercise 2.3.5 Explain why the word ‘implication’appears in quotation
marks in one place on page 49.
Exercise 2.3.6
Verify that the following implications are correct.
(1) A 3 (AVB);
( 2 ) ’ ( A A B ) (1A) V (1B);
(3) [ ( A B ) A ( B C ) ]+ ( A H C ) ;
*
Exercise 2.3.7
*
*
If M , N are sets prove
Sets
51
(1) ( M \ N ) U N = N H M c N ;
(2) M U N C N H M C N ;
(3) ( M \ N ) U ( N \ M ) = 0 M = N .
*
Exercise 2.3.8 If A and B are propositions state the negation of A V B
and A + B. (Example: the negation of A A B is 1 A V 1B.)
Exercise 2.3.9 Decide which of the following propositions are true and
which are false. Also state, in words, the negations of these propositions.
(1) There is an integer x such that for all integers y the equation xy = y
holds.
(2) For all integers y there is an integer x such that the equation x y = y
holds.
(3) For all integers y there is an integer x such that the equation x y = y2
holds.
(4) There is an integer x such that for all integers y the equation xy = y2
holds.
2.4
Indirect proof
+ B and B is false then only case IV in Table 2.2 is possible (since I1
is ruled out by the truth of A + B ) and consequently A is false. If someone
says ‘If I were rich I would buy a new house’, there is a clear implication
that the person is not rich. By the same reasoning, if 1 A implies something
false then 1 A is false, that is, A is true. This is used in proofs, since quite
often the simplest way of proving that proposition A holds is to show that
1 A implies something manifestly wrong. This is known as indirect prooj
In philosophical debate the method of indirect proof is known as reductio
ad absurdum. A defence lawyer can show the innocence of the accused by
showing that the assumption that the accused committed the crime leads
to the inescapable conclusion that the accused was at two distant places at
the same time-obviously an untenable proposition.
The next example is a classic example of an indirect proof and goes
back to the ancient Greeks. The argument appears in one of Euclid’s books
(written approximately 300 B.C.). We briefly summarise the prerequisites
for the proof. A prime is an integer greater than or equal to 2 which has
no divisors except 1 and itself. A theorem from elementary number theory
says that every positive integer greater than or equal to 2 is divisible by
some prime (possibly itself).
If A
52
Introduction to Mathematics with Maple
Now we prove: There are infinitely m a n y primes.
For an indirect proof we assume the contrary. Then we can list all the
primes as p l , pa, p3, . . . , p n . Now consider the integer formed by multiplying these together and adding 1: N = p l -pa .p3 - - - - p n 1; this is divisible
by some prime, that is, one of the pi. Consequently 1 is divisible by pi since
1 = N - p l - p2 p3 - - .* p n and both N and p l - p2 - p3 . - - p n are divisible
by pi (to make this rigorous we would also need to show that if a and b
are divisible by p i , then so is a - b). However, the proposition that 1 is
divisible by a prime (greater than 1) is obviously absurd. We have reached
our goal: the theorem in italics has been proved.
We have just given a simple example of a short indirect proof. In mathematics, however, we often encounter situations where an indirect proof
requires a number of fairly difficult steps and students are apt to start worrying in the middle of the proof because things look a little strange. If
this happens they should realise that in an indirect proof ‘the stranger, the
better’ for as soon as we reach real absurdity we have attained our goal (of
course, assuming that we didn’t make any errors).
+
-
Exercises
Exercise 2.4.1
Prove that each of the following numbers is not rational.
Exercise 2.4.2
Prove that every prime other than 2 is odd.
Exercise 2.4.3 Prove that for every prime p
integer n such that either p = 6n - 1 or p = 6n
Exercise 2.4.4
irrational.
Prove that if a E
Q,b
E
2 5 there exists a positive
+ 1.
Q,b #
0 then a
+ b J Z is
53
Sets
2.5
Comments and supplements
The concept of a set was not introduced precisely in Section 2.1, it was only
illustrated by examples. Now we give an example showing the inadequacy
of such a simple intuitive approach.
If z is an object then by {x} we denote the set consisting of one element
x. A set having only one element is called a singleton. If X is a set we may
ask whether or not { X } E X . Clearly, if X = 8 then { X } X because
nothing is in X . On the other hand it seems that the family F of all
singletons has the property that { F } E F because { F } is a singleton. Now
define a set Y by Y = { { X } ; { X } f- X } . This seems innocent enough.
Unfortunately we have a paradox on our hands. Neither { Y } E Y nor
{ Y } 4 Y . Indeed, if { Y }E Y then { Y } 4 Y by the defining property of
the set Y . On the other hand, if { Y } Y then again, by the definition of
Y , we have { Y }E Y . In either case we have both { Y }E Y and { Y } Y ,
which is absurd.
This example is a slight modification of a famous paradox, the so-called
Russell’s Purudox, named after its inventor, the British mathematician,
logician and philosopher Bertrand Russell.
Any reasonable theory must be free of any paradox. How are we to get
rid of the paradoxes of set theory? (There are others besides Russell’s.)
Perhaps we may say dogmatically that constructions like the set of all
singletons or the set of all sets are not legitimate in set theory. When using
a property defining a set it is safe to consider only subsets of a set which
we know (like the reals) or which can be constructed from known sets (like
the Cartesian product R x R) . Any mathematical discipline should be based
on an already established branch of mathematics or founded axiomatically.
Since set theory is most basic we have no choice but to build it axiomatically.
An axiomatically based discipline starts with a few basic propositions axioms-which are not questioned for truth or validity, and develops from
these axioms by logical means. Some may feel that axioms must be simple
and self-evident. However, what is obvious and simple to a mathematician
who has absorbed a great deal of knowledge accumulated over more than
two millennia need not be obvious or simple to everybody. The question
of the truth of axioms in the modern interpretation of axiomatic theories
simply does not arise. Firstly, circular definition must be avoided. Consequently the primitive terms in the axiomatic system cannot be defined.
If we set up an axiomatic system, say for set theory, then we simply agree
to call the objects which satisfy the axioms sets. It can be objected that
4
4
4
54
Introduction t o Mathematics with Maple
with such an interpretation axiomatic mathematics becomes a meaningless
game. Mathematics, because of its applicability to other sciences, engineering and its prominence in the history of human thought, is certainly not
meaningless. It may be a puzzle why mathematics is at all applicable but
this is a rather philosophical question which we will leave aside.5 To leave
the primitive terms undefined has its advantage in that the undefined terms
are capable of having several meanings. Imagine that we have proved some
theorems on divisibility of integers as logical consequences of a few basic
axioms. Then these theorems are applicable to any objects which satisfy
the axioms. This actually happens with objects called Gaussian integers,
and also polynomials with real or rational coefficients. We show this in
Subsection 2.5.1 An example of an axiomatic theory which does not require
any prerequisites is presented in Eves (1981, Lecture Thirty Five).
The desire for an easy verification that a certain set of objects satisfies a
list of axioms leads to the requirement of having as few axioms as possiblewithin reasonable limits: we don’t want to have so few axioms that the
system is too weak to do any significant amount of mathematics. If none
of the axioms (or parts of the axioms) is deducible from any of the others,
the system of axioms is called independent.
A fundamentally important requirement of any system of axioms is its
consistency. A system of axioms is consistent if it is impossible to prove
from it two propositions contradicting one another, for example, A and 1 A .
An inconsistent system is obviously useless for any serious study. Most
mathematicians now agree that in principle the only requirement a set of
axioms must satisfy is consistency.
An interesting application of sets and axiomatic ideas was developed by
the economist Kenneth Arrow. He was interested in the way the preferences of individual members in a society could be combined to produce an
overall preference for that society. For example, how do the individual preferences of people for breakfast cereal or clothes styles relate to the cereals
or clothes styles most in demand in society. This is of considerable interest
to economists and manufacturers. Arrow became famous, and received a
Nobel Prize in Economics in 1972, in part for this work. It was realised
that his work had much wider implications than just economics-it applies
5The interested reader is referred to a famous paper by Eugene P. Wigner: The
Unreasonable Effectiveness of Mathematics in the Natural Sciences, Communications in
Pure and Applied Mathematics, vol. XI11 (1960), pp. 1-14; or the book Mathematics
and Science, edited by Ronald E. Mickens, World Scientific Publishing, 1990. The
contributors to this book discuss aspects of Wigner’s paper.
Sets
55
to any area in which individual preferences need to be combined in some
way. In elections, voters (usually) have an order of preference for the candidates. The system of axioms set out by Arrow can be interpreted as a bare
minimum statement for a voting system to be regarded as ‘democratic’. Arrow proved that the axioms he set out were, in fact, inconsistent, implying
that the goal of finding a truly ‘democratic’ voting system is impossible.
His work, which is quite easy to follow, is set out in Arrow (1963). Other
people have tried, without success, to find other axioms for a ‘democratic’
voting system. Some of these, with proofs of inconsistency, can be found
in Kelly (1978).
The first scientific discipline which was axiomatized was geometry about 300 B.C. by Euclid. The geometry he created (or, rather, put on an
axiomatic basis) is now called Euclidean geometry. The critical study of the
foundations of Euclidean geometry was undertaken towards the end of the
1gth century, shortly after discoveries of other geometries. It was found that
Euclid used tacitly some propositions unwarranted by his axioms. Euclid’s
axioms were then augmented and Euclidean geometry perfected. Since
their introduction no-one seriously doubted the consistency of the axioms
of Euclidean geometry. It was, however, only later, in the 20th century that
consistency was proved by the American mathematician of Polish origin, A.
Tarski, and then only for a section of Euclidean geometry called ‘elementary
Euclidean geometry’.
The history of Euclidean geometry seemingly offers a way out of the
problems besetting set theory. Set theory ought to be developed from a
system of axioms which prevent the occurrence of the paradoxes. This was
actually done in the first half of the 20th century. Among the axioms of
set theory is one which prohibits constructions like the one in Russell’s
paradox. Then we should aim at proving consistency for the axioms of set
theory. Unfortunately, such efforts are doomed to failure. An Austrian
mathematician, K. Godel, proved that the consistency of the generally accepted axioms of set theory cannot be proved by mathematical means.6
This creates a problem, in that we do not have an entirely satisfactory philosophy for the foundation of mathematics. The foundation of mathematics
is not as rock solid as the layperson believes.
We may add that there is one somewhat controversial axiom in set
6Actually Godel proved more than this. He showed that if the axioms of set theory
are consistent, then it is possible t o formulate a proposition A such that neither A nor
- A can be proved by mathematical means. ‘The axioms are consistent’ is one such
proposition. These propositions are called undecidable.
56
Introduction to Mathematics with Maple
theory - the axiom of choice mentioned in Chapter 9. It leads to rather
surprising results, some of which are contradicted by our i n t ~ i t i o n .When
~
the foundations of mathematics are studied there is also a need for an
analysis of the logical means used in deriving conclusions. Mathematicians
are more divided on which arguments are permissible and which are not,
rather than which system of axioms should be used. Not all mathematicians
are prepared, for example, to accept an unrestricted use of indirect proof.
Set theoretical language plays only an auxiliary, but convenient, role in
the mathematics considered here: much of the mathematics in this book
was in existence long before set theory was born.
Here we leave axiomatics and set theory. The interested reader is referred to Halmos (1974).
2.5.1
Divisibility: A n example of an axiomatic theory
This subsection requires more mathematical maturity than the rest of this
chapter and uses some concepts explained only later in the book. Skipping
this subsection will not affect understanding of the rest of the book.
A set R is called a ring if, for every two elements x E R and y E R
there is associated a sum x y E R and a product xy E R such that the
axioms in Table 2.3 are satisfied.
The set of even integers as well as the set of numbers of the form 9m+15n
with rn,n E Z form a ring. The sum and product of even integers is even.
The axioms from Table 2.3 are obviously satisfied for even integers, as they
are for numbers of the form 9 m 15n.
A ring R is called Euclidean if it has the following properties:
+
+
(1) if x # 0 and y # 0 then xy # 0;
(2) for every element T E R with T # 0, there is a positive integer N ( T )
such that
(a) N(xy) 2 N ( z ) if x # 0 and y # 0;
(b) for every two elements x, y with y
such that
and either
T
= 0 or
#
0 there are q and
T
in R
N ( T )< N(y).
7There are no problems about independence and consistency of the axiom of choice,
however. Godel showed that the axiom of choice is consistent with the other axioms of
set theory, and Cohen proved that it is independent of the other axioms.
57
Sets
Table 2.3 Ring axioms
:X + Y = Y + X
:x
(y Z) = (X y) z
A3 : There is an element 0 E R
such that 0 x = x for all
A1
A2
+ +
+ +
Mi : X Y = Y X
M2 : x(?/z)= (XY)Z
+
x in R
every element x E
R there exists an element
(-2) E R such that (-x)
x = 0.
D : x(y Z) = xy
A4 : For
+
+
+ XZ.
The integers form a Euclidean ring: q, r are the quotient and the remainder,
respectively, by division of x by y; N is the absolute value, N ( r ) = Irl.
Polynomials with rational coefficients also form a Euclidean ring. q and r
can be found by the long division algorithm (see Section 6.3). For N we
take the degree of the polynomial.* In a ringg x is divisible by y if there
is q E R such that x = qy. If x is divisible by y we say that y is a divisor
of 2.l' The greatest common divisor d of two elements 2, y is defined as
a common divisor (that is x = m d and y = n d for some m, n in the ring)
which is divisible by any other common divisor of x, y. For example, in Z
the numbers -4 and 4 are the greatest common divisors of 8 and 12. If d
is a divisor and D the greatest common divisor of two elements in iZ then
D = m d for some integer m and consequently 1
0
12 Id[. In Z the greatest
common divisor of two elements has the largest absolute value among all
common divisors. The set of all greatest common divisors of a and b is
denoted by gcd(a, b). It is common but perhaps a little confusing to write
d = gcd(a, b) instead of d E gcd(a, b). If z = 0 or y = 0 then the greatest
8Another example of a Euclidean ring is the Gaussian integers: these are numbers of
the form a bz with a, b E Z and z2 = -1. Here i t is less obvious what q, T in (2.5)
should be. Let x = a zb, y = c zd, x/y = Q zp. Denote by q1 and 42 the nearest
integer to Q and p, respectively. Then la - q1 I 5 1/2 and - q2 I 5 1/2. Set q = q1+ zq2
and T = x - qy with T = T I + z T ~ .We define N ( x ) = )xI2,that is N ( a + z b ) = a2 b2 and
5 lyI2(f f ) < IyI2 = N(y). Consequently
have N ( T )= IrI2 = Iy(q1 - a ) y(q2 the Gaussian integers form a Euclidean ring.
'Not necessarily a Euclidean ring.
"0 is divisible by any element of R and no element distinct from 0 is divisible by 0.
+
+
+
+
+
+
+
Introduction to Mathematics with Maple
58
common divisor of x, y is 0. Otherwise two greatest common divisors differ
by at most a factor which is a divisor of 1. Indeed, if d, D are two greatest
common divisors of x and y then d = m D and D = nd for some m, n E R.
Consequently m n = 1 and m, n are divisors of 1. In Zthe divisors of 1 are
1 and -1. In the ring of polynomials with rational coefficients the divisors
of 1 are all rational numbers distinct from 0.
Theorem 2.1
Two elements x, y in a Euclidean ring always have
a greatest common divisor.
Proof. The Theorem is obvious if x or y is zero. We may assume for the
rest of the proof that x # 0 and y # 0. Consider the sets
M = (mx + n y : m E R , n E R } ,
P = { N ( w ) : w E M , w # o}.
P is a nonempty set of positive integers and has its smallest element N ( d )
(see Theorem 4.10). Let w E R be arbitrary. Since R is Euclidean we have
w = dq+r and either r = 0 or N ( r ) < N ( d ) . However, the last inequality is
impossible since N ( d ) is the smallest element of P . Consequently r = 0 and
d is a divisor of x and y because both of these elements are in M . Every
common divisor of z and y divides any element of M , and in particular d.
Hence d is the greatest common divisor.
0
The proof just given is a good example of an existence proof characteristic of modern mathematics. The existence of a mathematical object is
established without any laborious process of actually constructing the object. We now show a constructive and effective way of finding the greatest
common divisor. This can be done by the so-called Euclid’s algorithm. If
y # 0 then we can find qo and r1 such that
If
r1 = 0
then gcd(x, y) = y. If
r1
# 0 then again
7-2 # 0 we continue the
process and obtain elements r1, 7-2, r3 . . . and the corresponding natural
numbers N(r1) > N(r2) > N ( r 3 ) > .... The process must end after at
If r2 = 0 then it is easy to see that gcd(x, y) = T I . If
59
Sets
most N(r1) steps when we obtain
It can be shown by mathematical induction (see Chapter 5) that r n =
gcd(z, 3 ) . Let us illustrate the Euclid algorithm by a simple example in Z.
Start with x = 133 and y = 119.
133 = 119 X 1 -I- 14,
119 = 14 x 8
+ 7,
14=7~2.
gcd(133,llg) = 7
Maple can be employed to do the computations in the Euclid algorithm.
The command for finding the remainder of division of integers z by y, as
we know, is irem(x,y). Let us find gcd(215441,149017)by using MupZe.ll
>
irem(2I5441,149017);
66424
>
irem(149017,66424) ;
16169
>
>
# We can now use t h e same Maple command repeatedly
# t o evaluate t h e successive remainders,
# and use t h e s e as t h e d i v i s o r i n t h e next s t e p
>
irem(%%,%>
;
1748
"The command irem(%%,%>
; below need not be retyped, you can use the menu of
Edit to copy and paste.
Introduction to Mathematics with Maple
60
437
>
irem(%%,%) ;
0
Using Maple and the Euclid algorithm we found that 437 is the greatest
common divisor of 215441 and 149017. One can do even better: Maple has
an inbuilt facility for determining the greatest common divisor.
The command is igcd if we are working in iZ and gcd if we are working
in the ring of polynomials with rational coefficients. For example
~
~~
~~
a
>
~
igcd(215441,149017) ;
437
>
gcd(x-2-5*~+6,~-2-4*~+4)
;
2-2
1
Exercises
Exercise 2.5.1 Define the greatest common divisor ofn elements. Show
that, in a Euclidean ring, n elements always have a greatest common divisor.
Exercise 2.5.2
R is a ring, w E R, z E R,h E R with gcd(w, h) = 1.
Show that gcd(w, z ) = gcd(w hz, z ) . Prove this and apply it to finding
a greatest common divisor of the following polynomials:
+
+
+ +
+ +
(1) xlo0 ax2 bx 1, ax2 bx 1;
(2) 2 6 x5 3x4 - 6x3 - 8x2 - 22 - 3, x2 - 2 2 - 3.
+ +
61
Sets
Exercise 2.5.3
Use Maple to find the greatest common divisors oE
(1) 108, 144;
(2) 1234567, 7654321;
(3) 5xs - 2x7 x6 5 x 5
+ +
+ 2x4 - x3 + x2
-
-
1,
x5 - x4
+ x2
-
1.
This page intentionally left blank
Chapter 3
Functions
In this chapter we introduce relations, functions and various notations connected with functions, and study some basic concepts
intimately related to functions.
3.1
Relations
In mathematics it is customary to define new concepts by using set theory.
To say that two things are related is really the same as saying that the
ordered pair ( a , b) has some property. This, in turn, can be expressed by
saying that the pair ( a , b) belongs to some set. We define:
[ Definition 3.1 (Relation)
A relation is a set of ordered pairs.
1
This means that if A and B are sets then a relation is a part of
the Cartesian product A x B. If a set R is a relation and ( a , b) E R
then the elements a and b are related; we also denote this by writing
aRb. For example, if related means that the first person is the father
of the second person then this relation consists of all pairs of the form (father, daughter) or (father, son). Another example of a relation is the set
C = { (1, 2), (2, 3), . . . , (11, 12), (12, 1)). This relation can be interpreted
by saying that m and n are related (rncn)if n is the hour immediately
following m on the face of a 12 hour clock.
We define the domain of the relation R to be the set of all first elements
of pairs in R. Denoting by domR the domain of R we have
domR = { a ; (a, b) E R } .
The range of R is denoted by rgR and is the set of all second elements of
63
64
Introduction to Mathematics with Maple
pairs in R, so
r g R = { b ; (a, b) E R ) .
For the relation father-offspring the domain is the set of fathers, the range
is the set of daughters and sons. For the 'clock' relation C clearly d o m C =
r g C = (1, 2, ..., 12).
We can often draw the graph of the relation R as in the diagram below.
The graph of R consists of all points P with first coordinate x and second
coordinate y such that P z (2, y) E R. The domain and the range of a
relation is schematically indicated in Figure 3.1.
" f
range R
0
I
/
domain R
X
Fig. 3.1 Graph of a relation
The graphs of
are shown in Figures 3.2 to 3.4, respectively. The graph of a relation can
cover a whole area, as in relations R2 and R3. Note that the boundary is
included in R2 but not included in R3.
Fzlnctions
65
n
Fig. 3.2 Graph of R1
Fig. 3.3 Graph of R2
The graph of a relation or a geometric picture generally is a telling guide;
it helps understanding, and often motivates a proof of a theorem or construction of a counterexample. We shall use geometry freely to motivate or
illuminate our theory. For this we presume that the reader is familiar with
elementary geometry, including basic analytical geometry. However, we
wish to emphasise that our theory itself is independent of any geometry and
can stand on its own feet without any support of graphical illustrations. Our
ability to draw a graph is restricted by the unavoidable imperfection of the
Introduction to Mathematics with Maple
66
Y
I
X
Fig. 3.4 Graph of
R3
drawing instruments. It is perhaps not clear from Figure 3.4 that the points
lying on the parabola y = x2 do not belong to the graph of R3. The next
example is far more serious. Let R4 = ((2, y ) ; either x or y is rational}.
Here the imperfection of the drawing instruments makes it impossible to
draw the graph of R4, Either we will blacken the whole plane or the graph
of R4 will be very incomplete. Obviously R4 cannot be graphed.
We can define a number of properties of relations.
0
0
0
A relation R is said to be reflexive if xRx for every x E dom R.
A relation R is said to be symmetric if x R y implies y R x for all
x E dom R and y E rg R.
A relation R is said to be transitive if x R y and y R a imply x R z .
A relation which is reflexive, symmetric and transitive is called an
equivalence relation, or simply an equivalence.
Example 3.1
Let Mk = {(x,y ) ; x E
Z,y
E Z, x - y divisible by
k}
for
67
Functions
k
N. Then
the relation Mk is reflexive, symmetric and transitive. (Prove
this!) The equivalence Mk is often called congruence modulo k, and the
y mod k (read x congruent to y modulo k) is used instead of
notation 2
E
=
XMky.
The ‘clock’ relation is not symmetric, nor reflexive, nor transitive. Examples of relations which are symmetric, reflexive or transitive are given in
Exercises 3.1.1-3.1.6.
If R is an equivalence relation on A and a E A then the set K =
{ b ; aRb) is called an equivalence class and a is a representative of K . Note
that K depends on a; we can emphasise this by writing K , instead of K .
Two equivalence classes K , and Kb are either identical or disjoint. For
congruence modulo 3 it is easy to see that K1 = K4 and K1 n K2 = 8.
Remark 3.1
Sometimes all elements in an equivalence class are identified,
in other words elements in an equivalence class are considered equal. If this
sounds too abstract, it is not. We are used from primary school to equations
2
4
6
like - = - = - . This is based on identification of elements in an
7
14
21
equivalence class. A common fraction p / q is just a pair of integers, say ( p , q).
If two pairs of integers ( p , q ) and (r, s) with q # 0, s # 0 are defined to be
equivalent if p s - qr = 0, then the pairs (2,7)
(4,14)
(3,21); this is the
above equation of common fractions. Similarly, in arithmetic mod 3 we write
1 = 4 = 7 = ....
=
=
Exercises
Exercise 3.1.1 Graph the following relations from R into R and decide
which relations are (a) symmetric, (b) reflexive, or (c) transitive.
If the relation is symmetric or reflexive, how is this shown by the graph?
Exercise 3.1.2 Let dom R = { 1, 2, 3, . . . , 12), and aRb if a and b differ
by 6. Write this relation as a set of pairs, and describe the relation using
the face of a clock.
Introduction to Mathematics with Maple
68
Exercise 3.1.3
Define separate relations which are:
(1) reflexive, but not symmetric or transitive;
(2) symmetric, but not reflexive or transitive;
(3) transitive, but not symmetric or reflexive.
@ Exercise 3.1.4
sets such that A =
uA
Let
be a set and B1 with 1 E L a family of disjoint
B1. Define a relation on A by declaring aRb if a and
1EL
b lie in the same Bl. Show that this relation is an equivalence.
Define a relation S for subsets of R by
S = { ( A , B ) ; A c R, B c R, ( A \ B ) U ( B \A) is finite).
Prove that S is an equivalence relation, and the family of finite subsets of
R is one equivalence class.
Exercise 3.1.5
@ Exercise 3.1.6
Define a relation R with dom R = Zand rg R = (Z\{O})
as follows: (m,n)R(r,s) if m s = nr. Prove that R is an equivalence. This
equivalence relation is a starting point for defining rational numbers in
a
terms of integers. The fraction - simply denotes the equivalence class
h
2 4 -2
-4
containing the pair (a, b); clearly - = - = - = - = . . .
3
6
-3
-6
3.2
Functions
The concept of a function is the mathematical abstraction of the notion
of dependence in ordinary life. The price of a commodity depends on its
quality, the average daily temperature depends on the date, the area of a
square depends on the length of its side. Using mathematical terminology
we say, for example, that the area of a square is a function of the length
of its side. A reasonably good description is to say that a function is a
rule which associates with every element of a set, called the domain of a
function, a uniquely determined element y called the value of the function.
In one of the examples above the rule associates with each date a number
equal to the average temperature on that date. However some doubts may
arise as to what is meant by the word ‘rule’. In this book we define functions
using set-theoretical language.
Functions
Definition 3.2 (Function)
69
A function is a relation F such that
[(z, y) E F and
(2,z )
E F]
+y =x
for all x E d o m F and y, z E r g F .
A function, then, is a set of ordered pairs such that no distinct pairs
have the same first element. If F is a function and ( 2 , y) E F then y
is the element associated with x by the rule for F . In the above example, if there are only two qualities of a merchandise, good and bad,
for which the prices are $20 and $10, respectively, the function is simply {(good, $20), (bad, $10)). Functions are usually denoted by letters
f, g, h , . . . or F, G, H , . . .. Sometimes letters of the Greek alphabet such
as 4, +, . . . are also used. Suffices are employed too. For example, we may
denote the function { ( x , x / ( l n x 2 )); x E R} by fn.
Since a function is a relation it is automatically clear what is d o m f ,
rgf and the graph of f . Definition 3.2 ensures that every line parallel to
the y-axis intersects the graph of f in at most one point. Not every relation
is a function; the relation fatherRdaughter is not a function because there
is (at least one) father who has two daughters. The ‘clock’ relation C is
a function because it does not contain two distinct pairs with the same
first element. The relation R1 from the preceding section is not a function
because the line x = 0 intersects the graph of R1 in two distinct points (see
Figure 3.2).
Iff is a function and ( 2 , y) E f then y is denoted by f ( x ) (read f of 2 ) .
The symbol f ( x ) is called the function value or the value of the function at
x. The range of f is simply the set of all function values. It is important to
make a clear distinction between f , which is a set of pairs, and f ( x ) ,which is
an element of rg f.’ It is possible to encounter in the literature expressions
like ‘the function x2’. This is a drastic and undesirable abbreviation for
{ ( x , x 2 ) ; x E R}; undesirable because it confuses function with function
value. We shall try to avoid such abuse of terminology.
Instead of writing f = { ( x ,3); x E D } we shall often write
+
f:x++y, X E D .
(Read: ‘ x goes to y’ or ‘f sends x to y’.)
IThis can be illustrated by reference t o using a computer or programmable calculator
t o evaluate some expression. The computer program is the function, while the output
displayed when the program is run with a particular value of z as input is the function
value. There is a major difference between the program and the output of the program.
Introduction t o Mathematics with Maple
70
The notation f : x H f(x) is also used, for instance, h : x I-+ x / ( 1 + x).
In a situation like this it is understood that the domain of f is the largest
subset of R for which f(x) makes sense. For h it is R \ {-1). If there is no
need to name a function we may even write x H y to describe a function,
for example, x w x / ( I + x).
The notation f : A I-+ B is used to indicate that domf = A and
rgf c B. This notation is convenient in theoretical discussions, but it has
the disadvantage that it does not explicitly describe the values of f. For
instance, f : R 4 P means that f is defined and positive for all real x,but
otherwise f can be arbitrary.
It is often convenient t o display the function pairs in a table, particularly
if we wish to draw the graph of the function. The table for the clock function
C is given below.
Table 3.1 The clock function
x
1 2 3 4 5 6 ’ 7 8 9 1 0 1 1 1 2
C(x) 2
3
5
4
7
6
8
9
10
11
12
1
If dom f is infinite (or simply very large) we may fill in only a few values
for x. Such an incomplete table for h : x I-+ x / ( 1 + x) is given in Table 3.2.
+
Table 3.2 The function h : x H x/( 1 z)
x
-3
-2
-3/2
-4/3
-1/2
-1/4
0
1
2
h(x) 3/2
2
3
4
-1
-1/3
0
1/2
2/3
J
The graph of a function usually conveys a good idea of what the function
is like. To graph a relatively simple function (by hand) might be laborious,
involving a considerable amount of calculation. This is where Maple is of
great help. Not only does it carry out the required calculations, but it
also has excellent graphical capabilities. We deal with graphs in Maple in
Subsection 3.3.4. Figure 3.5 shows the graph of h obtained by Maple.
Two functions are equal if they are equal as sets, that is, if they consist of
the same elements (ordered pairs). Equal functions have the same domain
71
Functions
/
1""""'"' 4
X
~~
X
Fig. 3.5 Graph of l f x
and associate the same y with the same x. Of course, they then also have
the same range.
Example 3.2 Denote by a!(z)the area of a square whose side has length
x. Using the arrow notation for functions we can write QI : x t+ x 2 , x E P.
Note that a! is not the same as s : x I-+ x 2 because s is tacitly defined on all
of R (doms = R).
So far, the letter z has been used for the first element of a pair belonging
to a function. Other letters may be used freely; for example for h above
Introduction t o Mathematics with Maple
72
and a from Example 3.2 we could equally write:
h : t H t / ( t + 1);
h = {(w,
V/(W
+ I));v E R - {-I}};
a!:yHy2, YEP;
a!
= {(s, s2); s E
ED}.
Example 3.3
The function x H x is perhaps the simplest function. It
is called the identity and we denote it by id. In other words id : x H x or
id(x) = x. By idA we denote the function {(x, x) : x E A } . This notation is
used by other authors too but it is by no means standard. It is perhaps a little
strange that there is a standard notation for many functions (like sin, cos, log)
but not for the very often occurring function id.
Example 3.4 For a set A the characteristic function 1~of A is defined
by l ~ ( x =
) 1 if x E A and l ~ ( x =
) 0 if x @ A. In terms of ordered pairs
1~= {(x, 1); x E A } U {(x, 0); x @ A } . If we do not specify otherwise we
shall assume automatically that dom1A = R. However, in mathematics one
often encounters a situation in which there is some general set X I fixed during
a discourse, like R in our case, and then it is understood that dom1A = X .
Clearly, l ~ ( z =
) 1 for every x E R. We can also write lw : x H 1. The
function l p is sometimes called the Heaviside function in honour of the
British mathematician and physicist Oliver Heaviside, who used it when
applying mathematics to solve problems in electrical circuit theory.
The function l g is called the zero function; clearly 1 0 : x H 0. It
is customary to denote 1 0 by 0; however, one should realize that 1 0 and
the number 0 are different objects. The context will (usually) make it
clear when 0 is the number zero (see Axiom A3 in Table 4.1 in Chapter 4)
and when it is used for the zero function. There is an intimate connection between sets and characteristic functions: see Exercise 4.7.1 and the
comments at the end of this chapter.
If f is a function then we define f(A)and f-l(B)
by
Obviously f(A) = (9;(2,y) E f and x E A } . The set f(A)is called
the image of A under f , and f - l ( B ) is called the pre-image of B. Some
properties of the symbols f ( A ) and f-l(B) are established in Exercise 3.2.5.
x2 then
Clearly rg f = f(dom f ) and dom f = f-l(rg f). If s : x
Functions
73
s({-1, 1, 2}) = {1,4}, s-1({1}) = (1, -l}, s-l({-l}) = 8. There is
some harmless ambiguity in this notation. The symbol f ( N ) may denote
the value of f at N E domf or it may denote the image of N under f for
a set N . Usually it is clear from the context what N is.
If f : D H V and A c D then the function f l (read
~ f restricted to A )
is defined by
f l ~
is called the restriction of f to A. Obviously, always f l C
~ f and
d o r n f l ~= A . Perhaps we should have written dom(flA) but on occasions
like this we shall omit the parentheses, since there is little fear of confusion
about the meaning. The function a from Example 3.2 is a restriction of
s : x H x2 to P. If g is a restriction of f to some set B , g = ~ J Bthen
,
f is
called the extension of g from B to D. The function s is the extension of
cu to R. Given f and B , the function f l is~ uniquely determined. However
for a given g and D there may be several extensions of g from domg to D
x2.1p(x) are both extensions of a from P to R.
For instance, s and h : x
It is a slight abuse of notation to denote by the same symbol a function
and its restriction or a function and its extension. However we may resort
to it in order to conform with widely used notation. For example, there is
often no such distinction as we emphasised made between a and s above.
Other words are used with the same meaning as the word ‘function’.
We may occasionally use the word ‘map’ or ‘mapping’ instead of the word
‘function’, particularly if either dom f or rg f is not a subset of R. Some
authors use the word ‘function’ only if the range is a set of numbers. The
terms operator, functional, transformation, transform are also used for some
functions. We may occasionally use the word ‘operator’ for a function
whose domain is a set of functions. For example, if c E R, f : R + R and
g : x H f(x - c ) then T : f
g is called the translation operator. Similarly
the function f ++ f ( is ~
called the restriction operator.
Exercises
Exercise 3.2.1
Let f : R
graphs off and g if
-+
R. Describe the relation between the
Introduction to Mathematics with Maple
74
Sketch the graphs for f = 1 p and f (x) = x2.
Use the results of Exercise 3.2.1 to graph x
Exercise 3.2.2
52 3.
+
@ Exercise
I--+
2x2 +
The function signum is defined by
3.2.3
sgn : z
where A denotes {z; x E R, x
Hl
p
- 1~
< 0). Graph the function sgn.
Let f be defined as follows: if x is irrational then
Exercise 3.2.4
P
f(x) = 0; if x = - with p E Z,q E N and p , q relatively prime, then
f
(F)
q
a. Determine the values f ( l ) ,f(-l), f(.5), f(Jz),and graph
1
=
the corresponding points.
graphed .)
@ Exercise 3.2.5
X , and B,
(1) A1
(2) B1
B1, B2,
(You should realise that f itself cannot be
Let f : X I-+ Y ; A, A l , A2, A1 for 1 E L be subsets of
B1 for 1 E L be subsets of Y . Prove:
c A2 =$ f(A1) c f(A2);
c B2 +-f-l(B1) c f-l(B2);
give an example showing that equality need
not hold;
(5) f ( A i ) \ f(A2) c f(A1
need not hold;
(8) f - l ( B l \
B2)
\ A2); give an example showing that equality
= f-l(B1)
\ f-l(B2);
75
Functions
*
(9) f(f-l(B)) B; f(f-l(B)) = B
B c f(X);
(10) f-l(f(A))3 A; give an example showing that A can be a proper
subset of f-l(f(A)).
(11) A1 c A
(12) B c y
* f(A(A1)= f(A1);
* (f(A)-l(B)= A n f-l(B).
Exercise 3.2.6
Let rg f = (0, 11. Show that there is a set A such that
f(z)= ~ A ( z )for every z E d o m f . [Hint: A = f-1(1).]
3.3
3.3.1
Functions in Maple
Library of functions
Maple includes hundreds of mathemat,:al functions. The notation ldr unctions in Maple is similar to that used before for functions: these functions
operate on one or more arguments, which are passed to the function in
parentheses. The arguments to the functions usually specify the point at
which the function should be evaluated. Some of the more common functions are shown in Table 3.3.
If you want to use a function you must know the name of the function and then you can ask Maple for help. You might wonder why the
common logarithm (logarithm to base 10) is not in the above table. The
reason is simply because mathematicians rarely use it, and in these days of
cheap pocket calculators for all sorts of specialised uses, and fairly cheap
computers, a negligible number of other people use logarithm tables. The
command ? l o g will tell you all you need about the common logarithm in
Maple. The following session illustrates use of the common logarithm.
In( 100)
In( 10)
In( 100)
-In( lo)
Introduction to Mathematics with Maple
76
Table 3.3 Some Maple library functions
Mathematical
name
Description
Absolute value
Square root
Exponential Function
Natural logarithm
Trigonomet ric function
Trigonometric function
Trigonometric function
Maximum of two
or more numbers
Minimum of two
or more numbers
The greatest integer
not exceeding x
The smallest integer
not preceding x
Maple usage
abs (x)
1
sqrt(x)
exp (x)
log(x) or h ( x )
sin(x)
cos (XI
tdx)
max (x y z )
)
m i n h ,y z )
floor (x)
ceil (x)
>
2
ln(2)
+ ln(5)
~
~~~~~~
There is an obvious error on the second last line, but we can blame only
ourselves for it because we did not take into account the fact that the first
command simplify (%%%) produces a result, namely 2, which now becomes
the most recent result for the next command. The commands should have
been
Functions
77
> simplify(%%%) ;simplify(%%%) ;simplify(%%%) ;
Now the best way to recover is
.3010299957
3.3.2
Defining functions in Maple
In Maple you can define your own functions. This allows you to evaluate
functions at various points, without having to retype the function value.
The following example shows how to define a function f (x) = x2 1, and
how to find the value of this function at x = 2 and at x = 5. The notation is
similar to the one we employed for defining functions: for H, in the Maple
definition of the function, type -> as a hyphen followed by a greater-than
sign.
+
>
>
(x) = x-2 + 1
f:=x->x^2 + 1;
# Define f
f :=x+x2+1
>
>
f a t 2 and 5
f(2); f(5);
# Evaluate
5
26
In the next example, the variable a is assigned the value 3, then the
function f (2)= ux2 is defined, and finally f ( 2 ) is evaluated.
Introduction to Mathematics with Maple
78
a := 3
>
f :=x->asx12;
12
As shown above, when you want to evaluate a function at a particular
point, first define the function (say f ) , and then specify the required point
in parentheses (for example, f (2)). More generally, you can place variables
or expressions in the parentheses, and Maple will evaluate the function with
respect to those variables or expressions. Look carefully at the following
example:
>
>
Define f (x) = x+2
f:=x->x+2;
#
f :=x+x+2
>
>
# Define g(x) = x-2
g:=x->xn2-2*x+1;
-
2x + I
g := x + x2 - 2 x + 1
x2-2x+l
79
Functions
( x + 2 ) (x2- 2 x + 1)
( x2 - 2 x + 1 ) 2
+2
In a similar way, you can define functions of two or more variables. Note
the slightly different way of typing such functions, as shown below.
>
>
#
Define f (x,y) = sqrt(xA2+yn2)
f := (x,y) ->sqrt (x^2+y^2) ;
f
:= ( x,y )
-+
sqrt( x 2
+ Y2 )
5
>
>
# Define g(x,y,z) = (x^2+~^2+~^2)/(X+y+Z)
g: =(x, y,z) -> (x-2+y-2+zn2)/ (x+y+z) ;
Use := and -> to define functions. For example:
f(x) = x2 1 is defined by f :=x->x^2+1;
f(z,y) = x2 y2 is defined by f := (x ,y)->xA2+y^2.
+
+
Introduction to Mathematics with Maple
80
3.3.3
Boolean functions
We call a function a Boolean function if its domain of definition is a set of
propositions and its range has either two elements, namely true and false,
or three elements, namely true, false and fail2 We shall tacitly assume
that if the value of a Boolean function at P is true then, indeed, P is a true
propostion. A Boolean function is realized in Maple by the command is.
For instance
>
is (Pi<22/7) ; is ( (sqrt (2) (3/2)>5/2); is (x>O) ;
true
false
FAIL
The last answer is natural, nobody can decide whether x is positive or
not unless it is known what x actually represents.
We now define two functions which we shall use in selecting elements
from a list.
> big: =x->is (x>2) ;sqbig:=x->is (xL2>2);
< x)
:= x -+ is(2 < x 2 )
big := x
+ is(2
sqbig
We have seen earlier in Subsection 2.1.2.1 how to select elements from a
list by using the op command, however very often it is needed to select the
elements by some property rather than by their position in the list. Let us
consider the list
>
L:=[Pi,-l,sqrt(3),-2,
L
0.5,1/31;
:= [7r, -1,
a,-2,
.5,
-131
from which we wish to create a list of elements greater than 2. The command is select and we use the function big defined above.
>
select (big,L) ;
[TI
Similarly, we can create a list of elements with squares greater than 2.
>
select (sqbig,L) ;
2Sometimes 0, 1 are used instead of false and true. Fail is used in Maple; on other
occasions the word undecided is sometimes more appropriate.
Functions
81
Other properties can also be used for selection or removal. For instance,
for removal of rationals from L, we first define a function
>
rat:=x->is(x,rational) ;
rat := x -+is(x, rational)
and then use the command remove .
>
remove (rat ,L);
a,
.51
Note that for Maple the floating point number .5 is not rational. If you
do not like .5 in the list you can remove it manually, for example
>
Cop(1,L) ,op(3,L)1;
a1
[r,
Exercises
Exercise 3.3.1
Which of the following numbers are prime: 1979, 7919,
1317237’ [Hint: is (x ,prime)]
Exercise 3.3.2
Find all complete squares between 100 and 200. [Hint:
Make a list L of these integers, define sq:=x->is(sqrt(x) ,integer) and
select (sq,L) .]
Exercise 3.3.3 Find all primes between 5000 and 5100. [Hint: Use the
methods of the two previous Exercises.]
3.3.4
Graphs of functions in Maple
Maple provides great power and flexibility in graphing various types of
functions. The form of the command for graphing a function is
plot (name,range,options);
(3.1)
The name and range are always compulsory. The word ‘range’ is used here
as in common English meaning the limits (for the first coordinate) within
which the graph will be displayed. It should not be confused with range
of a function. The ‘name’ can be a general expression for a function value.
This function value can be entered directly, as x2 - 3z is, or symbolically
as sin(x) is, below. The alternative for the ‘name’ in (3.1) can be a name
of a function, like sin. We deal first with ‘name’= function value. There is
Introduction t o Mathematics with Maple
a2
a great number of options, which are all listed in the help file. The ones
which are most likely to be needed by readers are explained as we go along.
>
>
p1ot(xA2-3*x,x=-l. .4) ;plot(sin(x) ,x=-Pi..Pi);
The graphs of these two functions are shown in Figures 3.6 and 3.7.
Note that the scales on the x- and y-axes are different. We shall see later
how to force Maple to produce the same scales.
Fig. 3.6 Graph of y
= x2 - 32
The alternative for ‘name’=symbol of a function is perhaps more logically consistent, since we graph functions. It also saves typing if the function
value is given by a long expression. The difference in ‘range’ in (3.1) is now
in that the form x=a. .b must not be used, but just the limits in the form
a. .b. We will illustrate this by an example with a function which we define
our selves.
>
# First define the desired function
>
f :=x->x+l/(x-1) -2;
83
Functions
0.:
Fig. 3.7 Graph of sinx
f
:=x-+x+-
1
(x - 1)2
Then p l o t the function;
> plot (f ,-infinity. .infinity) ;
>
#
The graph, shown in Figure 3.8, is reasonably good but, as must be
expected with the infinite range of x, looks a little rough. The use of the
infinite range is generally not recommended. It can, however, serve as a
guide where to have a closer look at the graph. In our example it is near
the dip in the curve. We now restrict the range of x to the interval where
we expect the dip to occur.
>
>
Restrict the range of x for the plot of the function
plot(f,3/2..4);
#
The portion of the graph restricted to 1.5 5 x 5 4 is shown in Figure 3.9.
84
Introduction t o Mathematics with Maple
infinity
infiiity
1
Fig. 3.8 Graph of x
+A
- unrestricted range
(x 1 y
-
2.5
1.5
Fig. 3.9 Graph of x
3
3.5
I
4
+ (x -11 ) 2 -* restricted horizontal range
~
Functions
85
If we specify the range of x as -1..5, Maple will not be able to cope3
because f takes very large values near x = 1. One possible way to help
Maple to produce a good graph is to limit the range of the y values as well,
for instance between 0 and 6. Note that the range of values for x must
also be given, and must precede the range of values for y. As with other
computer programs, Maple does just what you ask it to. The command
p l o t (f , O . .6) will plot the function f for x between 0 and 6 and will not
restrict the range of y.
>
p l o t ( f ,-I. .5,0..6);
The result of restricting x to the range between -1 and 5 and y to the
range between 0 and 6 is shown in Figure 3.10.
Fig. 3.10 Graph of x
1
+both ranges restricted
(x 1 ) 2 '
*
-
+
The graph of S : x H x sin 7x, which was shown in Section 1.1 (see
page 3) was not satisfactory. In order to obtain a good graph we can either
limit the range of x or use the option numpoints to increase the number
of points used to plot the graph. By default Muple uses 50 points, and it
3Try it on your machine.
Introduction to Mathematics with Maple
86
joins successive points by a straight line. In most cases this is satisfactory.
But with functions with a large number of maxima and mimima within the
range, more points are needed. With 150 points the graph is fine.
>
# F i r s t d e f i n e t h e f u n c t i o n t o be graphed
>
S :=x->x+sin(7*x) ;
>
>
# Now p l o t t h e f u n c t i o n :
>
>
# and t h e second time over t h e o r i g i n a l range
# but using more p o i n t s
>
p l o t ( S , - 5 . .5) ;plot(S,-20. .20,numpoints=i50);
# f i r s t with a r e s t r i c t e d range,
The results of these two graphs are shown in Figures 3.11 and 3.12,
respectively.
Fig. 3.11 Graph of x
+ sin 72: restricted range for x
87
Functions
Fig. 3.12 Graph of x
+ sin 72: additional points included
Maple adjusts the scales on each axis to fit the picture. This sometimes
results in an undesirable distortion as in the next graph of sinx.
>
plot(sin,O. .Pi);
This is shown in Figure 3.13.
The option scaling=CONSTRAINED ensures the same scale in both directions. The result of this is shown in Figure 3.14.
>
plot (sin,-Pi. .Pi,scaling=CONSTRAINED);
_______~~
~
It is possible to include multiple plots on the same set of axis by using
set notation, so that all the function to be plotted are enclosed within braces
{ and 3. Try plot({sin(x) , c o s ( x ) ,sin(2x)),x=-4. .4) ;.
88
Introduction to Mathematics with Maple
Fig. 3.13 Graph of sinz over the interval [0, 7r]
Fig. 3.14 Graph of sinz using option scaling=CONSTRAINED
Functions
89
Exercises
Exercise 3.3.4
Graph the following functions:
+
x3 x - 2;
H x3 - 32;
2 3 - 3x2 - 1.
(5) x
(3) x
(4) x
H
Exercise 3.3.5
picture:
(1) x
(2) x
3.4
In each case, graph each group of three functions in one
+
x H (x - 2)2, x H -(x
2)2;
H cos x, x H cos(x/2),x H cos 22.
H
22,
Composition of functions
If f and g are two functions then the composition of f and g , denoted by
f o g (read f circle g) is defined by
The domain of f o g is the set of all x E dom g for which g(x) E dom f .
In particular dom f o g = dom g if rg g c dom f . Generally f o g # g o f ;
see Exercise 3.4.1. The same function can be expressed as a composition of
two functions in many different ways; see Exercise 3.4.2. It is easy to see
that
and we shall denote this double composition simply by f o g o h.
Exercises
Exercise 3.4.1
Find f o g and g o f i f
(1) f : u H u 2 , g : x w x + l ;
(2) f = C(1, 3), (2, 4511, 9 = ((1, 21, ( 0 , 0 ) ) .
What are domf o g and domg o f in (2)?
Introduction to Mathematics with Maple
90
Exercise 3.4.2
A given function can generally be expressed as a composition of two functions in many different ways. Let
h : x H x2 + 22,
f :y
g :x
y2 - 1,
x + 1,
F : y~ y 2 + 4 y + 3 ,
G : x H x - 1.
Show that h = f
3.5
og =F o
G.
Bijections
A function f is said to be one-to-one (or injective) if
for every
XI, 2 2 E
dom f . An equivalent implication is
f(x1) = f(x2)
A function f is said to be
* x1 = x2.
one-to-one o n a set S if
fls is one-to-one.
Example 3.5
In a theatre, associate with each visitor the chair sat on.
This association defines a function which is one-to-one: each chair has a t most
one visitor sitting on it.
Example 3.6
The function h : x I+ x / ( 1 + x) is also one-to-one. Indeed,
if--x2
then it follows easily by removing fractions that x1 = x2.
1+x1
1+x2
If f(C) = D then we say that f maps C onto D , or that f : C H D
is surjective. If the theatre in Example 3.5 is sold-out, then the function
defined by the association between people and seats is surjective.
Y is called a bzjection of X onto Y if it is one-to-one
A function F : X
and onto (or it is both injective and surjective). A bijection F : X H Y
is sometimes described as a one-to-one correspondence between X and Y .
The function of Example 3.5 is a bijection of the audience onto the set of
chairs in the theatre. The function h from Example 3.6 is a bijection of
X
R\{-1} ontoR\{l}. Indeed, if y # 1 there is an x such that -= yand
x+l
X
for no x is -= 1. For given X and Y there can be several bijections of
x+1
Functions
91
X onto Y . For instance, the identity function, the clock function C shown
in Table 3.1 and the mapping from one number to the number diametrically
opposite on the face of the clock are all bijections of (1, 2, 3,
itself.
. . . , 12) onto
Exercises
Exercise 3.5.1
Prove that f : x H A x 3
&(u3- w3) u - w = (u - w)A with A > 0.1
+
3.6
+ x is one-to-one.
[Hint:
Inverse functions
If S is a relation then the relation S-1 = ((9, x); (2, y) E S } is called the
inverse relation to S or simply the inverse of S . Since the point ( u ,w) is
the mirror image of (w, u) across the line y = x, the graph of S-1 is simply
the mirror image of the graph of S across this same line, y = x. Every
function, as a relation, has an inverse: however this inverse relation need
not be a function. If, for a function, the inverse relation f-1 is a function
then it is called the inverse function to f or simply the inverse of f . The
inverse function f-1 exists if and only if
that is, if f is one-to-one. The domain of f becomes the range of f-1, and
similarly rg f = dom f-1. These results are illustrated in Figure 3.15.
As a concrete example, if H : x H 22 then H-1 exists and
H-1
= ((2334;
x E R},
= {(y,Y/2>; Y E R l ,
= { (x,4 2 ) ;
x
E R}.
In other words, H-1 : x
x/2.
Viewing the inverse function as a set of (reversed) pairs is useful in
graphing. The graph of f-1 can be obtained in Maple by the command
note the presence of [ and I in the command! In Figure 3.16 the graph of
the inverse to x I-+ &x3
x was obtained in this way.
It follows from the definition of an inverse that f-1 (f(x)) = x for every
z E dom f and f (f-1 (y) ) = y for every y E dom f-1 = rg f . This can also
+
92
Introduction to Mathematics with Maple
Y
............
............
Fig. 3.15 Graph of a one-to-one function
be written in the form f-10 f = idA and f o f-1 = idB where A = domf
and B = r g f .
For finding the inverse of f we simply solve the equation
with respect to x. If there is a unique solution to (3.2)then it is f-l(y).
Since we know that it is irrelevant whether x or y or u is used in the notation
f-1 : y H f-l(y) we can, after (3.2)has been solved, write x instead of y
and y instead of x. This is illustrated in Figure 3.17.
Example 3.7
Let
G : x ~ l / x x~:{x;O<x<l}.
,
Find G-1. We have y =
1
1
1
-,
x = -, so G-1 : x H -.
X
Y
X
However G and G-1
93
Functions
-4t
,TT
Fig. 3.16 Inverse of x I-+ &x3
+ x.
are two distinct functions:
domG
= {x;0
< x < l},
while
domG-1
=
{x;x > l},
as can be readily checked. This should also be clear from the graphs of G and
G-1.
It may happen that a function f has an inverse but solving the equation
y = f(z)is difficult. The existence of f-1 is guaranteed if f is one-to-one,
and is independent of our ability to find an explicit solution of the equation
Y = f (4.
Introduction t o Mathematics with Maple
94
Y
0
Fig. 3.17 Graph of a one-to-one function and its inverse
+
We have seen in Exercise 3.5.1 that f : x H &x3 x is
one-to-one and has therefore an inverse. Its graph was obtained in Figure 3.16.
The equation
Example 3.8
-x1 3 + x = y
10
(3.3)
is not easy to solve4. The Muple command f solve can be used for numerical
solution of Equation (3.3) and therefore for approximate evaluation of the
inverse. The following Muple session illustrates this.
41n Chapter 8 we deal with solving equations generally and we also use Maple t o solve
equations like (3.3).
Functions
1
10
g := y --+ fsolve(-- x3
>
95
+ x - y = 0, x)
g(O>;g(l>;g(l.l>;g(-2);g(3);
0
.9216989942
1.
- 1.594562117
2.088730145
It is important to realize that g is only an approximation to f-1. This
happens because the command f solve gives only the numerical solution. For
instance, g(f (sqrt (%Pi))) ; gives 2.506628274 instead o f
6.
Exercises
Exercise 3.6.1
rg f and
f-1.
Exercise 3.6.2
Exercise 3.6.3
3.7
1
Let f : z H -. Prove that f is one-to-one and find
2-x
Use Maple to graph the inverse to f : x
Find and graph the inverse o f f : x
H
H
x/(l
x5/(l
+ x4).
+ 1x1).
Comments
In our approach we derived the concept of a function from that of a set.
It may be argued that the concept of a function is more fundamental than
that of a set and should be used as the most primitive notion. It is, indeed, possible to choose the idea of a function as the most basic for all
mathematics; such a procedure was conceived by the great mathematician
96
Introduction t o Mathematics with Maple
John von Neumann in the early 1920s. Naturally if we choose functions
as the foundation stone their theory has to be established on an axiomatic
basis. We cannot a priori exclude the possibility that mathematics built on
functions would then be distinct from the one based on sets. This does not
seem to be the case: set theory and the rest of mathematics as we know it
can be recaptured from von Neumann’s theory via characteristic functions,
that is, functions whose range is ( 0 , 1). The fact that functions can be
used instead of sets for the foundation of mathematics is interesting but
little known, and at our level of sophistication no mathematician would be
inclined to adopt it.
Chapter 4
Real Numbers
In this chapter we introduce real numbers on an axiomatic basis,
solve inequalities, introduce the absolute value and discuss the
least upper bound axiom. In the concluding section we outline an
alternative development of the real number system.
4.1
Fields
Real numbers satisfy three groups of axioms-field axioms, order axioms
and the least upper bound axiom. We discuss each group separately.
A set F together with two functions (x,y) x y, (x,y) xy from
F x F into F is called a field if the axioms in Table 4.1 are satisfied for all
x, y, z in F .
+
Table 4.1 Field axioms
~~
A1
Mi
:X + Y = Y + X
:X ~ = Z J X
A z : X+(Y+Z)=(Z+Y)+Z
M2 : x(~z)
= (XY)Z
A3 : There is an element 0 E F
M3 : There is an element 1 E F ,
1 # 0, such that lx = x for
such that 0 x = z for all
x in F
all x in F ;
A4 : For every element x E
M 4 : For every element x E F ,
x # 0, there exists an elF there exists an element
ement x-l E F such that
(-x) E F such that (-x)
x-lx = 1;
x = 0.
D : x(y z ) = xy xz.
+
+
+
+
97
98
Introduction to Mathematics with Maple
+
The functions (x, y) I-+ x y and (2, y) I-+ xy are called addition and
multiplication, respectively; x + y is the sum of x and y, xy is the product of
x and y. Axioms A1-A4 deal with addition, while axioms M1-M4 deal with
multiplication. Axioms A1 and M1 are called the commutative laws; axioms
A2 and M2 are called the associative laws; axioms A3 and M3 state the existence of a unit element under addition and multiplication, respectively,
which does not change x; axioms A4 and M4 state the existence of an inwerse element under addition and multiplication, respectively, to produce
the respective unit element. Note that the unit element under addition,
commonly called the zero element, does not have an inverse under multiplication. In more usual language, division by zero is forbidden. Axiom D
is called the distributive law. In Chapter 2 we defined a ring as a set with
addition and multiplication satisfying axioms A1-A4, MI, Ma and D. Hence
every field is a ring, and a ring in which axioms M3 and M4 are satisfied is
a field.
It follows easily that the element 0 from axiom A3 is uniquely determined. Indeed, let z be such that x = x x for every x. Setting x = 0
gives
+
o=z+o
=O+z
by axiom A l ,
-
by axiom AS.
--z
A similar argument shows that there is exactly one element 1 and that -x
and x - l are uniquely determined by x. We shall not dwell on such simple consequences of these axioms and hope that the 'usual rules' concerning addition and multiplication are known to the reader from elementary
-1
arithmetic or algebra (for example, -(-a) = a , a.0 = 0, (a-')
= a,
(-1)a = -a, a2 - b2 = ( a b)(a - b), etc.). Derivation of some of these
rules are to be found in the Exercises at the end of this section. Because
of axiom A2 we write a b c instead of either a ( b c) or ( a b) c;
similarly abc denotes either a(bc) or (ab)c.
Many other conventions are used, for example, a - b and a bc are
abbreviations for a (4
and
)a (bc), respectively. A centred dot is
sometimes used to indicate multiplication, as in a - b which means the same
a
as ab. The fraction - or a / b is simply ab-' and a b-' denotes a (b-').
b
We trust that the reader is familiar with these and similar conventions from
elementary algebra, including the rules for precedence of various operations.
In cases where there is the possibility of ambiguity in this book, parentheses
+
+ +
+
+ +
+ +
+
+
+
+
Real Numbers
99
will be inserted, or a centred dot used, to clarify the precise meaning.
There are many fields. If we accept reals as known then we may exhibit
the rationals also as a field. The structure of a field can be very different
from that of the reals or rationals. For example, there exist fields with
finitely many elements and these are important, not only theoretically but
also in application. Let F = { z , u>,
where z # u and addition and multiplication satisfy z z = z , u u = z , z u = u z = u,z z = z , uu = u,
uz = zu = z . It is very easy to check that F is a field, with z = 0, u = 1.
Note that there are no other elements in this field. The addition and multiplication tables for this field, which is known as G F ( 2 ) ,are shown in the
following table.
+
+
+
+
Table 4.2 Addition and multiplication in
Add
Mu1tiply
+ 0 1
0
0 1
1 1 0
0 1
+
It may be thought that any area of mathematics which contained 1 1 =
0 as a basic rule was of little use. However this field is, possibly, more
frequently used in the modern world than any other. Every time a computer
carries out a parity check G F ( 2 )is invoked. Axiom M3 guarantees that any
field must contain at least two elements, 0 and 1.
Exercises
In Exercises 4.1.1-4.1.3, a , b, c , d , z denote elements of a fixed field F .
These Exercises are stated as theorems: your task is to prove them.
+
@ Exercise 4.1.1
T h e equation a x = b is uniquely solvable for x. T h e
equation ax = b is uniquely solvable for x i f a # 0.
@ Exercise 4.1.2
(1) a . O = O - a = O ;
(2) -(-a) = a ;
(3) (a-l)-' = a;
(4) (-1)u = -u;
( 5 ) -U
(-b) = -(a
+
+ b);
Introduction to Mathematics with Maple
100
(-a)b = a(-b) = -(ab);
(-a)(-b) = ab;
a c
ace
--=bd
bd’
a c ad+bc
(9) - + - =
b d
bd ’
(6)
(7)
(8)
-*
(11) a-l
#o.
@ Exercise 4.1.3
(1) If ab = 0 then either a = 0 or b = 0;
(2) a + c = b + c + a = b ;
(3) ac = bc and c # 0 imply a = b.
@ Exercise 4.1.4
Let K be a set containing a t least two distinct elements. Assume that addition and multiplication on K satisfy axioms
Al, A2, MI, M2, D and the following:
A5 : For every a, b E K the
M5 : For every a , b E K , a # 0,
equation a i- x = b has a
the equation ax = b has a
solution.
solution.
Prove that K is a field.
This means that the axioms A3, A4 and M3, M4 can be replaced by A5 and
M5, respectively, and the definition of a field retains the same meaning.
[Hint: First solve the equation a x = a, denote it by z and show that
z b = b for every b E K . Show similarly that the solution to ax = a with
a # 0 is the multiplication unit. The existence of (-a) and a - l is obtained
by solving the equation x a = 0 or ax = 1 respectively. It is obvious that
A5 and M5 are satisfied in any field.]
+
+
+
4.2
Order axioms
A field F is ordered if the axioms in Table 4.3 are satisfied for all a, b, c in
F . If a < b we say that a is less (or smaller) than b; if b < a we may also
write a > b and say that a is greater than b. The sign of inequality points
towards the smaller element. The statement ‘a < b or a = b’ is recorded as
a 5 b. The inequality a < b is called strict inequality to distinguish it from
the inequality a 5 b. If 0 < a , then a is said to be positive, and negative if
a < 0. The inequalities a 5 b and b 5 c are shortened to a 5 b 5 c and a
similar convention is used if either or both of the inequalities is strict.
Some easy consequences of the axioms are stated in the next Theorem.
Real Numbers
101
Table 4.3 Order axioms
01 : There is a relation < on F such that exactly
one of the following possibilities occurs:
0 2
0 3
0 4
a<b; a=b; b<a.
: ( a < b) and (b < c ) 3 ( a < c ) .
: a < b + ( a c) < ( b c).
: If 0 < c then
a < b + ac < bc.
+
+
For the remainder of this section, a, b, c, d denote arbitrary elements in an
ordered field F , and x is an element of this field which is (usually) to be
found.
Theorem 4.1
(i) a < b + - b < - a ;
(ii) ( a < b, c < 0) +-bc < ac;
(iii) ( a < b, c < d ) + a + c < b d;
(iv) ( a < b, c < d ) + a - d < b - c;
(v) (0 < a < b, 0 < c < d ) + ac < bd;
(vi) a # ~ + - O < a ~ ;
1
(vii) 0 < a + 0 < -;
a
1 1
(viii) O < a < b + O < - < - .
b a
+
Since bc < ac means the same as ac > bc we have the following Memory
Aid for (ii): Multiplication of an inequality by a negative number reverses
the inequality. A consequence of (vi) is that 1 > 0.
Proof.
Each of the parts of the theorem is proved separately.
+
b) gives
(i) Using 0 3 with c = -(a
-b = u - ( a b) < b - ( a b) = -a.
(ii) If c < 0 then by (i) 0 < -c and it follows by
by (i) bc < ac.
(iii) Successive applications of 0 3 give
+
+
u+c<b+c
and
0 4
that -ac
b+c<b+d.
< -bc and
(4.1)
Introduction to Mathematics with Maple
102
+
+
(iv)
(v)
(vi)
(vii)
(viii)
+
Applying 0 2 to (4.1) (with a replaced by a c, b by b c and c by
b d) gives the result.
-d < -c by (i) and using (iii) with c replaced by -d and d by -c
gives the result.
We have successively ac < bc and bc < bd; an appeal to 0 4 and then
to 0 2 completes the proof.
If 0 < a then 0 = 0 - a < a - a = a2.
If a < 0 then 0 < -a and 0 = 0 - (-a) < (-a) (-a) = a2.
1
1
1
Since a - = 1it follows that - # 0 and by (vi) - > 0. Consequently
a
a2
la
1
1
0 = :0
< a: = -.
a"
a" la
1
11
1
By (vii) we have - > 0, - > 0. Consequently 0 < - - = - and
a
b
a b
ab
1
1 - 1
1
-=a< b - = -.
b
ab
ab a
-
Theorem 4.1 can advantageously be used to describe the set of x E F
satisfying a given inequality. Consider, for instance,
X
2-4
> 1.
(4.2)
Solving (4.2) means finding all x satisfying (4.2). This inequality only
makes sense if x # 4. For x # 4 we have (x - 4)2 > 0 and it follows that
(4.2) is satisfied if and only if
x2 - 4~ = X(X - 4) > (X - 4)2 = x2 - 82 + 1 6 ,
42
> 16,
x>4.
Thus x satisfies (4.2) if and only if x > 4. Maple can be used with advantage
to solve inequalities. We deal with this in Section 4.4.
Example 4.1
We wish t o solve
x2
+ 6 > 52.
(4.3)
We rewrite the inequality in an equivalent form
(x - 2)(x - 3) > 0.
We distinguish two cases:
(a) x > 2;
(4.4)
103
Real Numbers
(b) x < 2;
(for x = 2 the inequality is obviously false).
In case (a) we find by dividing by x - 2, that is, multiplying by -that
Inequality (4.4) is satisfied if and only if
x -3 > 0
2-2
and it is not satisfied for
2<x<3.
In case (b) we obtain similarly x < 3 , and since this provides no further restriction all x < 2 satisfy (4.4).
Summarising: Inequality (4.4) is satisfied by x if and only if either x < 2 or
x
> 3.
This result is obvious geometrically. Inequality (4.3) is satisfied if and
only if the point (x,y) on the parabola y = x2 - 5 x + 6 lies above the x-axis.
(See Figure 4.1.)
C
4
1
Y
L
0
X
Fig. 4.1 A graph of x2 - 5 2
+6
The set P of positive elements in F has the following properties:
(1) For every x E F, x # 0, either x or - x is in P.
(2) If x and y are in P then so is x y.
( 3 ) If x and y are in P then so is zy.
+
If, in a field F , a set P is given such that (l),(2) and ( 3 ) hold then this
104
Introduction to Mathematics with Maple
field becomes an ordered field by declaring a < b if b - a E P. The proof of
this is left as an exercise.
In an ordered field the sets
[a, b] = {x : a 5 2 5 b};
] a , b[ = {z : a < 2 < b};
[a, m[ = {x : a 5 x};
(A)
(A)
(B)
(B)
(C)
] a , 00[ = {x : a < 2);
1-00, a] = {x : x 5 a } ;
(D)
(E)
< a}.
(F)
3-00,
a [ = {x : x
are called intervals. The interval (A) is closed closed; the interval (B) is
open. It is not difficult to guess what the half-open (or half-closed) interval
[a, b[ is.
It should be emphasised that we have not introduced any new elements
into F ; 00 or --oo are used for convenience of notation and have no meaning
by themselves. This is emphasised by the use of m[ or 3-00 to indicate the
end of the interval, implying that there are no elements in the intervals
actually equal to these symbols.
It is also worth pointing out that other notations are used to indicate
open intervals. One common notation is ( a , b) = {x : a < x < b}. We
shall not use this, since we have used (a, b) to denote an ordered pair of
elements .
The length of [a, b] or ] a , b[ is b - a; a and b are called the end points
of the interval.
Exercises
In these exercises a, b, c, d , x denote elements of an ordered field.
@ Exercise 4.2.1 Prove the following:
(1) ( a 5 b, c 2 0 ) * ac 5 bc;
( 2 ) ( a < b, c < 0 ) * ac 2 bc;
(3) ( a < b , c < d ) * a - d < b - c ;
1 1
(4) O < a 5 b * O < - < - '
b - a'
( 5 ) 0 < a b +=a2 b2.
<
<
Exercise 4.2.2 Solve the following inequalities:
(1) 32 2 5 22 - 5 5 42 3;
+
+
Real Numbers
105
+ 6 5 x + 10 5 22 + 1;
1
(3) x + 5 2; [Hint: Consider z > 0 and z < 0 separately.]
(4) (z + l)(2x - 1) > 0;
(5) ( 5 -~2 ) ( 3 +
~ 1) 5 0;
(2) 22
J:
X + l <
(8) :+3
(9) x 2 x
x+5
x+6'
+ + 1 > 0.
[Hint: x2
@ Exercise 4.2.3
+ x + 1 = (x+ 1/2)2 + 3/4.]
Prove that 2ab 5 a2
a = b. [Hint: ( a - b)2 2 0.1
4.3
+ b2 with equality if and only if
Absolute value
By Max(a, b) we denote the larger of the two numbers a, b and, naturally,
if a = b we take Max(a, b) = a = b.
Definition 4.1 (Absolute value) The absolute value of a is
Max(a, -a). Absolute value of a is denoted by lal, that is, la1 =
Max(a, -a).
I
Theorem 4.2
(i)
(ii)
(iii)
(iv)
(v)
(vi)
la1 2 0;
a 5 lal;
-a 5 la[;
- a1 = lal;
a 2 0 la] = a;
a 5 O + la1 = -a;
I
*
All assertions (i)-(vi) are easy consequences of the definition. Rewriting
(iii) in the form -la1 5 a we can combine (ii) and (iii) into
106
Introduction to Mathematics with Maple
(i) Combining (4.5) with
gives
-(la1
+ PI) I a + b L1 . 1 + lbl .
This implies
a
-(a
That is, both numbers a
and hence
la
+ b I la1 + lbl ,
+ b) I la1 + lbl.
+ b and -(a + b) do not exceed la1 + lbl,
+ b( = Max(a + b, -(a + b ) ) 5 la1 + lbl .
(ii) Using (i) with a replaced by a - b gives
I4 I la - bl + PI
7
that is,
Interchangllig a and b in this inequality gives
lbl -1.1
5 J b - a1 = la - bJ .
Both numbers la1 - Ibl and -(la1 - lbl) do not exceed la - bl, consequently
114 - Ibll 5 .1
- bl -
(iii) The proof is simple when one distinguishes three cases:
(A) a 2 0, b 2 0;
(B) a < 0 , b < 0 ;
Real Numbers
107
(C) one number, say a, is not negative, a 2 0, and the other number,
b, is not positive, b 5 0.
In each of the cases (A)-(C) it is easy to verify (iii) of Theorem 4.3
and we leave it to the reader as an exercise.
0
The next Theorem will be often used, particularly in Chapter 10.
Theorem 4.4
inequality
If x , a , E are elements of an ordered field then the
.1
- UI
<€
(4.6)
is satisfied if and only if
a-€
< x < U S €.
(4.7)
Proof. We split the proof into two parts.
I. Let us assume Inequality (4.6). By Inequality (4.5) we have
It follows from Inequality (4.6) that
-€
< -Iz
- at.
(4.9)
Combining (4.6) and (4.9) with (4.8) gives
-€
< x - a < €,
which is equivalent to (4.7).
11. The Inequality (4.7) states that
x-a<e,
and
-(x
Both numbers x
lz - a1 < €.
- a and -(x
- a ) < E.
- a ) are less than
E,
and consequently
0
Resorting to geometrical illustrations of elements of F on the number
line, we can say la1 is the distance of a from zero, and so I
z - a1 is the
distance of z from a. Theorem 4.4 says that points x having distance from
a less than e constitute the interval ] u - 6, a E [ . (See Figure 4.2)
+
Introduction to Mathematics with Maple
108
Fig. 4.2 Intervals on the real line
Exercises
Exercise 4.3.1
Evaluate I(x - 1)(x - 2)/ for x = 0, 1, -5, 3, 4.
Solve the following inequalities (describe the set for
Exercise 4.3.2
which each of the inequalities holds):
(1) Iz - a1 2 6 ;
(2) Ix - 11 2 < 2;
(3) 0 < 12 11 < 4;
(4) 32 - 1 < 1x1 < 32 1;
(5) 1x1 I Ix - 21 1;
(6) 1x1 12 - SI < 2;
- 82 10.51 < 2.5.
(7)
+
+
+
+
+
+
Using Maple for solving inequalities
4.4
Maple can save a lot of mechanical work when solving inequalities. The
form of the command is
solve(inequality, unknown) ;
The parameter unknown is an option which can be left out if only one
variable occurs in the inequality. For instance
~
>
~
~
~~
solve ( (x+l) / (x+3)< ( x + 5 )/ ( x + 6 ) 1;
RealRange( Open( -9), Open( -6)), RealRange(Open( -3),
00)
This answer means that the inequality is satisfied for all J: in the intervals
] - 9, -6[ and ] - 3, 00[. Another example is
Real Numbers
>
109
solve(abs(2*x~2-16*x+21)~5);
RealRange(Open(4 - 2 h),
Open(4 - d)),
RealRange(Open(4
+ h),Open(4 + 2 h)).
The solution set consists again of two intervals. In order to obtain a
better idea of what these intervals are we repeat the question, but enter
the numbers as floating point numbers.
>
solve (abs (xn2-8*x+10.5) (5.0) ;
RealRange(Open( .7596296508), Open(3.292893219)),
RealRange( Open(4.707106781), Open(7.240370349)).
If we use the option unknown, enclosing the variable in braces, this tells
Maple to provide the solution as a set of inequalities. We recommend that
this option is always used when solving inequalities.
>
solve(abs(x~2-8*x+10.5)~5.0,(~)) ;
{.7596296508 < x, x
< 3.292893219},
{x < 7.240370349,4.707106781 < x}.
Most of our readers would find solving the next inequality difficult but
Maple has no problems. The sign ‘less or equal to’ is entered into Maple as
<=. Similarly the sign ‘greater than or equal to’ is entered as >=. The order
in which < and = appear is important! We do not expect a neat result and
therefore enter the numbers as floating point numbers.
>
s o l v e (xA3+x>=i.0, (x)
;
{.6823278038 5 x}
Solving inequalities involving polynomials of degree three might involve
Introduction t o Mathematics with Maple
110
solving an algebraic equation of degree three; this topic is dealt with in
Chapter 8. So far Maple has provided complete answers. The next example
shows that on occasions a small amount of additional work is needed.
>
>
>
#
>
solve (b*x+3<2*b,{XI) ;
We a r e going to solve an inequality which involves a
# parameter, so the option ‘unknownJ is now important.
# It is advisable to enclose the unknown in braces.
{signum(b) x
<
[email protected])(-3
b
+ 2 b)
1
From this it is easy to read the result: if b is positive the inequality is
satisfied for x < (-3+2b)/b, if b is negative it is satisfied for x > (-3+2b)/b.
(For b = 0 it does not make sense to solve the inequality with respect to
x.) In this particular case solving with paper and pencil is simpler.
A system of inequalities like a < b < c must be entered in the unabbreviated form as a < b and b < c. If there are several inequalities to solve they
should be entered as a set. The following example illustrates the result from
Maple when we solve the system of inequalities 1 < x 2 - 2x - 2 < 32 - 6.
> ineqs :=(1<xA2-2*x-2, xA2-2*x-2<3*x-6}:
>
solve(ineqs, {x}) ;
{x < 4 , 3 < x }
These results mean that the system 1 < x 2 - 2x - 2 < 3 2 - 6 is satisfied
if and only if 3 < x < 4. This is illustrated in Figure 4.3 below: the solution
set is the set of x where the parabola lies above the line y = 1 and below
the line y = 3x - 6 . It is important to understand Maple’s answer like
{ x < -1). This denotes the set with one element, namely the inequality
x < -1. The set { x < -1) should not be confused with { x : x < -l}, the
set of real x less than -1.
>
plot({l,~~2-2*~-2,3*~-6},~=-6..6,y=-6..6);
~~~
~
~
A mathematician cannot solve every and all problems, and neither
can Maple. It is, however, surprising that Maple has difficulties with
Real Numbers
111
Fig. 4.3 Plot of 1, x2 - 22 - 2, 3 2 - 6
solving some very simple problems, for example, x2 > a.' In this particular instance this is caused by the presence of the parameter a.
>
solve (xA2>a,{x}) ;
This is an incomprehensible result but if we help Maple by making an
assumption on a we get the correct answer:
lThis was true at the time of writing this book. Later versions of Maple may be more
powerful.
Introduction to Mathematics with Maple
112
>
solve (xn2>a,{x)) assuming a>O;
{-x < -&}, {x < - 6 1
Unfortunately the command assuming does not help in the next example.
>
solve(xn2>a,{x}) assuming a<O;
{-Ix < J-a},{Ix < G}
The correct answer is that the inequality is always satisfied. At this
stage we recommend to our readers not to use Maple for solving inequalities
which involve some parameter (like a in our case). However the command
assuming is worth remembering. Maple knows also assume, the difference
between assuming and assume is that the former applies only to the command which it follows. The latter should be used before the command to
which it applies and then it remains valid for the rest of the worksheet.
Exercises
Exercise 4.4.1
Use Maple to solve Exercises 4.2.2 and 4.3.2.
Exercise 4.4.2
Use Maple to find the unions and the intersections of
4.5
Inductive sets
In this section F denotes an ordered field. A set S
inductive if
(i) 1 E S ,
(ii) x E S + (x
cF
is said to be
+ 1) E S.
Obviously F itself is inductive. The intersection of all inductive sets in F
is denoted by N. We may describe this by saying that N is the smallest
inductive set. Since 1 E N and 1 1 = 2 E N by (ii), and then again by
(ii) 1 2 = 3 E N it is intuitively clear that N must contain all natural
numbers. However, from a strictly logical view natural numbers are defined
+
+
Real Numbers
113
as elements of N (in this book). We restate that N is the smallest inductive
set as
Theorem 4.5 (Principle of Mathematical Induction)
a subset of N with the following properties
(i) 1 E M , and
(ii) n E A4 + (n
+ 1) E M
I f M is
for every n E M ,
then M = N.
Although Theorem 4.5 is almost a trivial consequence of the definition
of
N it can be useful. As an example of its application we prove
I/
Theorem 4.6
Proof.
1 5 n for every n E
We define M = {x : x E
/I
N.
N, 1 5 x}. Obviously
1 E M and if
+ 1 5 n + 1. Since 1 < 1+ 1 we have 15 n + 1,
n E M then 1 5 n and 1
+
that is, n 1 E M . M is inductive and Theorem 4.5 gives M = N. This,
0
in turn, means that 15 n for every n E N.
Theorem 4.7
I f n E N , mE N and m < n then n - m 2 1.
In other words, there is no element of N between m and m
+ 1.
Proof. We define S = {m; m E N,m < n implies n - m 2 1for n E N}.
It suffices to show that S = N. We do this by showing that S is inductive.
First we prove that 1 E S. For that we consider M = (1) U { n ; n E
N, n - 1 2 1). It is easily verified that M is inductive; by Theorem 4.5
then M = N, that is, 1 E S . Now let m E S and n E N, m 1 < n. This
means that n > 1a n d m < n-1; sincern E S i t follows that n-1-m 2 1.
0
This last inequality shows that m + 1 E S.
+
We are now in a position to start building the theory of natural numbers;
however we shall not pursue this, but simply leave some indications in
the exercises how this theory can be established in our framework. We
proved Theorems 4.5 and 4.6 because they are very important for some
fundamental results of the next section (especially Theorem 4.10: The Well
Ordering Principle).
The German mathematician L. Kronecker once remarked “God made
the natural numbers, everything else is the work of man”. This reflects two
things. Firstly, the natural numbers are fundamental; we cannot explain
Introduction to Mathematics with Maple
114
what they are. Secondly, after accepting natural numbers (for example,
on an axiomatic basis), the negative integers, the rationals and the real
numbers themselves can be defined and their respective theories built on
the foundation of the theory of natural numbers. The axiomatic theory of
natural numbers was established by the Italian mathematician G . Peano
and the development indicated above is presented in Landau (1951) and
Youse (1972). In this book we have chosen a different starting point, namely
the axiomatic system for the reals, created for us by previous generations of
mathematicians. However, we briefly outline the Peano approach in Section
4.8.
Having defined N we can define Z and Q as
Z=NU{O}U{z; -xEN},
Q = {z;
Recall that we also set
z = ab-l,
a
E
z,b E N}.
No = N U (0) and P = {z; J: > 0 ) .
Exercises
Exercise 4.5.1
Prove: k E N and m E N imply k
[Hint: Use mathematical induction.]
+ m E N.
Exercise 4.5.2
Prove: k E N and m E N imply k m E N.
[Hint: Use mathematical induction.]
Exercise 4.5.3
Prove: k E Z and m E Zimply k
Exercise 4.5.4
Prove: n E N + n 5 n2.
[Hint: Use Theorem 4.6.1
4.6
The least upper bound axiom
We start with a definition.
+ m E Z and k m E Z.
Real Numbers
115
2
&finition 4.2 (Bounded sets in ordered fields) A set S in
ordered field F is said to be bounded above if there exists K E F such
that
for all x E S. S is said to be bounded below if there exists k such that
for all II: E S . S is said to be bounded if it is bounded above and
bounded below. The element K is called an upper bound for S and k
is called a lower bound for S .
Clearly, set S is bounded if and only if there exists K1 such that
for all x E S . The ‘if’ part is obvious with K = K1 and k = -K1. For the
‘only if’ part choose K1 = Max( IK I Ikl).
Example 4.2
+ +
n2 5n 3 n c N } .
n2+n+I’
Let S =
M is bounded above since
n2+5n+3
n2+n+I
<
n2+5n+3
51+5+3=9.
n2
Thus 9 is a n upper bound. Is there a smaller upper bound? It is easy to see
that the answer is ‘yes’. Taking into account that n 5 n2 we obtain
The natural question now is: Is there an even smaller upper bound? This time
the answer is ‘no’, because for n = 1 we have
n2 + 5n
+
n2+n+I
= 3. The number
3 is the smallest or, in more usual words, the least upper bound.
If we illustrate the elements of a set S as points on a vertical number line
then we can imagine an upper bound K for S as a kind of obstruction which
the elements of S cannot pass; they stay below K . Geometric intuition leads
us to believe that K can be moved to its lowest position, thus becoming
the least upper bound.
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Introduction to Mathematics with Maple
Definition 4.3 (Least upper bound) An element s E F is said to
be the least upper bound for S c F if
(i) x 5 s for every x E S;
(ii) if t < s then there exists a E S such that t < u(< s ) .
Property (i) states that s is an upper bound, while (ii) means that any
number strictly less than s is not an upper bound, that is, s is the smallest
possible upper bound. A set S can have at most one least upper bound; if
there were two, say s1 and s2, with s1 < s2 then by (ii) there will be u E S
such that s1 < u(< s2). This last inequality contradicts our assumption
that s1 is an upper bound for S.
The Latin word supremum is used with the same meaning as least upper
bound. The symbol s u p s denotes the least upper bound (supremum) of a
set S. The phrase ‘least upper bound’ is sometimes abbreviated ‘lub’.
Definition 4.4 (Complete field) An ordered field is said to be
plete if every non-empty set bounded above has a least upper bound.
Definition 4.5 (Real numbers as a complete ordered field)
The real number system is a complete ordered field.
It can be proved that there is essentially only one complete
Remark 4.1
ordered field. Spivak (1967) contains a precise statement of this result as well
as the proof on page 507. However, we need not worry unduly now how many
complete ordered fields there are, because whatever we prove will be valid in
all of them. As agreed previously the set of all reals is denoted by R.
We reformulate Definition 4.5 as
The Least Upper Bound Axiom Every non-empty set S
is bounded above has a least upper bound.
c IR which
The number 3 was the least upper bound for S in Example 4.2, because
it was the largest element in S. Whenever a set M has a largest element
then this element is the least upper bound. Also if, for a set S , the least
upper bound of S belongs to S then it is the largest element of S. However
the least upper bound of S need not belong to S: for the set S = {x; x E
R, x < 0) we obviously have s u p s = 0 and 0 4 S.
Theorem 4.8
that x < n.
For any x E R there always exists n E W such
Real Numbers
117
Assume, contrary to what we want to prove, that there is K E R
such that n 5 K for every n E N. Then N has a supremum s. Choose
t = s - 1; then there exists k E N such that s - 1 < k It follows that
s<k
1 E N,and consequently s is not an upper bound for N, which is a
contradict ion.
0
Proof.
+
Theorem 4.8 can be rephrased by saying that N as a subset of R is not
bounded. This is often referred to as the Archimedean property. There are
ordered fields which do not have the Archimedean property. Such fields
obviously cannot be complete. An example of an ordered field which does
not have the Archimedean property is given in Exercise 6.2.10.
Definition 4.6 (Greatest lower bound) An element m E F is
said to be the greatest lower bound for S c F if
(i) m 5 x for every x E S;
(ii) if m < t then there exists a E S such that m 5 a < t.
The Latin word infimum is used with the same meaning as greatest lower
bound. The symbol inf S denotes the greatest lower bound (infimum) of a
set S. The phrase ‘greatest lower bound’ is sometimes abbreviated ‘glb’.
A set S c R can have at most one infimum; the proof is very similar to
the above given proof of uniqueness of the least upper bound.
Theorem 4.9
Every non-empty set S C R bounded below has a
greatest lower bound.
Proof. Define M = {x : -x E S}. It is not difficult to see that M
is non-empty and bounded above. By the least upper bound axiom there
exists s u p M . It is now easy to check that - s u p M is the greatest lower
bound for S.
0
Theorem 4.10 (The Well Ordering Principle)
empty set M c N has a smallest element.
Proof.
Every non-
A4 is bounded below and non-empty, therefore it has a greatest
lower bound i. By the second property of the greatest lower bound there
is a k E M such that i 5 k < i 1. We now prove indirectly that Ic is
the smallest element of M . If it were not, there would be n E M such that
n < Ic. Hence we would have i 5 n < k < i 1 and consequently Ic - n < 1,
contradicting Theorem 4.7.
0
+
+
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Introduction to Mathematics with Maple
Theorem 4.11
For every x E R there exists a unique n E Z such
that n 5 x < n+ 1. This number is called the floor of x,and is denoted
by 1x1.
We obviously have
= I,
151 [-:I
= 0,
= -1,
101 = 0 , etc.
+
Proof. The number n (if it exists) is unique, for if n 5 x < n 1 and
5 x < f i + 1, with n < f i , we would have f i - n 5 x - n < 1. On the other
hand, fi - n E N , so we have a contradiction with Theorem 4.7.
If x E Z there is nothing more to be proved, so we assume x 4 Z. The
set of natural numbers strictly greater than 1x1 has the smallest member k ;
if x > 0 we take n = k - 1, if x < 0 we take n = - k .
0
fi
1x1 is also called the integer part of x, it is denoted by f l o o r ( x ) in
Maple. The function x H 1x1 is called the greatest integer function.
Remark 4.2
Similarly as in Theorem 4.11 it is possible to prove that for
every x E R there is a unique integer n such that n - 1 < x 5 n. This number
is called the ceiling of x, denoted in Muple by ceil (x) . The mathematical
symbol is 1x1. For x E Z we have 1x1 =
otherwise 1x1 1 = 1x1.
1.1,
+
I f a E R, b E R and a < b there exists r E Q such
Theorem 4.12
that a < r < b.
/I
The content of Theorem 4.12 is sometimes described by saying the set of
rationals is dense. If we try to illustrate graphically the set Q on the number
line we would not be able to distinguish between Q and the number line R
itself, because the points for Q are so densely packed.
2
b-a'
1
b-a
m
a+b
that is, - < -. Define m = ' k ] and we have - < - <
k
2
k 2
a+b
b-a
m+l
1m
m+l
a + b < b.
. It follows that a = -- -< -- - - - < 2
2
k
k
k
2
k
m
We have that r = - E Q and a < r < b.
0
Proof.
Since N is not bounded above there is k E N such that k >
-*
k
Actually we have proved that there are infinitely m a n y rationals lying
between any two reals, since we can take for k any of the infinite number
of integers greater than 2 / ( b - a ) .
Real Numbers
119
Exercises
Exercise 4.6.1
Find sup S and inf S if
n
(2) s = 15) u {3)u10,
(3) s = (-5) u {3)u]4, 5[;
(4) S = {x; x E R, x2 52 < 6.
+
n2 - 5
nEN}
n4 - 3n2 - 5’
is bounded from above and from below, and find sup S and inf S.
Exercise 4.6.2
Show that the set S =
Exercise 4.6.3
If S1 and S2 are bounded then so is S1U S2.Prove this.
@ Exercise 4.6.4
If S1 c S 2 c R, S1 # 8, 5’2 bounded, then sups1 5
sup S 2 and inf S1 2 inf S 2 . Prove this. Give an example of an S1 which is
a proper subset of S 2 but for which sup S1 = sup 5’2.
@ Exercise 4.6.5 If X and Y are bounded non-empty subsets of R and
for every x E X and every y E Y one has x 5 y then sup X 5 inf Y .
If, however, for every x E X there exists a y E Y such that x 5 y then
sup X 5 sup Y . Prove this, and also show that in the second case sup X
could be greater than inf Y .
@ Exercise 4.6.6
Prove that if a
< b there exists an irrational x such that
a<x<b.
[Hint. Use the same idea as in the proof of Theorem 4.12 and apply the
result of Exercise 2.4.4.1
Exercise 4.6.7
Graph the following functions, preferably without the
use of Maple.
(1) X H 1x1;
(2) X H X - 1x1;
(3) x
(4) x
(x - lxJ)2;
1x1 + 1-4;
Introduction to Mathematics with Maple
120
+
[Hint: For (1)-(3) consider separately the intervals [n, n 1[ with n E N.
The other parts of the exercise should be treated similarly. For instance in
1
= n if n 5 - < n 1. Even if you are using Maple
part (6),we have
I:]
X
+
look at the intervals separately.]
Exercise 4.6.8
The function x H Min(x - LxJ, LxJ + 1 - x) is called
the distance from x to the nearest integer, and denoted by saw. Graph the
following functions:
(1) x I+ saw(x);
(2) x I--+ saw(2x);
Exercise 4.6.9 Graph he hump function h, h(x) = Iz
with Maple and also without it.
4.7
+ 11+ 1x1+ 111:
-1
1
Operation with real valued functions
In this section we assume that the ranges of all functions appearing are
subsets of R. It is customary to define the sum, difference, product and
quotient of functions as follows.
f
+g :x
f -g :x
fg : x
f :x
9
f(x)
+ g(x); x E domf n domg,
f(x) - g(x); x E d o m f n d o m g ,
f(x)g(x); x E domf n domg,
f(x) x E domf n domg n {x; g(x) # 0),
9(4’
H -‘
(4.10)
Instead of ff we shall write f 2 . For example, id2 : x H x2. We shall
write f 5 g (or f < 9) to mean f ( x ) 5 g(x) (or f ( x ) < g(x)) for all
x E domf n domg. If f < g and domf = domg then the graph of f lies
entirely below that of g.
The definitions (4.10) make sense if the ranges of f and g are part
of some field, not necessarily of R. If, during a discourse, there is some
underlying field, definitions (4.10) are assumed to hold automatically.
Real Numbers
121
Exercises
4.8
Supplement. Peano axioms. Dedekind cuts
The axioms listed earlier in this Chapter, the axioms for a field given in
Table 4.1, the order axioms listed in Table 4.3 and the least Upper Bound
axiom treated in Section 4.6 are not the only possible system of axioms for
the reals. We have already seen in Exercise 4.1.4 that the field axioms can
be formulated differently. However the difference between the two sets of
axioms there was minor. Different approaches to the real number system are
possible. The one we shall consider in this section is, from a philosophical
point of view, diametrically opposite to ours: instead of considering the
natural numbers as a subset of the reals it starts with the natural numbers
and builds up to the reals. Perhaps, from a purely logical viewpoint, this
approach may be preferable. We did not employ it because it is long and
laborious. It starts with what are called the Peano axioms for the natural
numbers. We briefly indicate this approach leaving many proofs aside.
There are five Peano's axioms.
Peano 1: There is a nonempty set with a distinguished element 1.
The set will be henceforth denoted N.
Peano 2: For each x E N there exists one and only one element 2'.
The element x' will be from now on called the successor of x: after
each element of N there is one which immediately follows it. This
axiom describes in abstract the idea of counting: 2 comes after 1, 3
comes after 2, etc., n 1 comes after n, the successor x' comes after
+
X.
x' # 1 f o r every x E N.
In words, 1 is not the successor of any element in N.
Peano 4: If x' = y' then x = y.
This means: there is at most one x for a given x'. In other words
the mapping x H x' is one-to-one.
Peano 3:
Introduction to Mathematics with Maple
122
Peano 5: If M is a subset of N with the following properties
(i) 1 E M , and
(ii) n E M + (n
+ 1) E M
for every n E M ,
then M = N.
We immediately recognize this axiom as Mathematical Induction: in
our approach this is Theorem 4.5.
Note that in our development Mathematical Induction was derived as a
theorem, while here it is one of the fundamental axioms. With these five
axioms at hand building up the theory of natural numbers proceeds by
defining addition, multiplication and order in N and establishing the ‘usual’
properties of natural numbers. For instance, additionof x and y is defined
as follows:
To every pair of natural numbers x, y there exists a uniquely determined
natural number, henceforth denoted x y such that
+
(i) x
(ii) x
+ 1 = x‘ for every x E N;
+ y‘ = (x+ y)’ for every x, y E N.
This, of course, as well as the definitions of multiplication and order,
require proof, which we omit but mention that axiom Peano 5 is essential
for the proof.
Then the theory moves to extend N to Z,in other words introducing 0
and the negative integers. We skip over this, noting in passing that it can
be shown that the elements of Z thus defined satisfy the axioms for a ring
(see Table 2.3), but pause at the next step, the creation of Q. We denote
P
pairs of elements of Z as p / q or - rather than ( p , Q), though what follows
9
here can be written in terms of ordered pairs if desired. Two such pairs $/i
and p / q with i # 0 and q # 0 are declared equivalent if
$9 = P i
(4.11)
This relation is indeed an equivalence relation according to Section 3.1, that
is, it is symmetric, reflexive and transitive. As is customary we identify
equivalent pairs: two equivalent pairs are simply equal. The set of pairs
p / q with p E Z and 0 # q E Z for which addition and multiplication is
defined below is denoted by Q.The definitions are
Real Numbers
multiplication
T
q s
123
=E.
qs
These definition are meaningful. Firstly with q # 0 and s # 0 we
have qs # 0. Secondly, the definitions are independent of the choice of
p r
6 -r and p- -r = f-.i -r
representation of the pairs. If p / q = $14 then - - =
q s
q s
q s
4s'
To prove this, we first note
+
+
It follows that
From symmetry arguments we have also
if rg = ?s. This shows the correctness of the definition of addition. The
proof for multiplication is similar and left to the readers. It is obvious that
addition and multiplication of elements in Q is commutative and there is
no difficulty showing that all the other axioms from the Table 4.1 are also
satisfied. As a sample of such proofs let us prove axiom D.
p ru
+ ts
+
pru pts - pru
--+qsu
qsu
pts = pr
qsu
qs
pt
+ -.qu
At various places in the above proofs use is made of the equivalence relation among pairs of rational numbers expressed in Equation (4.11). The
construction of the field Q from the ring Z can be applied more generally:
instead of starting with Z one can take a ring M, proceed in the same
manner and the resulting field is called the quotient field of M. There is an
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Introduction to Mathematics with Maple
additional requirement on M for this construction to work, namely ab = 0
must imply that either a or b is 0.2
After the field axioms are shown to hold in Q derived in this fashion,
it is time to introduce order in Q. This is done by defining the set P of
positive element^:^ p / q with p , q E Z and q # 0 is defined to be positive if
p q E N. The relation a is less than b is then defined by declaring a < b if
and only if ( b - a ) E P. It is not difficult to prove that this relation satisfies
the order axioms in Table 4.3.4 We have now almost reached our goal:
we have in Q an ordered field, and it remains to extend it to a complete
ordered field. This can be accomplished by what are known as Dedekind
cuts, which later will be renamed real numbers. A pair of subsets A , A of
Q is called a (Dedekind) cut if the following conditions are satisfied:
C1 Both sets are nonempty and Q = A u A.
C2 If a E A and ii E A then a < ii.
C3 If a E A then there exists a’ E A satisfying a
< a‘.
Such a cut is denoted by AIA, the symbol I denoting the ‘cut’ in Q between
A and A. The set A is the called the lower section of the cut, and A the
upper section. Property C3 simply means that A does not have a largest
element: A may or may not have a smallest element. For cuts an order
relation, using the symbol 4,is defined as follows:
Order
AIA 4 BIB if and only if B n /I # 0. This is fairly intuitive: BIB is bigger
than AIA if B reaches over A into A. Interpreting this may be easier if the
reader makes a sketch. This order has the trichotomy property, Axiom 01
in Table 4.3: for two cuts A ) Aand BIB, one and only one of the following
is true:5
AIA 4 BIB,
AIA = BIB,
BIB
4
AIA.
If B n A # 0 then the first order relation holds. If B n A = 0 then A c B.
If A = B then we have equality, otherwise B nA # 0 and then BIB 4 AIA.
In our development the existence of the least upper bound was guaranteed by an axiom: here it becomes a provable theorem. We now prove the
least upper bound theorem for cuts.
2This is, of course, satisfied in Z.
3See page 103.
*With F replaced by Q in 01.
5AlA = BIB means naturally that A = B and
A =B
Real Numbers
125
Theorem 4.13 (The Least Upper Bound Theorem for cuts)
I f 6 is a non-empty set of cuts and ~ ( is
f such
i that it exceeds every
cut in G, that is X l X 4 MIA? for every X l X E G, then there exists a
cut s ~ Ssuch that
(i) if X l X E G and X l X # SlS then X l X 4 SlS,
(ii) if TIT 4 SlS then there exists X l X E G such that TIT
4
XlX.
Proof. To make the proof easily readable we divide it into several easy
steps.
Step I . Definition of SlS. The set S consists of all rationals which
belong to a lower section of a cut in 6. The complement of S , that is Q\ S ,
is S.
The next three steps prove that SlS is a cut.
Step 2. Condition C1. It suffices to show that @ n S = 0. If this were
not true then
n X O # 8 for some Xo(X0 E G but then [email protected] 4 XolXo
contradicting the assumption that X ( X 4 [email protected] for every X ( X E 6.
Step 3. Condition C2. If s E S and S E S then s E X I for some
X l l X l E 6 and 3 is in no X for X l X E G. Consequently 5 E X1 and
therefore s < 3.
Step 4. Condition C3. If s E S then s E X2 for some X2lXz E G and
there is some s" E X 2 c S with s < s".
Step 5. Proof of (i). Let X l X E G and X l X # S ) S . Assume contrary
to what we want to prove that SlS 4 X l X . Then, by the definition of
order in G ,we have X n S # 8 and this is impossible, since X C S.
Step 6. Proof of (ii). By assumption S n T # 8 which means there is
an X l X E 6 with X n # 0. By the definition of order in 6 this means
a
TIT 4 X ( X .
0
We now define addition and multiplication for cuts.
Addit ion
Let Y = { a + b; a E A , b E B } and
= Q\Y. Then
YIP is a cut'
and we
define
YIP Ef AIA + BIB.
We denote by 6 the cut 212with 2 = {z;xE Q,z < 0) and 2 = Q \ 2.
The cut 0 is the zero element for addition: for every cut AlA we have
'This, of course, must be proved, but we omit the proof as we did with many other
proofs in this section.
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Introduction to Mathematics with Maple
AIA+0 = AIA. We say that a cut AJAis positive if 6 4 AIA, which simply
means that all elements of A are positive. The definition of addition of cuts
was simple enough, but unfortunately multiplication is not as easy. It is
defined in stages, first for positive cuts and then generally.
Multiplication: positive cuts
Let AIA and BIB be positive cuts. Let X be the set consisting of negative
rationals, zero and all numbers of the form ab with a E A , b E B and both
numbers a, b positive. Define X = Q \ X . Then X l X is a cut7, and we
define multiplication by
Clearly the restriction to positive cuts was necessary, otherwise the definition of X l X as a cut would not be meaningful.
Multiplication: general cuts
If z,y are cuts then xy is defined by
xy = 0
zy = -((-x)y)
xy = -(z(-y))
xy = (-x)(-y)
i f x = O or y=O;
if x 4 6 and 0 4 y;
if 0 4 z and y 4 0;
if x 4 6 and y 4 0.
With these definitions, it is possible to prove that the cuts form a complete
ordered field, that is they satisfy Axiomss 0 2 - 0 4 (see Table 4.3) and all the
field axioms (see Table 4.1). This is not very difficult but could be tedious:
it also involves defining the cut -x (used in the definitions of general cuts)
and the unit cut for multiplication, i.
When is a cut a rational number?
If X contains a smallest element x* then the mapping X l X I-+ x* is clearly
one-to-one and onto Q,so it is a bijection. Moreover, it can be shown that
X l X + YIY I-+ x* + y*,
X l X - YIY H x* - y*,
X ( X 4 YIY H x* < y*.
(4.12)
(4.13)
(4.14)
Relations (4.12) to (4.14) show that the mapping X l X H x* preserves
addition, multiplication and order. Two fields F and Fl are said to be
7Again, this needs to be proved
We have already established Axiom 0 1
Real Numbers
127
isomorphic if there exists a bijection of F onto Fl which preserves addition
and multiplication. Two ordered fields are isomorphic if there is a bijection
of one onto the other which preserves addition, multiplication and order.
The bijection is called an isomorphism. Frequently in mathematics isomorphic structures are identified. This is natural: in two isomorphic structures,
elements act the same way in both structures. They may be different in
appearance, but not in substance. In accordance with this, we say that
those cuts for which the upper section contains a smallest element are the
rationals.
Cuts and real numbers
There remains the case where, for the cut X l X , the set X does not contain
a smallest element. We then use this cut to define a real number (in fact,
an irrational real number). Our outline of an alternative route to reals
has come to an end. We have constructed a complete, ordered field which
contains the rationals as an ordered subfield, so we now ‘have’ the reals.
Summary of Peano’s axioms
It is not possible to establish the consistency of Peano’s axioms but, as
Godel showed, neither is it possible to establish the consistency of the axioms of set theory. However, the construction of the reals by cuts shows
the relative consistency of the axioms for the reals. If Peano’s axioms are
consistent then so are the axioms for reals. From a philosophical view this
is the main difference between the two approaches. From a mathematical
point of view they are equally valid.
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Chapter 5
Mat hernat ical Induction
In this chapter we study proof by induction and prove some important inequalities, particularly the arithmetic-geometric mean
inequality. In order to employ induction for defining new objects
we prove the so called recursion theorems. Basic properties of powers with rational exponents are also established in this chapter.
5.1
Inductive reasoning
The process of deriving general conclusions from particular facts is called
induction. It is often used in the natural sciences. For example, an ornithologist watches birds of a certain species and then draws conclusions about
the behaviour of all members of that species. General laws of motion were
discovered from the motion of planets in the solar system. The following
example shows that we encounter inductive reasoning also in mathematics.
Example 5.1
Let us consider the numbers n5 - n for the first few natural
numbers:
n
n5-n
1
0
30
2
3
4
5
6
7
240
1020
3120
7770
16800
It seems likely that for every n E N the number n5 - n is a multiple of 10.
129
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Introduction to Mathematics with Maple
The reasoning in the above example does not give us the feeling of castiron certainty which mathematical arguments usually have. It may not be
true for n = 8, though you can easily check that it is. Even if you have
used a computer to check the first billion natural numbers, that does not
prove that it is true for all natural numbers. Indeed, basing arguments on
a finite number of examples is an uncertain procedure, and it can lead to
serious mistakes, as we shall shortly see in Example 5.2.
In everyday life, and in the natural sciences, our conclusions are subject
to further observations and experiments (devised to check the conclusions).
In mathematics this additional check is missing, and there is yet another
important difference. In Example 5.1 we observed a few particular cases,
but we made conclusions about the validity of the formula for infinitely
many cases.
Example 5.2
Consider the inequality
5n+$
1
1 >1+5n 55
(5.1)
*
If we substitute for n quite a few of the early natural numbers we see t h a t the
inequality holds. However it would be wrong to conclude that (5.1) holds for
all natural numbers n. For natural numbers n the (rational) number 5 n - !j is
positive, and therefore Inequality (5.1) holds if and only if
5 n + L2 > ( 5 n - i )
(It;),
and this inequality holds if and only if
-2 + -2 (1 + 5:)
1
>-n.
54
The last inequality holds if and only if n < 625.1. We summarise this as:
Inequality (5.1) holds for the natural number n if and only if 1 5 n 5 625.
Inequality (5.1) represents a statement valid for the first 625 natural numbers
but not for all natural numbers.
If we considered the inequality
instead of (5.1) we could spend all our life (or even all the life of the universe)
testing successive particular cases for n and we would never discover that
Mathematical Induction
131
there are natural numbers n for which (5.2) is not true.
Something more than testing the first few (or the first few million)
natural numbers is need to prove that n5- n is a multiple of 10. The key is
Theorem 4.5, the Principle of Mathematical Induction. Let M = (n; n E
N,n5 - n is a multiple of 10 }. Clearly 1 E M . Now we want to show that
if n5 - n is a multiple of 10, then so is ( n 1)5- (n 1). By the Principle
of Mathematical Induction it will follow then that M = N, that is, n5 - n
is a multiple of 10 for every n E N. By expanding (n 1)5we have
+
+
+
(n+ 1)5 - (n + 1) = n5 - n + 10(n3 + n 2 ) + 5 n ( n 3 + 1).
We are assuming n5 - n is a multiple of 10, and 10(n3 + n2) is obviously a
multiple of 10, it suffices to show that 5 n ( n 3 + n) is a multiple of 10. One
of the numbers n and n3 + 1 must be even, and so 5 n ( n 3 + 1) is indeed a
multiple of 10.
We can now summarise: A proof by mathematical induction has two
important parts:
(I) A check that the proposition is valid for the natural number 1.
( 2 ) A proof that if the proposition holds for any natural number, then it
also holds for the next natural number.
+
The second step is often called inference from n to n 1. The assumption
made in the second part, namely that the statement holds for some natural
number is often referred to as the induction hypothesis.
We have seen in Example 5.2 that the first part by itself is insufficient,
even if we consider a lot of particular cases. It is natural to concentrate on
the second part, particularly because it is usually the more difficult one.
However, neglect of the first part can also spell disaster.
Let us 'prove' by induction that for every n E N the number
2 n 1 is even. Assuming that 2n 1 is even, we obtain then 2 ( n 1) 1 =
( 2 n 1 ) 2 is even because it is the sum of an even number 2 n 1 (by the
induction hypothesis) and 2.
Example 5.3
+
+
+ +
+
+ +
+
Of course, 2 n 1 is never even. It is not likely that somebody would
make such a blunder as asserting that 2 n 1 is even for some particular
n E N, but example 5.3 clearly demonstrates the necessity of step 1.
The particular starting value in the first step need not always be 1 as
the next example shows. See also exercises 5.1.8, 5.1.9 and 5.1.10.
+
Introduction to Mathematics with Maple
132
Example 5.4
We wish t o prove that the equation
2x+5y=n
(5.3)
has a solution for every n E N, n 2 4 such that x and y belong to No. An
everyday interpretation of (5.3) is that any amount of whole dollars of a t least
$4 can be paid with a mixture of $2 and $5 notes (or coins).
Step 1: For n = 4 Equation (5.3) has a solution x = 2, y = 0 , and both x
and y belong t o No.
Step 2: Assume that (5.3) has a solution X O , yo in non-negative integers
for a particular value of n and consider the equation
+ 5y = n + 1.
(5.4)
If yo 2 1 then it is easy t o check that
+ 3, yo - 1) is a solution of (5.4)
and both zo+ 3 and yo 1 are non-negative integers. If, on the other hand,
yo = 0, then 2x0 = n 2 4, and so n must be even. Consequently xo 2 2,
leading to xo - 2 2 0 , yo + 1 > 0 and (20 - 2, yo + 1) satisfy (5.4) and both
2x
(20
-
belong t o
No.
We now wish to emphasize the phrase any natural number in step 2.
Example 5.5 We 'prove' by induction that all natural numbers are equal.
Let S be the set containing 1and all natural numbers n such that n = n 1.
Clearly 1E S by the definition of S. Now let n E S, that is, n = n 1. Then
n + 2 = (n+ 1)+ 1= n+ 1, and consequently (n+ 1) E S. So, by the Principle
+
+
of Mathematical Induction, S = N,and this, in turn, implies that n = n
for every n E N,and so all natural numbers are equal.
+1
+
The error was that the inference from n to n 1 was incorrect: if n E S
then either n = n 1 or n = 1. However, if n = 1 there is no way of
showing that n 1= 2 E S.
Another amusing example with a similar twist is given in Exercise 5.1.12.
+
+
Exercises
Exercise 5.1.1
integer n.
Show that nI3 - n is divisible by 13 for every positive
Exercise 5.1.2 Prove that if p is a prime then
for every positive integer n.
Exercise 5.1.3
np -n
is divisible by p
Prove the following formulae by mathematical induction:
Mathematical Induction
133
+ + 32 + - - .+ n2 = n(n + I)(%+ 1) .
(2) i 3+ 23 + s3+ - . + n3 = [ n ( n ; : ) ] 2 ;
’
n(n + 1)(6n3 + 9n2 + n - 1)
(3) l 4+ z4 + 34 + - - - + n4 =
30
n2(n+ 1 ) ~ ( 2 +
n ~2 n - 1).
(4) 1 5 + 2 5 + 3 5 + - + n
=
(1) l 2 22
9
7
12
n ( 2 n - 1 ) ( 2 n 1)
(an - 1)2=
7
3
1
)
(
2
n
1)
.
2
n
(
n
=
+
+ + s2+ . - +
+ +
( 6 ) 22 + 42 + 62 + - - - +
3
(7) l3 + 33 + s3+ . - -+ (272 - q3= n 2 ( 2 n 2 - 1 ) ;
(8) 23 + + 63 + - - - + (2n)3 = 2 n 2 ( n + 112;
n(n + l ) ( n+ 2 ) .
( 9 ) I . 2 + 2 - 3 + 3 - 4 + - . . + n(n + 1) =
3
(5) l2 32
9
7
(10) 1.2.3+2-3.4+3-4-5+.
- -+n(n+l)(n+2)
=
n(n
+ l ) ( n+ 2 ) ( n + 3 ) .
7
4
(11) Suggest a generalisation of (9) and (lo), and then prove the sug-
gested formula, by using induction.
Exercise 5.1.4
Derive formulae (5)-(8) in the previous Exercise from
formulae (1) and (2). [Hint:
l2 2’
- - - ( 2 n ) 2 = l2 32 - - .( 2 n - 1)2 4(12 22 - - - n2).]
+ + +
@ Exercise 5.1.5
+ +
If a l , a2,
+
+ + +
. . . , an are real numbers then
Prove this by using mathematical induction.
Exercise 5.1.6
Prove, by mathematical induction, for x E R
(1 2) (1 2 2 ) (1 2 4 ) - * - (1 22”) = 1 2 2’
x3 * - + 2 2 ” + ’ - 1 ,
where the right hand side contains all powers of x up to the highest shown.
+
+
Exercise 5.1.7
(1-;)
+
+
+ + + +
Let k be a natural number. Prove that for every n E
(1-5>
(l-;)...(l-;)
+
> I - n(n2k 1) .
N
134
Introduction to Mathematics with Maple
@ Exercise 5.1.8
The formula
is valid for n = 1, 2 , 3, 4, 5 but not for n = 6. Construct a formula valid
for the first million natural numbers but not valid for all n E N.
@ Exercise 5.1.9
Let
IM c Z with the following properties:
(1) M contains an integer r ;
(2) k E M implies k 1 E M .
+
Then M contains all integers greater than or equal to r, M = {x; x
Z, x 2 r } . Prove this.
E
Exercise 5.1.10
Use induction to decide for what values of n E N the
inequality n 12 < n2 is correct.
+
Exercise 5.1.11
Ifm E
N,m > 1 then n 5 mn. Prove this.
@ Exercise 5.1.12
This puzzle is attributed to the Hungarian born American mathematician G. Polya.
Theorem: If a finite set of blonde girls contains one girl with blue eyes
then all girls in the set have blue eyes.
The ‘proof’proceeds by induction on the number of girls in the set. The
statement is obviously true if the set contains only one girl. Consider now
a set of k 1 blonde girls { G I , G2, . . . , Gk+l}, and assume that at least
one of them, say GI, has blue eyes. Now taking the set { G I , Ga, . . . , Gk},
and using the induction hypothesis, we deduce that all the girls in this set
have blue eyes. Taking now the set (G2, Gs,. . . , Gk+1}, which has only
k elements and contains a girl with blue eyes, namely G2, the induction
hypothesis ensures that all these girls, including Gk+l, have blue eyes.
Combining this with the earlier result we see that all k 1 girls have blue
eyes, which completes the proof.
Explain this paradox, that is, find the logical error on the above ‘proof’.
+
+
@ Exercise 5.1.13
Prove: if n E
numbers q and r such that
No and rn E N then there exist unique
n=mq+r,
OLr<m,
Mathematical Induction
135
and q E No,r E No. (This theorem expresses what you know from school
as division of n by rn, with quotient q and remainder r . )
@ Exercise 5.1.14 Prove that if n
and ao, a l , a2, . . . , arc, such that
N
E
then there exist numbers Ic E No
withaiE(0, 1 , 2 , 3,..., 9}, i = O , 1 , 2,..., k .
[Hint: use induction and Exercise 5.1.13.1
5.2
Aim high!
When proving theorems by mathematical induction, we often encounter a
seemingly paradoxical situation that a stronger theorem is easier to prove.
If we try to prove the inequality
+
+
1
1
1
1.2
2.3
1
+
.
.
a
+
3.4
n(n
+ 1) < 1
(5.5)
by mathematical induction, then ( 5 . 5 ) is of no use for the proof that
1
-+1.2
21
.3
+
31
.4 +
*
n ( n 1+ 1)
* +
+
(n
+
1
+ 2) < 1.
(5.6)
On the other hand, the stronger statement that
1
1.2
+ 2 1. 3 + 3 1. 4
- - -
+
1
*
.
a
+
n(n
+ 1)
can easily be proved by induction. Let M = {n; n E N, and (5.7) holds}.
Clearly 1 E M . Let n E M , that is, let (5.7) be true, then
1
-+1-2
1
2-3
+-3 1. 4 +
+
This proves that n 1 E M , so by the principle of mathematical induction
M = N and (5.7) holds for every n E N.
Introduction to Mathematics with Maple
136
5.3
Notation for sums and products
Let n E N and a l , a2, . . . , an be elements of a field. The sum and product
of the numbers ai are denoted respectively by
The index i is called the summation or product index, respectively, and
the numbers below and above the signs C and
are called the limits of
the sum or the product. The upper limit is written simply n, but is to be
interpreted as i = n. The index i can be replaced by any other letter (or
symbol) without altering the value of the sum or product, and is sometimes
referred to as a ‘dummy index’. Thus
n
n
n
n
C a i = C a j = x a t .
j=l
i=l
t=l
If m E N,m < n we can first sum the numbers a l , a2, . . . , am and then
the rest, so we have
m
n
n
C
C a i +
i=l
ai
=C a i .
i=m+l
(5.8)
i=l
The second sum on the left hand side of (5.8) can also be rewritten in
the form
+
We obtain the right hand side of (5.9) from the left by setting i = m j
and by changing the limits of summation accordingly.
The symbols C and
also represent parentheses including any of the
following terms which involve the summation or product index, so that we
can write
n
n
n
(5.10)
i= 1
I
i=l
137
Mathematical Induction
It follows from the distributive law that
(5.11)
If
bl,
ba, . . . , bn are elements of the same field then
n
n
n
i= 1
i=l
i=l
(5.12)
Consider now the rnn numbers aij, i = 1, 2,
For these numbers we can first form the sum
for j = 1, 2,
. . . , n,
j = 1, 2,
. . . , m.
. . . , m, and then sum these to obtain
m
m
n
(5.13)
or we can first form the sum
m
j= 1
for i = 1, 2,
. . . , n and then sum these numbers and obtain
n
m
(5.14)
Since both (5.13) and (5.14) are the sum of the mn numbers aij it is obvious
that
m
n
n
m
(5.15)
and either sum is often written
(5.16)
Introduction to Mathematics with Maple
138
If m = n then (5.16) can be further abbreviated to
n
C
(5.17)
aij
i, j = l
When dealing with sums like (5.13) or (5.14) care should be taken to
use distinctive letters for the summation index. For instance
whereas
The inner summation limit in a sum like (5.14) can depend on the outer
summation index. Thus we have, for example,
n
i
If we sum the columns in this expansion first and add these sums together
we obtain
n
i
n
n
Formula (5.19) can be proved without explicitly writing out the terms as
in (5.18) by realising that each term in (5.19) contains aij with i and j E N
such that 1 5 j 5 i 5 n. Denoting the summation indices by distinct symbols is also necessary when, for example, two sums need to be multiplied,
139
Mathematical Induction
as in
Formulae (5.8), (5.11) and (5.12) are consequences of the field axioms, and
should, in fact, be proved by using mathematical induction. We do not
wish to dwell on simple proofs such as these, but, as an example of how
these proofs could be arranged, we prove the next theorem, and leave a few
similar proofs as exercises.
Theorem 5.1
If n E
N and a l , a2, . . . , an are real numbers then
(5.20)
Proof. If n = 1 then (5.20) is true with the equality sign. Now assume
(5.20) holds for some n E N, and consider n 1 real numbers a k , k =
1, 2, . . . , n, n 1. Since
+
+
we have, by Theorem 4.2 and by the induction hypothesis,
There are formulae for products similar to those for sums in (5.8), (5.9),
Introduction to Mathematics with Maple
140
(5.12) and (5.15).
m
n
i=l
i=m+l
(5.21)
n
(5.22)
i=m+1
n
n
i=l
n
(5.23)
i=l
m
n
(5.24)
Care is needed in handling sums and products if the terms following
the signs C or
are independent of the summation or product index,
respectively. If, for instance, ai = c for i = 1, 2 , . . . , n then
n
e a i = e c = n c .
i=l
(5.25)
i=l
Similarly
n
n
i=l
i=l
(5.26)
In order to preserve some formulae in limiting cases, and to avoid continually having to consider these cases separately, it is convenient to introduce
the void sum and product by defining
0
Cai = 0 ,
(5.27)
i=l
and
0
I-J.2
= 1.
(5.28)
i=l
These formulae are purely conventional and are devoid of any deeper meaning.
Mathematical Induction
5.3.1
141
Sums in Maple
The s u m 0 function can be used to calculate sums.
In the form
sum (expr ,variable=m. .n) , the specified expression is summed over the
given range for the specified variable.
>
# Evaluate t h e sum of i from 1 t o 10
>
sum(i,i=l..lO);
55
>
>
# Evaluate t h e sum of i n 2 + i from 1 to 5
sum(in2+i,i=l. .5);
70
Recall that Maple gives exact solutions. Often when using the s u m 0
command, you will need to use either evalf (1 to obtain a floating point
result, or a decimal point somewhere in the sum. In the following examples,
s u m 0 is being used on fractions.
>
# Evaluate t h e sum of l / i from 1 t o 50
>
s u m ( l / i , i = l . .50);
13943237577224054960759
3099044504245996706400
>
>
>
# Use e v a l f ( ) t o e v a l u a t e t h e same sum
# giving a decimal r e s u l t
evalf ( s u m ( l / i , i = l . .50));
4.499205338
>
>
>
>
# Use a decimal p o i n t t o e v a l u a t e
# t h e sum of l / i from 1 t o 100,000
# g i v i n g a decimal r e s u l t
s u m ( l . / i , i = i . .100000);
Introduction to Mathematics with Maple
142
12.09014612
Rather than specifying numbers as the range for summation, you can
also use variables. In the following examples, the indefinite sum is calculated for i = 1 . .. N ) .
>
>
# Evaluate the sum of i from I to
N
sum(i,i=l. .N);
1
- (N
2
+ q2- -21 N - -21
The above result is not in the form you will find quoted in textbooks
or other reference works such as Gradshtein and Ryzhik (1996), so we
try to simplify it.
>
simplify(%) ;
-1N 2 + 21N
2
_
>
>
>
_
_
~
~
Evaluate the sum of i-2 from 1 to N
# in simplified form
simplify(sum(in2,i=l. .N));
#
1
1
1
3 N 3 + 5N 2 +
s
Maple honours our convention on void sums (5.27), at least in cases
where the second index is 1 less than the first index. For instance
0
0
Mathematical Induction
143
Maple can sum elements of a list. This enables evaluation of sums
which would be difficult to form. The next few lines of Maple show how
to evaluate a sum of numbers of the form l / p where p runs through the
first 100 primes.
> L:=[seq(ithprime(k) ,k=l..loo)] :
>
evalf(sum(l/L[i]
,i=I..iOO));
2.106342121
5.3.2
Products in Maple
The product0 function can be used t o calculate products. Its usage is
very similar to that of the sum() function described above.
> # Evaluate the product of l+l/i^2 from 1 to 10
> product (1+1/in2,i=l. .lo) ;
2200962205
658409472
Now turn this result into a decimal number.
>
evalf(%);
3.342847117
>
>
# Evaluate the product of (i+l)/(i-l)
product((i+l)/(i-1)
,i=2..20) ;
from 2 to 20
210
>
>
>
>
Evaluate the product of (i+l)/(i-l)
for i ranging from 2 to N and reduce it
# to what Maple thinks is the simplest form
simplify(product((i+l)/(i-1) ,i=2..N));
#
#
1
-N(N+l)
2
Introduction to Mathematics with Maple
144
Muple can produce sums and products which would not be so easy to
produce otherwise. Assume we want to sum terms of the form aiajak where
i, j , k are all distinct and run from 1 to 4. Subscripts are created in Muple
by brackets: ai is entered into Muple as a[il . The first step is to produce a
list of all possible combinations of three elements from { 1, 2, 3, 4). We use
the combinatorics package called combinat and the command is choose.
>
chs :=combinat[choose] (4,3);
chs := [[1,2,31, [I, 2,41, [I, 3,41, [ 2 , 3 , 4 1 1
The second element in the third list [1,3,4]can be recalled by
>
chs[31 [2];
3
The required sum is now easily created
>
sum(product (a[chs [i] [j]1 ,j=1.. 3 ) ,i=l . .4) ;
Exercises
@ Exercise 5.3.1
i=l
n
Prove, by induction, for n E
N,q E R
n
i= 1
n
i=l
C qi = qn+l - 1 .
(3) (q - 1)
i=l
Exercise 5.3.2
notation.
Exercise 5.3.3
Rewrite the formulae in Exercise 5.1.3 using the
Let ai, bi be real numbers for i = 1, 2, 3,
n
(1) 0 5 ai
n
< bi f o r i = 1, 2, ..., n implies n a i < r l [ b i ;
i=l
n
i=l
n
(2) 0
5 ai 5 bi for i = 1, 2, . . . , n implies
n n
ai 5
i=l
bi ;
i=l
. . . , n. Prove
Mathematical Induction
n
145
n
(3)
k=l
n
2
(4) ‘\ x a i )
c
n
=
i=l
aiaj;
i, j = l
Prove that among n real numbers a l l a2, . . . , an
there is a largest and a smallest. The largest number is denoted by
Max(a1, a2, . . . , an) and the smallest by Min(a1, a2, . . . , an).
@ Exercise 5.3.4
Exercise 5.3.5
Let a l l 132, . . . , a, be real numbers, and bl, b2, . . . , bn
be positive real numbers. Prove that
Min
(EL
a2
b1’&”’’
”>
bn
ai+aa+-*.+an
b,+b2+...+bn
[Hint: Start with n = 2. Let the minimum on the left-hand side be a l / b l .
Derive the inequality alb2 albl I
a2bl albl from which the left-hand
inequality follows. Then use induction to prove the left-hand inequality.
The argument for the other inequality is similar.]
+
0 Exercise 5.3.6
n
n
+
Show that
n
[Hint: (1) Use (5.19) and (5.25). (2) Simplify the double sum in (1) by
using Exercise 5.3.1 (3).]
5.4
Sequences
A function whose domain is N is called a
sequence. Letters a, b, 2, y, z , u
are often used to denote sequences. The function value of a sequence u at
n is called the nth t e r n of the sequence and is denoted by un. However, we
may occasionally use the notation u(n) also. Displaying the first few terms
often gives a good idea of the behaviour of the sequence although one must
realize that the first few terms alone do not determine the sequence.
Introduction to Mathematics with Maple
146
Exercises
Exercise 5.4.1 For a given k define two different sequences which have
the same first k terms.
5.5
Inductive definitions
Sequences can often be defined inductively. For example, the arithmetic
sequence with first term c and difference d can be so defined, by setting
a1 = c and a,+l = a,
d for n E N. Using the last relation successively
for n = 1, 2, 3, . . . we obtain
+
a2=a1+d=c+dl
a3 = a2
a4
+d = c+ 2d,
= a3 + d = C + 3 d ,
There seems to be no difficulty in finding a, for every n E N. Is it really
certain that a, is defined for every n? The answer is ‘yes’, in this case.
However this process of defining a sequence has some similarity with inductive reasoning discussed in Section 5.1 and we saw there that such a process
needed to be made quite specific in mathematics. Consider the following.
99
- and define f(1) = 1, f ( [ ( n+ l ) a ]+ 1)
100
f ( n ) + 1. Setting n successively t o 1, 2, 3, . . . we obtain
Example 5.6
Let
Q
=
f(2)=f([g]
f(3) = f
(
f(4)=f([3
=
+1) = f ( l ) + l = 2 ,
+ 1) = f(2) 4- 1 = 3
7
+1) = f ( 3 ) + 1 = 4 ,
...There seems t o be no difficulty in finding f ( n ) successively for every n E
However this time there is no sequence f such that
N.
147
Mathematical Induction
and
for every n E N. Indeed, for n = 100 we have L(n
to f(lO0) = f(lO0) 1. This is clearly impossible.
+
+ 1)aJ+ 1= 100, leading
Something more is needed to define a sequence than just checking that
there is no difficulty in calculating the first few (or the first few billion)
terms of the sequence. The solution is found in the Recursion Theorem.
Theorem 5.2 (Recursion Theorem) Let S be a set, a E S and
g : S 4S. Then there exists a unique sequence f such that
f(1) = a ,
and
f(n + 1) = g(f(nNProof. Here we prove only the uniqueness and postpone the proof of
existence to Section 5.9 at the end of this chapter. Let F satisfy
Define M = {n; n E N and F(n) = f ( n ) } ;clearly 1 E M . Assume now
that n E M . Then F(n 1) = g(F(n))= g(f(n))= f ( n 1). This means
that n 1 E M . Consequently A4 = N, f ( n ) = F(n) for every n E N and
therefore f = F .
+
+
+
We now use the Recursion Theorem to define the arithmetic sequence.
It is sufficient to take g : x H x d and f(1) = a1 = c. Theorems concerned with sequences defined inductively (recursively) are usually proved
by mathematical induction.
It is also possible to start with earlier or later terms than with n = 1.
For instance, if a E R, n E No then we define an recursively as follows:
a0 = 1, an+l = a . an.
+
148
Introduction to Mathematics with Maple
Theorem 5.3
(i)
(ii)
(iii)
(iv)
(v)
If a E R, b E R,
n E No,m E No then
anbn = (ab)";
anam = an+m;
(am)n = amn;
0 5 a < b + an < b";
m < n, a > 1 am < an.
*
This theorem is elementary and we leave the proof, generalised to the
case where m E Z, n E Z as Exercises 5.5.1, 5.5.5 and 5.5.7. As a guide,
here we prove item (ii) by induction.
Proof. The statement is true for n = 0 and arbitrary m. Assume (ii)
and consider an+' -am. By the recursive definition an+' = a - a n and by the
induction hypothesis anam = an+m. Consequently an+' . am = a . an - am =
a . an+m = an+m+l.
0
1, 1, 2, 3, 5, 8, 13, 21, . . .
The sequence f is well defined and is called the Fibonacci sequence after the
mathematician who first investigated it. It occurs in a wide range of seemingly unrelated areas, such as the arrangements of branches on a tree, the
size of breeding populations of animals and in efficient procedures for sorting large amounts of data. However Theorem 5.2 is not directly applicable
to this definition, and the required modification is stated in Exercise 5.5.4.
If a similar recursive definition is needed relating more than two previous
terms in the sequence, this will also need to be justified. Exercise 5.5.4
can serve as a guide to more general recursive definitions. A very general
recursion theorem is proved later as Theorem 5.13.
Exercises
@ Exercise 5.5.1 For a # 0 , a E R and
a # 0 , b # 0 , m E Z and n E Zprove:
(I) an+m = anam;
(2) (am)n= anm;
(3) (ab)n = anbn.
n E N define a+
=
( u - ' ) ~ .For
Mathematical Induction
149
Exercise 5.5.2 Define the geometric sequence (progression) inductively
and prove the formula for the nth term and for the sum of n terms.
@ Exercise 5.5.3
Prove that
n
(1) an - b" = ( a - b)
an-'bk-l
;
k=l
2n
k=O
for n E N and a , b elements of a field.
Exercise 5.5.4 Let S be a set and g : S x S -+ S . Then there exists
exactlyone sequence f such that f ( 1 ) = a E S, f ( 2 ) = b E S and f(n+2)=
g ( f ( n ) f, ( n + 1) ) for n E N. Prove the uniqueness o f f .
@ Exercise 5.5.5
nE
N and 0 5 a < b imply an < b". Prove this.
@ Exercise 5.5.6
nE
N and a < b imply a2n+1 < b2n+1. Prove this.
@ Exercise 5.5.7
mE
Z,n E Z,n > m and a > 1 imply am < an. Prove
@ Exercise 5.5.8
-n
E
@ Exercise 5.5.9
m E
this.
N and 0 5 a < b imply bn < an. Prove this.
Z,n E Z,n > m and 0 < a < 1 imply am > an.
Prove this.
2
1
. Show that xn is not
= -3
1 x,
defined for all n, and explain why this does not contradict Theorem 5.2!
[Hint: Search for S.]
Exercise 5.5.10
5.6
Let
x1 =
--, xn+l
+
The binomial theorem
nt n! ( r e 2
finition 5.1 (Factorial and Binomial Coefficient)
nz.
n
n factorial) is defined for n E NO by n! =
Consequently we have
i=l
O! = 1, l! = 1, 2! = 2, 3! = 1 2 3 = 6 , etc. The binomial coefficient
)
(read ' n by k' or ' n choose k ' ) is defined by
n!
Introduction to Mathematics with Maple
150
It is a matter of simple calculation to check that
(L) + (Ic
1> (L 1:)
=
. The last relation enables us to calculate
successively as shown in the following table.
1
1
1
1
1
3
1
1
2
4
5
1
3
4
6
10
(5.29)
1
10
1
5
1
Each number in this table is the sum of the two numbers which are situated to the left and the right in the row directly above it. The triangular
table (5.29) is called Pascal's Triangle, and the kth element in the nth row
is
(L I:) .
The Pascal triangle shows that
is an integer for n, k E
No
with k 5 n.
Theorem 5.4 (Newton's Binomial Theorem)
ments of a ring and n E N then
(a
+ b)" =
2 (L)
x a"-'b'.
If a , b are ele-
(5.30)
k=O
Remark 5.1 A ring need not contain all the natural numbers: for instance
the ring of even numbers does not. In such a ring the product nu may not
be defined for a in the ring and n E N . Usually multiplication by a natural
number means repeated addition: by analogy we define
nxa
def
=
a+a+...a
Y
n summands
for a in a ring and n E N. For the proof of the Newton Theorem we also need
the identity n x a m x a = (n m) x a.
+
+
Proof. The theorem is obviously true if n = 1. Assume (5.30) and consider ( a
+ b)"+l.
Mathematical Induction
(a
+ b)""
=
(a
151
+ b)(a+ b)"
Two easy consequences of (5.30) are
2n =
2 (;)
(5.31)
7
k=O
and
0=
g(-l)k(;).
(5.32)
k=O
The binomial coefficient
(3
is produced in Maple by binomial (n,k) .
Exercises
Prove the following formulae for the binomial coeffi-
Exercise 5.6.1
cients:
N
(&)
= 2n-1
(1)
k=O
k=O
where N =
IT]
n+l
;
Introduction to Mathematics with Maple
152
[Hint: (1) Add (5.31) and (5.32). (2) Expand ( 1 + ~ )and
~ " (l+x)"(l+x)"
and compare the coefficient of x".]
Exercise 5.6.2
For integers k , n, 0 5 k 5 n prove
n+l
j=k
[Hint: Use induction on m = n - k.]
Exercise 5.6.3
For natural numbers k, n, k 5 n prove
(1
@ Exercise 5.6.4
+
q
k
k2
n
n2
< 1+ - + - .
n
Define the polynomial coefficients
and prove the polynomial theorem
r
r
for ai in some ring.
@ Exercise 5.6.5
[Hint:(l
Prove: 0
+ h)" < 1+
< h < 1, n E N implies
+ h)" < 1+ 2"h.
(1
and use (5.31).]
k=O
Exercise 5.6.6
5.7
Prove: ( a
+ b)5 = a5 + b5
mod 5
Roots and powers with rational exponents
A basic result of this section is the following:
Theorem 5.5 I f a > 0 , a E R, n E
positive number x such that xn = a.
N then there exists exactly one
Mathematical Induction
153
The number x from Theorem 5.5
5.2 (nth root of a )
For n = 2 it is
is called the nth root of a and is denoted by $.
customary to write fi instead of fi.For a = 0 we set fi = 0.
For the proof of Theorem 5.5 we need the following Lemma.
Lemma 5.1
Let a > 0, n E N,J: > 0.
(i) if zn < a there exists y E R such that x < y, y n < a.
(ii) if a < xn there exists z E R such that 0 < z < x, a < zn.
Proof.
(i) We find h with 0 < h < 1such that ~ ~ ( l t <ha )and
~ set y = x ( l +
h). Since (1+ h ) n < 1+2nh by Exercise 5.6.5 we have ~ ~ ( l + <h ) ~
xn 2nxnh, and it is now sufficient to choose h such that 0 < h < 1
+
and xn
+ 2"xnh
requirements.
(i, a2ixy)
(k) < .:
5 a. h = Min
-
- satisfies both of these
1
(ii) We note that - > 0, - > 0,
X
a
1
1
that yn < -. We can now take z = -.
a
Y
By (i) there is y
1
> - such
X
0
We now take up the proof of Theorem 5.5.
Proof. Since 0 < u < v implies un < vn there is at most one x satisfying
zn = a. For the existence proof we assume n > 1 since the case n = 1 is
trivial. We now distinguish three cases.
(A) a = 1 then x = 1.
(B) a > 1. We define S = { u ; u > 0, un < a}. The set S is non-empty
since 1 E S . It is also bounded above by a , since v 2 a implies
wn 2 an > a (see Exercises 5.5.5 and 5.5.7). By the least upper
bound axiom there exists x = s u p s . We prove that xn = a by
showing that each of xn < a and a < xn is impossible.
1. If xn < a then by (i) of Lemma 5.1 there exists y E S , y > x,
contradicting the definition of x as the supremum of S .
2. If a < xn then by (ii) of the same Lemma there exists x, z < x
with a < zn. Since x is the least upper bound of S there exists
u E S, with z < u 5 2. This implies that a < zn < un, so that
u $ S, another contradiction.
Introduction to Mathematics with Maple
154
It is worth emphasizing that @ is, by its very definition, positive or
zero. Consequently 0need not be a (it is not if a < 0); however laI2 = a2
and it follows from the uniqueness part of Theorem 5.5 that
= la1
(always). Further, if a > 0, b > 0, since (
= ab we have (again by
the uniqueness part of Theorem 5.5)
@a)n
a=
Gvi.
0
(5.33)
If n is odd then for every a E R there exists an x E R such that xn = a.
Indeed, for a < 0 we have a = - 1 ~ 1 , so that xn = a = - 1 ~ 1 , from which
it follows that x = It is customary to denote this x also by fi.
Hence for an odd n and a < 0 we have @ = - fi.
m.
If a > 0 , b > 0 and m E N then
Theorem 5.6
(i)
(ii)
(iii)
fis;
6 $;
8 5;
(iv) a
=
=
= 1
< b =+
~
<
a.
Proof.
(i) is just a restatement of (5.33).
(ii)
~ ffi i
=
by (i).
(iii) This follows from (ii) by setting a = 1.
(iv) @ = f i and fi >
are both impossible, since it would follow
that a = b in the first case and a > b in the second. Hence we must
have * < ~.
r
Every rational number r can be expressed in many different ways as
P with p E Z and q E N. We wish to define ar as [email protected]
but before we
= -
4
do that we prove
Mathematical Induction
155
Let p , m be integers, q and n natural numbers and
Theorem 5.7
P
m
a > 0. If - = - then
q
n
fi=
*.
(5.34)
Let @ = x, @ = y. Then u p n = xQn and amq = ynQ. It
follows that xnq = ynQ and by the uniqueness part of Theorem 5.5 we have
x = y.
O
Proof.
Definition 5.3 (a' €or rational r )
For a > 0, r E
Q,r = E ,
we define
ar =
Remark 5.2
fi.
Q
q EN
(5.35)
If r E Zthen ar defined by (5.35) is the same as defined in
Section 5.5.
Theorem 5.8
If a , b are positive, r E Q, s E Q then
(i) 1' = I, a' > 0 , a' = 1;
(ii) arbr = (ab)';
(iii>
= aT br '
(iv)
=
b' '
(v) aras = a r + s ;
(i)r 1.
a'
(vi) - = ar-';
as
1
(vii) ads = - as '
(viii) (a < b, r > 0 ) 3 a' < b';
(ix) (1 < a, s < T ) 3 as < a'.
Proof.
(i) is obvious.
rn
P-, s = with q E N and p E Z,
Q
rn E Z (it is a trivial exercise to show that r and s can be written with the
same denominator).
For the rest of the proof we put r =
156
Introduction t o Mathematics with Maple
(ii)
(by Theorem 5.6 (i) )
=
f/(ab)p
(by Exercise 5.5.1)
= (ab)'.
(i)'
(iii) b'
= ( b i ) ' by (ii), and therefore b'
(iv) This follows from (iii) by setting a = 1.
(i)' = a';
(vi) asar-s = ar by (v).
(vii) Put r = 0 in (vi).
(viii) Since r > 0, q E N we have p E N. Consequently a p < bP (by
Theorem 5.3 (iv) ), and by (iv) of Theorem 5.6 (with a replaced by
u p , b replaced by bP, etc.) we have @ < @;
(ix) We have r - s
> 0 and by using (viii) we obtain
lr-'
< a'-'
the last equation following from (vi).
=
ar
as
'
0
Exe rc ises
@ Exercise 5.7.1
Prove that Theorem 5.6 remains valid for arbitrary real
a , b provided n is odd and b # 0 in (ii) and (iii).
@ Exercise 5.7.2
then
Prove by induction: if a is positive, n E No and r E Q
= urn.
@ Exercise 5.7.3
Prove: i f a is positive, r E Q and k E Z then
= ark.
Mathematical Induction
157
d r m = ,/= fi = 1. B u t
we also have d r m = d m ,/m= ( d n ) ' =
= -1.
@ Exercise 5.7.5
W e have
Hence 1 = -1. Explain this paradox.
v m + qr&;
Exercise 5.7.6
Let g : x H
domg = [0,1]. By
showing that the equation y = g ( x ) has a unique solution for y E [ f i 2,1
prove the existence of 9-1 and find it.
Exercise 5.7.7
Let h : x
one-to-one and find h-1.
H
x 2 , domh =] - 00, -1[. Show that h is
Exercise 5.7.8
Let F : x +I x 2 for x < -1 and x H -x for x 2 -1.
Graph F . Prove that F is one-to-one, and find rg F and F-1.
5.8
Some important inequalities
Mathematical induction is useful in proving general inequalities, as the next
three theorems show.
Theorem 5.9 (Bernoulli's Inequality)
then
+
If x E R, x
+
(1 x)" 2 1 n x .
Remark 5.3
Theorem 5.4 if x
2 -1,
nEN
(5.36)
Inequality (5.36) is a trivial consequence of the Binomial
2 0.
Proof. If n = 1, equality holds in ( 5 . 3 6 ) . Assume (5.36) holds for n and
multiply it by 1 x 2 0.
+
+
(1 x)"+'
2 ( 1 + nz) (1 + 2 ) ,
=1
+ (n+ 1)z+ n x 2 ,
Ll+(n+l)x.
If a1 , a2, . . . , an are non-negative numbers then
G=
V a l a2 . . . an
Introduction to Mathematics with Maple
158
is called the geometric mean of a l l u2,
1
A = ;(a1
. . . , an and
+ +- - - +
a2
is called the arithmetic mean of a l , u2,
an)
. . . , an.
Theorem 5.10 (Arithmetic-Geometric Mean Inequality)
ui 2 0 for i = 1, 2, . . . , n then
If
(5.37)
i=l
Proof. Equality holds in (5.37) for n = 1. Clearly, rearranging the numbers doesn't affect the result, so we can arrange them in order of increasing
size
n+l
Let A be the arithmetic mean of these n
+
+ 1 numbers, A = -
+ 1 i=l
ai. It
is clear that a1 an+l- A 2 0. The arithmetic mean of the n non-negative
numbers a2, u3, . . . , a n , a1 an+l - A is A, and applying the induction
hypothesis to these numbers we have
+
n-1
i=l
and hence
From the obvious inequality a1 5 A 5 an+l it follows that ( A- a l ) ( a n + l A) >_ 0, and this can be expanded to give A ( a l + a n + l - A)
or
If this is combined with Inequality (5.38) we get
n-1
(5.40)
i=l
Mathematical Induction
This is (5.37) with n replaced by n
Corollary 5.10.1
159
+ 1.
0
Equality holds in (5.37) if and only if all ai are
Proof. The ‘if’ part is obvious. We prove ‘the only’ part by showing that
if not all ai are equal then
nfl
(5.41)
a= 1
This is obvious if a1 = 0, so let 0 < a1 < an+l With notation as before, we
now have a1 < A < an+l, SO that ( A - al)(an+l - A ) > 0, leading to strict
inequality in (5.39). Multiplying this inequality with the positive number
ai+l and taking into account Inequality (5.38) gives (5.41).
0
Jaz Inequality (5.37) becomes
2ab 5 a2 + b2.
For n = 2, a = 6,
b=
(5.42)
This was established directly and easily in Exercise 4.2.3 by considering the
inequality ( a - b)2 2 0.
Theorem 5.11 (Schwarz’s Inequality)
k = 1, 2, . . ., n then
If ak and bk are in
for
(5.43)
Proof.
If n = 1 equality holds in (5.43). Assume now that the theorem is
n
true for n E
we have
N.Writing
C akbk = sn, ( k a ; ) 1 ’ 2
k=l
k=l
= rn,
(2
bi)1’2 = t n ,
k=l
The induction hypothesis
(5.44)
Introduction to Mathematics with Maple
160
implies
we have, substituting this in (5.45),
(5.47)
0
Corollary 5.11.1
There is equality in (5.43) if and only if for
a = 1, 2, . . . , n either bi = 0 or there exists c E R such that ai = cbi.
(ai = 0 is covered by the special case c = 0.)
Proof.
0
See Exercise 5.8.7.
Exercises
Let S c N with the followingproperties:
Exercise 5.8.1
(1) 2n E S whenever n E N;
(2) m E S implies Ic E S for all Ic < m, Ic E
N.
Show that S = N.
Exercise 5.8.2
Prove the arithmetic-geometric mean inequality for n =
2 m by induction on m, and then use Exercise 5.8.1 to prove it for general
n.
Exercise 5.8.3
Let
n
i=l
n
i=l
X I ,22,
. . . , xn be non-negative.
n
n
i= 1
i=l
Prove these results.
Exercise 5.8.4
Use Theorem 5.10 to prove that among all rectangles of
perimeter s the square has the largest area.
@ Exercise 5.8.5 For x 2 -1 and n = r / s with r,s E N and r < s prove
X
that q G 5 1 + -. [Hint: Use Theorem 5.10 on a1 = a2 = - - = a, =
n
1 + X , ar+l = 1,.. .
= 1.1
,US
Mathematical Induction
Exercise 5.8.6
r
For
12:
161
and n as in the previous exercise prove that 1
[Hint: Use Exercise 5.8.5 to obtain
divide byI.-/?
2/72
+d
p 5 1+
+
and then
Exercise 5.8.7
Prove Corollary 5.11.1.
[Hint: The ‘if’ part is easy. The ‘only if’ part is obvious if n = 1. Proceed
by induction. If equality holds in (5.47) then from 5.45 and 5.46 we must
have
Consequently equality holds throughout and Isn] = rntn. Now use the induction hypothesis to complete the proof. Distinguish three cases:
(a) t , = 0 , bn+l = 0 ;
(b) t, = 0 , bn+l # 0;
(c) ai = cbi for i = 1, 2, . . . , n.]
5.9
Complete induction
In some inductive proofs the assumption that the assertion is valid for n - 1
is not strong enough to conclude that the assertion is valid for n. A stronger
induction hypothesis is employed, namely, that the assertion is valid for all
natural k less than n. The justification for such proofs is based on the
following theorem.
Theorem 5.12
Let S be a subset ofN with the followingproperties:
(i) 1 E S ;
(ii) for every n E N,the foIIowing implication holds:
( k E S for all k < n) + n E S
Then S = N.
Proof.
Assume that S
# N,
that is,
N \ S # 8. By
the well ordering
162
Introduction to Mathematics with Maple
principle (Theorem 4.10) there is a smallest n E N \ S, say m. Clearly
m # 1 since 1 E S by (i). For all k < rn, k E N we therefore have
k E S, and by (ii) we have m E S, which is a contradiction, so our initial
assumption is wrong, and S = N.
0
Theorem 5.12 is often useful as the next example shows.
Example 5.7
For every n E
N there exists s
E
No and m
E
N such that
n = Y(2m - 1).
Let S = (n; n E N, n = 2'(2m - 1) with s E No and m E N}. We have
1 E S; indeed every odd n E 5'. Assume now that n is even and k E S for
all k E N, k < n. Since n is even we have n = 2 k , k E N, k < n and by
the induction hypothesis k = 2t(2m - l), n = 2t+1(2m - 1) and n E S. By
Theorem 5.12 S = N.
Complete induction can also be used for recursive definitions. The next
example shows this.
Example 5.8
Let x E R, x > 0. There exists the largest integer not
exceeding z, and we denote this' by ao. Then find the largest integer a1 such
that
a1
ao+-<x,
10
-
and proceed indefinitely. At the nth step find the largest integer an such that
a0
a1 + a2 + . * . +an
+5 X.
10
102
lon
This defines a sequence an and we say that x is expressed as a decimal fraction
a0.ala2ag. ... This notation should not be confused with any product: it
simply expresses the fact that for every n the number x satisfies
If z
n
n-1
i=O
i=O
> 0 and is expressed as a decimal fraction
uo.a1a2a3.. . then
a1
a2
u u = a0 + ++* *
10
'In other words a0 = 1.1.
102
*
an
+lon
Mathematical Induction
This, together with the proof that ai E
postponed to Exercises 5.9.2 and 5.9.3.
No and,
163
in particular, 0
5 ai 5 9 is
The decimal fraction 0,000.. . is associated with the number 0. If y < 0
we write x = -y, find the decimal expansion of x in the form ao.ala2a3.. .
and then associate the decimal fraction --ao.ala2ag.. . with y. If J: is a
positive rational and of the form
bl
x=bo+ 10
b2
++-*-+lbno n
102
then the decimal fraction for x is bo .bl b2 . . . bnOO0 . . ..
Any other number b E N,b > 1 can be used instead of 10 in the above
construction. For b = 2 we obtain what are called binary fractions, and for
b = 3 ternary fractions.
The above construction of decimal fractions or, more precisely, of the
sequence with nth term an can be made precise by using the following
theorem.
Theorem 5.13 (Recursion Theorem) Let S be a set, a E S and
sn-l H S for every n E N, n > 1. Then there exists a unique
function f : N -+S such that
gn :
(9 f ( l >= a ;
f (n>= g n ( f ( l > , f (21, - - - f (n- 1)>-
(ii)
7
Proof. The proof of uniqueness is rather similar to the uniqueness proof
of Theorem 5.2. The proof of existence is postponed to the last section of
this chapter.
0
The definitions of
and
ai
above can now be formally made:
Introduction to Mathematics with Maple
164
Exercises
@ Exercise 5.9.1
Every natural number is divisible by a prime. We used
this theorem as self-evident without proof in Chapter 2. Prove it using complete induction! [Hint: If n is a prime there is nothing to prove, otherwise
it is divisible by a number smaller than 71.3
Exercise 5.9.2
Let a real number x > 0 be expressed as a decimal
fraction ao.a1a2a3.. . . Prove that 0 5 an 5 9 for every n E N. Also prove
that for binary and ternary fractions one has a, E (0, 1) and a E (0, 1, 2},
respectively.
Let x > 0 and ao.a1a2u3.. . be its decimal fraction. Let
a
i
- Prove that x = SUP(Y; y = An}.
1oi *
@ Exercise 5.9.3
n
A, =
C
i=O
@ Exercise 5.9.4 Let x and y be positive with the same decimal fraction
ao.a1a2a3.. . . Prove that x = y.
[Hint: The proof is immediate from Exercise 5.9.3.1
@ Exercise 5.9.5
Our definition of decimal fractions is slightly different
from the conventional one. We shall rectify this in Chapter 11. Prove that
with our definition 0.999... is not a decimal fraction for any x E R. Also
1
show that the decimal fraction corresponding to - is 0.3333.. ..
3
@ Exercise 5.9.6
Prove that the numbers s and rn from Example 5.7 are
uniquely determined by n.
Exercise 5.9.7
Prove that if two positive real numbers x and y have
distinct decimal fractions then they are distinct.
Exercise 5.9.8
imal fractions.
5.10
Prove that two distinct real numbers have distinct dec-
Proof of the recursion theorem
To complete our treatment of the Recursion Theorems 5.2 and 5.13 we are
going to prove the existence part of Theorem 5.13. We need the following
Lemma.
Mathematical Induction
Lemma 5.2
For every K E
(1, 2, . . . , K ) H S such that
165
there exists a unique function f~ :
fK(1) = a
f K W = gn(fK(I), fK(%
(5.48)
* * *
, f K ( n - 1))
(5.49)
for all n 5 K , n E N.
Proof. First we prove that f ~ if ,it exists, is uniquely determined. The
proof is by induction. The function f l is uniquely determined, f1 : 1 H a.
Assume now f ~ - is
l unique, then
for n 5 K - 1 by the induction hypothesis and
f d K ) = 9K(fK(1), . . . , fK(K- 1))
(5.51)
by (5.49). In view of (5.50) we have
which means that fK(n) is uniquely determined for n = 1,2, . . . , K .
If f~ exists it must satisfy (5.52). We use this piece of knowledge to
define f ~ More
.
precisely we define first fl(1) = a. Then we proceed by
induction again. Assume f ~ - 1exists and define f ~ ( nby
) (5.50) and by
(5.52). It remains to show that f~ so defined satisfies (5.48) and (5.49).
The first equation is easy, f ~ ( 1=) f ~ - l ( l )= a. Then we have
for n 5 K
-
1 and
for n = K by (5.52) and by (5.50).
We now take up the proof of Theorem 5.13.
166
Introduction to Mathematics with Maple
u
00
f
f~ and emphasize that each fK is a set of
K=l
pairs (n,f K ( n ) ) . Obviously f is a relation, and it is sufficient to prove
that it is a function. Let (s, u ) E f and (s, w) E f. Then u = fp(s) and
w = fq(s) for some natural numbers p , Q. Since both the restriction of fp
and the restriction of fq to (1, 2, . . . , s} must, by uniqueness, be equal to
fs, we have fp(s) = f,-,(s)= fs(s), that is, u = w. Then f(1) = a since
f(1) = fl(1). Similarly f(k) = f n ( k ) for all k 5 n, k E N. Consequently
Proof.
We define
for every n E N.
5.11
=
0
Comments
We devoted this chapter to mathematical induction and its applications
and to recursive definitions. The foundation for this was laid in the previous chapter in Theorems 4.5 and 4.10. These theorems were established
as consequences of the axioms for the reals, that is, axioms of a complete
ordered field. In Peano’s axiomatic approach, which we sketched in Section
4.8, mathematical induction was one of the principal axioms. However the
recursion theorem still must be proved and it plays an even more fundamental and central role than in our approach. In Peano’s approach, the
recursion theorem or some modification of it is needed for the definition of
addition and multiplication of natural numbers.
Chapter 6
Polynomials
Polynomial functions have always been important, if for nothing
else than because, in the past, they were the only functions which
could be readily evaluated. In this chapter we define polynomials
as algebraic entities rather than functions, establish the long division algorithm in an abstract setting, we also look briefly at zeros
of polynomials and prove the Taylor Theorem for polynomials in a
generality which cannot be obtained by using methods of calculus.
6.1
Polynomial functions
If M is a ring and ao, a l , a2, . . . , an E M then a function of the form
is called a polynomial, or sometimes more explicitly, a polynomial with
coefficients in M . Obviously, one can add any number of zero coefficients,
or rewrite Equation (6.1) in ascending order of powers of z without changing
the polynomial. The domain of definition of the polynomial is naturally M ,
but the definition of A(x) makes sense for any x in a ring which contains
M . This natural extension of the domain of definition is often understood
without explicitly saying so. If A and B are two polynomials then the
polynomials A B , -A and AB are defined in the obvious way as
+
A + B : x H A(x)+ B ( x )
-A : x
-A(x)
AB : z H A(x)B(x)
The coefficients of A+B are obvious; they are the sums of the corresponding
coefficients of A and B. The zero polynomial function is the zero function,
167
Introduction to Mathematics with Maple
168
that is 10 : x H 0. Similarly, the coefficients of - A have opposite signs to
the coefficients of A. The coefficients of AB are obtained by multiplying
through, collecting terms with the same power of x and sorting them in
descending (or ascending) powers of x. If
and
P
= AB,
then
+ an-ibm
There is a clear pattern to the formulae (6.2). In order to subsume them
in a compact formula we set a k = 0 for k > n and bk = 0 for k > r n . Then
we can rewrite Equations (6.2) as
k
pk =
ak-jbj
(6.3)
j=O
for' k = 1 , 2 , . . . .
With these definitions of addition and multiplication polynomials themselves form a ring. In modern mathematics there is a need to regard polynomials as algebraic expressions. The precise meaning of this will be made
clear in the next section. Treating polynomials as algebraic expressions is
needed because the coefficients of a polynomial function, generally speaking, are not uniquely determined by the polynomial function. For instance,
the polynomial x I-+ x2 x over GF(2) and the zero polynomial x I-+ 0
+
lFor Ic
> n + rn, all pl, = 0.
Polynomials
169
are equal as functions but they do not have the same coefficients. Later
in this chapter we prove that if the ring M is a field which contains the
field of rationals, then indeed, equal polynomial functions have the same
coefficients.
6.2
Algebraic viewpoint
A sequence of elements from M constitutes an unending polynomial, more
precisely an unending polynomial over M . To indicate that we regard a
sequence n I-+ a,, n E No as an unending polynomial rather than as a mere
sequence we write (an),or more explicitly
Two unending polynomials (a,) and (b,) are equal, by definition, if and only
if ak = bk for all k E No. For an unending polynomial (an) the elements ak
are referred to as coefficients. The reason for this name becomes apparent
later in this section. The sum and product of two unending polynomials
are defined as
where pk are defined as in (6.3).
Unending polynomials are also (more commonly) called formal power
series, and a different notation is used too. Instead of (a,) one writes
One problem with this notation is that X " is not a power of an element
from M , it is a purely formal symbol indicating term number n 1 of the
sequence. An advantage of this notation is that it facilitates multiplication.
To multiply two unending polynomials, we multiply term by term, replace
X k X 1by Xk+' and collect terms with the same 'power' of X . For instance
+
+
(1 2~
+ 3 x 2 + + (n + l)xn+ . . . )
*
(1 - 2 x
+ 3 x 2 + + (-l)"(n + l>xn
+ -)
= 1 + 2 x 2 + 3 x 4 + - - - + (n+ 1
* * *
+
) ~ ~ - "-
Some examples on multiplication of unending polynomials are given in Exercises 6.2.1 and 6.2.2. Addition of unending polynomials satisfies axioms
170
Introduction to Mathematics with Maple
to A 4 from Table 4.1. The role of the zero element is played by the
zero unending polynomial 0 OX - - - OXn - - - , which we denote by
0 . Axiom M 1 is also obviously satisfied, since
A1
+
+ +
k
k
j=O
j=O
+
Now we prove that multiplication of unending polynomials is associative.
Using formula (6.3) for multiplication of unending polynomials we have
n n-i
Both sums in (6.7) and (6.8) consist of terms of the form a&c,, where a ,
p and y a;e non-negative integers with a! ,B y = n, and each such term
appears in the sum exactly once. Consequently the sums are equal and the
multiplication is associative. It is easy to see that Axiom D from Table 4.1
is also satisfied. It follows that unending polynomials form a ring.
A polynomial is a formal power series which has only a finite number
of coefficients distinct from zero. In other words ( a n ) is a polynomial if
there is a natural number N such that a k = 0 for k > N . Similarly to the
notation (6.6) we write
+ +
We shall denote the polynomial in (6.9) briefly as A ( X ) . The coefficient
a0 is called the absolute term, the coefficient a N is said to be the leading
coefficient. The polynomials over a ring M themselves form a ring which is
denoted by M [ X ]. Polynomials are a special class of unending polynomials:
therefore to show that they form a ring it suffices to show that the sum and
product of two polynomials have only a finite number of coefficients distinct
from zero. This is obvious for the sum. For the product of the polynomials,
(a,) with a k = 0 for k > n and ( b n ) with bk = 0 for k > m, the general
N
term is
aN-ibi.
If N > n + rn then (at least one of)
i=O
This completes the proof that polynomials form a ring.
aN-i
or bi is zero.
Polynomials
171
If a, # 0 and ak = 0 for k > n then the degree of A(X) is defined to
be n; in symbols degA(X) = n. However, the zero polynomial which has
all coefficients zero and which is denoted by 0 has no degree. The non-zero
elements of M can be regarded as polynomials of degree zero. Indeed, as
far as addition or multiplication is concerned, polynomials of degree zero
behave exactly like elements of M .
. . .) + (b, 0, 0, .) = (a + b, 0, 0, . . .),
( a , o,o, ...) (b, 0, 0, ...) = ( a - b ,0, 0, ...),
(a, 0, 0, . . .) - (bo, b l , . . .) = (a bo, a b l , . . .).
(a, 0, 0,
*.
*
If we have two algebraic structures, such as two fields or two rings,
and if the elements of these structures behave, as far as addition and multiplication are concerned, in the same way, we say that these structures
are isomorphic. More precisely, if M and M' are two rings (fields) we
say that they are isomorphic if there is a bijection I : M I-+ M' such
that I("
b) = I ( a ) I ( b ) and I ( a b) = I ( a ) - [email protected]). The bijection I
is called an isomorphism. In the situation above, for a E M we have
I ( a ) = ( a , 0, 0, . . .). In abstract algebra and in this book we shall consider
two isomorphic structures as identical. In this spirit we say that the set of
polynomials of degree zero together with the zero element is M .
It is easy to see that
+
+
+ B(X)) 5 Max( degA(X),degB(X))
deg (A(X) - B(X)) L deg A(X) + deg B(X),
deg (A(X)
(6.10)
(6.11)
+
provided deg (A(X) B(X)), deg A(X) and deg B(X) are defined. The
case B ( X ) = 1 - A(X) shows that strict inequality can occur in (6.10).
Exercise 6.2.5 gives an example showing that strict inequality can occur in
(6.11). If, however, M is a field then the equality holds in (6.11): indeed,
the coefficient of Xn+min this case must be different from zero. Hence we
have
deg(A(X) - B(X)) = deg A(X)
+ deg B(X),
(6.12)
For more details see Exercises 6.2.4-6.2.6. The ring M need not have an
element 1 (the unit element for multiplication) of Axiom M3 from Table
4.1; for example, the ring of even integers has no unit element. If M does
have a unit element then we can denote the polynomial
(0, 1, 0, . . .o, . . .)
Introduction to Mathematics with Maple
172
by X and then X . X = X 2 is the polynomial
(0, 0, 1, 0,
... 0, ...).
-
Generally
X” = (0, 0 , . . ., 1, 0 , * ..).
n zeros
X is called the indeterminate and it behaves, with respect to addition and
multiplication, like an element of M or an element of some ring containing
M . The notation
now becomes less formal and more meaningful.
Each term akXk
is a product of a polynomial ak of degree zero with the polynomial
X k , and the original polynomial itself is a sum of polynomials a n X n ,
an-1 xn-1, . . . , a l X , ao.
The following rule looks obvious but still requires proof2.
Cancellation rule:
If F is a field and A ( X ) ,B ( X ) , C ( X ) E F [ X ] with A ( X ) not a zero
polynomial and if
A(X)B(X)=A(X)C(X)
then B ( X ) = C ( X ) .
Proof.
Since F [ X ] is a ring we have
A ( X ) ( B ( X )- C ( X ) )= 0.
(6.13)
If B ( X ) - C ( X ) were not the zero polynomial then the leading coefficient
on the right hand side of (6.13) would not be zero and Equation (6.13)
0
could not hold. Consequently B ( X ) = C ( X ) .
With the polynomial A ( X ) associate the polynomial function A defined
on M
A :x
I-+
anxn
+ anal
xn-l
+ - + ao.
* *
(6.14)
Let us make the notation very clear: A is the function defined in (6.14),
A ( x ) is the value of this function at x and A ( X ) is the polynomial (6.9).
This notation is convenient because we have the following rule:
2This rule is false if F [ X ] is replaced by M [ X ] .
173
Polynomials
Subst it ut ion rule:
If A ( X ) and B ( X ) are two equal polynomials ouer M and z is an element
of M or an element of a ring containing M then A(z) = B ( z ) .
Proof. A(X) and B ( X ) are equal polynomials which means that when
both are written in order of ascending powers of X, and each term of the
form C k X k appears exactly once, they have the same coefficients. Consequently the function values of A and B are evaluated in exactly the same
0
way and must be equal.
Exercises
Multiply the following unending polynomials
Exercise 6.2.1
+ + +
+
+ +
(1) (1 x - xn - . ) (1 - x + x2 - (-1)"Xn + .)
(2) (1+2X+X2+0X3+~~
.+OX"+. - .).(l-X+X2+. -+(-l)"X"+. .)
*
*
*
* *
* *
*
+ +
@ Exercise 6.2.2
If A 4 has a multiplicative unit element 1 then 1 OX
- .+OX"+- - - is the multiplicative unit in the ring of unendingpolynomials:
this unit is also denoted by 1. If A ( X ) = a0 a l X
... a n X n
with a0 = 1 then there exists an unending polynomial B(X) such that
A ( X ) - B ( X ) = 1. Prove this. Show also that if M is a field then the
polynomial B(X) exists if merely a0 # 0.
+ +
+
+
@ Exercise 6.2.3
An easy way to multiply two polynomials A(x) and
B(x) of degree m and n, respectively, is to use the Maple command3
taylor(A(x)B(x)
,m+n) ;convert(%,polporn) :
Veri& this for A(x) = x2 - 1 and B(x) = xlo0 xg9 - - - 1.
+
+ +
Exercise 6.2.4
The relation x = y mod m was introduced in Example
3.1. Denote the equivalence class containing an element x as 5. Define
Z ij = x y and 5 - ij = xyy. Show that these definitions are correct, that
is, if x = x' mod m and y = y' mod m then x y = x' y' mod m, with
the corresponding relationship for the product. Show that the equivalence
classes modulo m with addition and multiplication j u s t defined form a ring.
Show also that this ring has a unit element satisfying axiom M3 from Table
4.1. For greater clarity we denoted the classes with a tilde sign. It is however
common to omit the tilde sign, and denote the class by the number which
it contains.
+
+
+
+
3The command convert is needed only for further computations: if you want only
the result of multiplication you can omit it.
Introduction t o Mathematics with Maple
174
@
Exercise 6.2.5
If M is the ring of integers modulo 4 then the product
of two polynomials 1 SX and SX of degree one is a polynomial of degree
one. Verify this. Give your own example of M and polynomials of second
and third degree for which the product (a) is of third degree; and (b) is the
zero polynomial.
+
@ Exercise 6.2.6
A ring M is called an integral domain if
(a) it has a multiplicative unit element,4 as in axiom M3.
(b) if z, y E M then z # 0, zy = 0 + y = 0.
Show that
(1) a field is an integral domain;
(2) if M is an integral domain then Equation (6.22) holds;
(3) if M is an integral domain then so is M [XI.
@ Exercise 6.2.7
Prove that an integral domain having only a finite number of elements is a field.
@ Exercise 6.2.8
Let p be a prime. Prove that the equivalence classes
mod p with addition and multiplication defined in Exercise 6.2.4 form a
field.
Exercise 6.2.9
Algebraic expressions of the form
(6.15)
+
can be introduced as pairs ( k , A ( X ) ) where k E N and A ( X ) = a-k
a-k+lX
- - - aoXk - - is an unending polynomial. If the coefficients
ai E R, we call such pairs formal L-series. We say that two L-series
( k , A ( X ) ) = ( l , B ( X ) ) are equal if and only if X z A ( X ) = X k B ( X ) as
unending polynomials. We define addition and multiplication as follows:
+ +
+
+ ( I , B ( X ) ) def ( k + I , X z A ( X )+ X k 1 3 ( X ) ) ,
( k , A ( X ) )- (1, B ( X ) )ef( k + I , A ( X ) - B ( X ) ) .
( k ,A ( X ) )
Show that with these definitions of addition and multiplication the formal
L-series constitute a field. The set of elements of the form (1,X - A ( X ) )
with A ( X ) = a 0 - X
- - is isomorphic to R. [Hint: The unit element
of multiplication is (m,X m ) . To find a multiplicative inverse use Exercise
6.2.2.]
+
+
4Some authors do not include this axiom in the definition of an integral domain.
Polynomials
175
Exercise 6.2.10
Let a be the first non-zero coefficient of an unending
polynomial A ( X ) , that is A ( X ) = a x "
am+1Xm+'
- . In the field of
formal L-series (see the previous exercise) define (k,A ( X ) ) to be positive
if and only if a > 0. Show that the positive formal L-series have the
properties of positive elements mentioned on page 103 and conclude that
formal L-series constitute an ordered field. Show that this field lacks the
Archimedean property.
[Hint: For A ( X ) = 1+ O X . . . +OX" - - - and n E N prove (1,A ( X ) ) > n.]
+
+-
+
Let S be a non-empty set and 5 the set of functions
f : S +-+ R. Define (f g)(z) = f(z) g(z) and (f - g)(z) = f(z)- g(z).
Show that with these definitions 5 becomes a ring which has a multiplicative
unit. Show that if S contains at least two distinct elements then 5 is not
an integral domain.
@ Exercise 6.2.11
6.3
+
+
Long division algorithm
The next theorem describes so-called long division. We have already used
it in Section 1.5.2 and readers have probably encountered it many times
before, however, most likely only for F = R and without a proper proof.
~
Theorem 6.1
If P ( X ) ,H ( X ) are polynomials over a field F and
H ( X ) is not the zero polynomial then there exist polynomials Q ( X ) E
F [ X ]and R ( X ) E F [ X ]such that
P ( X >= H ( X ) Q ( X )+ R ( X )
(6.16)
and either deg R ( X ) < deg H ( X ) or R ( X ) is the zero polynomial. The
polynomials Q ( X ) ,R ( X ) are uniquely determined.
Proof. First we prove uniqueness. If besides Q ( X ) and R ( X ) there are
Q 1 ( X ) and R 1 ( X ) with the same properties then
H ( X ) [ Q ( X) Q l ( X ) ]= R l ( X ) - R ( X ) .
(6.17)
If Q ( X )- Q1 ( X )were not the zero polynomial then neither would R1 ( X )R ( X ) be and by (6.10) and (6.12)
Introduction to Mathematics with Maple
176
This contradicts (6.17) and consequently Q(X) = Q1 (X) and then R(X) =
R1 (X).
Now we prove existence. Let
with pn # 0 and hm # 0. If rn > n or P ( X ) = 0 then the theorem
is true, with Q(X) = 0 and R(X) = P(X). In the remaining case we
proceed by complete induction. The assertion is true if degP(X) = 0
with R(X) = 0 and Q(X) = poh;' = P(X)/H(X). Let us now consider
the polynomial p(X) = P(X) - pnhklXn-mH(X). The polynomial p(X)
has been chosen so that degp(X) < degP(X) or possibly p(X) = 0. In
the latter case R(X) = 0 and Q(X) = anhmlXn-m. In the former case
either by induction hypothesis or because degp(X) > degH(X) there exist polynomials G(X) and R(X) such that p(X) = H(X)G(X) R(X)
with degR(X) < degH(X) or R(X) = 0. It follows that P(X) =
0
H(X)[pnh,lXn-m
G(X)] R(X), as required.
+
+
+
Maple can find the polynomials Q(X) and R(X). The commands are
quo(P(X) ,H(X) ,X) for the quotient and rem(P(X1 ,H(X) ,X) for the remainder. Maple works in the field which is implied by the coefficients, this
means, in the smallest field which contains the coefficients of P(X) and
H(X). If, for example, P ( X ) and H(X) have integer coefficients then
Q(X) and R(X) are found in Q[X]. In the following example the field is
formed by numbers of the form a b& with a, b E Q.
+
-31J z - -91+ -31x
>
rem(Xn2+sqrt(2)*X+sqrt (2) ,3*X+l,X);
2
-3 h + g
1
One can obtain both the remainder and the quotient by one command by
adding an additional fourth argument, either q or r. However this argument
must be enclosed in ' '. For instance
Polynomials
177
4
x+1
or
The capitalized commands Quo and Rem work the same way, but the
coefficients can belong to the field of equivalence classes mod p , where p is
a prime. This is illustrated in the next example.
>
Quo(x~10,6*~-1,x,’r’)mod 7;r;
62’
+ x8 + 6z7+ x6 + 6 x 5 + z4+ 6z3+ z2 + 6 z + 1
1
For polynomials of zero degree the Quo command can be used to find
the multiplicative inverse of an element in the field of integers mod p . The
next example shows how to find the multiplicative inverse of 91 mod 97.5
irem is then used to check the result.
>
Quo(l,9l,X) mod 97;
5 0 f course, there is no remainder.
Introduction to Mathematics with Maple
178
16
>
irem(l6*91,97);
1
Exercises
@ Exercise 6.3.1
If F is replaced by Z in Theorem 6.1 then polynomials
Q ( X ) and R ( X ) need not exist. Consider the following example: P ( X ) =
X 2 + 1 and H ( X ) = 2 X . However, polynomials in Z[X]
lie also in Q [ X ]and
the theorem can be applied to Q [ X ] .Obviously, the required polynomials
do not have integer coefficients. Prove all these assertions.
@ Exercise 6.3.2
Prove that numbers of the form a
and b E Q form a field.
[Hint: Only Axiom M4 is not obvious.]
+b
d with a E Q
Exercise 6.3.3
Let M be the ring of equivalence classes modulo 4. (See
Exercise 6.2.4.) Considering P ( X ) = 2 X 2 , Q l ( X ) = X and Q z ( X ) = 3X
show that Equation (6.16) is satisfied with two distinct polynomials Q ( X ).
+
+ +
I f H ( X ) = X"
h,-lX"-'
- - ho and F is merely
an integral domain then the assertion of Theorem 6.1 remains valid. Prove
it!
@ Exercise 6.3.4
Exercise 6.3.5
Quo command.
6.4
Find the multiplicative inverse of 2 mod 101 using the
Roots of polynomials
If F is a field, P ( X ) E F [ X ]then a belonging to some field containing F is
a root of the polynomial P ( X ) or of its associated function P if P ( a ) = 0.
A root of P ( X ) or of P is also called a zero of P ( X ) or of P , respectively.
Theorem 6.1 is often used for H ( X ) = X -a, in which case Equation (6.16)
reads
+
P ( X ) = ( X - a ) Q ( X ) P(u).
(6.19)
Polynomials
179
It follows that if a is a zero of P ( X ) then X - a divides P ( X ) . From this
we have
Theorem 6.2 If F is a field then the polynomial P ( X ) E F [ X ]of
degree n has at most n distinct roots in any field which contains F .
Proof. We proceed by induction. The theorem is obviously correct for
polynomials of degree zero, If P ( X ) of degree n has no roots then there is
nothing more to prove. If it has a root a then by Equation (6.19) we have
P ( X ) = ( X - a ) Q ( X ) ,where Q ( X ) is of degree n - 1. By the induction
hypothesis Q ( X ) has at most n - 1 roots and consequently P ( X ) has at
most n roots.
0
If M is merely a ring then P ( X ) E M [ X ]of degree n can have more
roots than n: one such example is given in Exercise 6.4.1. Obviously a polynomial of degree n can have fewer than n roots, for example, the polynomial
of the second degree X2-2X+1 has only one root. If P ( X ) = ( X - a ) " Q ( X )
with Q ( a ) # 0 we say that a is a root of P ( X ) of multiplicity k . For k = 2
the root is called a double root, for k = 1 the root is simple. It can be
shown that for any polynomial in F [ X ]of degree at least one there exists
a field F 3 F in which the polynomial has a root. The proof cannot be
given here but we shall make some remarks concerning this result about
field extensions in Chapter 7. Theorem 6.2 has the following very important
consequence.
~~
Theorem 6.3
~
If F is a field and P ( X ) E F [ X ]is of the form
P ( X )= p , x n
+ ... + p o
+
and has n 1 distinct roots then p i = 0 for i = 0 , 1 , . . . ,n. I f F has
infinitely many elements and P and Q are two polynomial functions
with P ( z )= Q(z) for everyz E F then they have the same coefficients.
Proof.
P ( X ) - Q ( X ) must be the zero polynomial.
0
Exercises
@ Exercise 6.4.1 This exercise uses the notation of Exercise 6.2.11. Let
A c S , A # S and assume that A has infinitely many elements. Show that
the polynomial
with B c A.]
ls\AX
E
g [ X ]has infinitely many roots. [Hint: X = 1~
180
Introduction to Mathematics with Maple
Exercise 6.4.2 Show that if F is finite then there exists a polynomial
which is not the zero polynomial and for which every element in F is a
root.
6.5
The Taylor polynomial
In this section we explore Equation (6.19) further. The coefficients of
Q ( X ) = qn-lXn-' +...+qo and the value P ( a ) in Equation (6.19) can be
easily obtained by comparing the coefficients.
(6.20)
P i = -aql
Po = -aqo
+ qo
+ P(a).
One finds qn-1 from the first equation. qn-2 can be found from the second equation, and by continuing with this process we can find all qk and
also P(a). The whole computation can be arranged in the following array,
known as Horner's scheme. Firstly, we write all coefficients of P ( X ) in the
first row. It is important to write all coefficients, even the zero ones. Then
we leave one row empty and copy p n in the first column of the third row.
With this done we multiply each element of the third row by a , place it
in the next column in the row above, add up the elements in that column
(by using equations (6.20) in the form qn-2 = pn-1
aqn-l, etc.) and
successively fill in the whole array.
+
Pn
Pn-1
aqn-1
Pn-2
aq,-2
...
Pl
Po
... aq1 aqo
(6.21)
We can divide Q ( X )by X - a , the quotient again by X - a and by continuing
this process we form the following scheme.
181
Polynomials
(6.22)
The coefficients of Q k ( x ) , which we denote by qk,a, can be obtained by
continuation of the Horner scheme.
(6.23)
...
Finally, by working backwards through the schemes (6.22) and (6.23), we
express P ( X ) as a sum of powers of X - a.
The right hand side of this equation is called the Taylor polynomial or the
Taylor expansion of P ( X ) around the point X = a. In the scheme (6.23)
we have g l , n - l = 92,n-2 = - - * - 9n-1,1 = Qn,o = 4n = P n .
To illustrate the procedure we expand X4 4 X 2 3 X 2 in powers of
X - 2 with the coefficients lying in the field of integers mod 5.
+
+ +
Introduction to Mathematics with Maple
182
1 0 4 3 2
2 4 1 3
1 2 3 4 0
2 3 2
1 4 1 1
2 2
1 1 3
2
1 3
As a result we have
x4+4x2+3x
+ 2 = (X
- 2)
+ 3(X - 2)2 + 3 ( X - 2)3+ (X - 2)4.
The coefficients qk can be determined in other ways than by the scheme
k
(6.23). One can write Xk = ((X - a ) a ) and use the binomial theorem
(Theorem 5.4). Such a procedure might be advantageous if the degree
of P(X) is very small. Collecting the absolute terms leads to the result
qo = P ( u ) which we already know, collecting the coefficients of X - a leads
to
+
We can take some inspiration from this formula and define a derivative of a
polynomial (derivatives are treated from a calculus point of view in detail
in Chapter 13) by
The formula for
q1
then becomes
q1 = D P ( a ) .
The following formulae are rather important.
Polynomials
183
Equation (6.26) follows easily from the definition of derivative. To prove
(6.27) we write
+
+ ( X - a)2W1(X- a ) ,
P 2 ( X ) = P'(a) + ( D P z ( a ) ) ( X- A ) + ( X - u ) ~ W ~ -( X
a),
P l ( X ) = P I ( u ) ( D P l ( a ) ) ( X- A )
where we abbreviated by W1 and W2 some polynomials whose form is unimportant for the proof. It follows that
P l ( X ) P 2 ( X )= P1(a)P2(a)
+ [ P 2 ( 4 D P 1 ( 4+ P1(a)DP2(a)]( X - a ) + ( X - a ) 2 W ( x- a ) ,
where we again denoted by W some polynomial. From this equation the
formula (6.27) follows. Using induction it follows from (6.27) that D ( X c ) =
~ k ( X - c ) ~ - ' .This can also be verified directly from (6.25). Defining
D k P ( X )Ef D ( D k - l P ( X ) )and applying D successively to Equation (6.25)
and then substituting X = a we obtain
D 2 P ( a )= 2 1- 4 2 ,
D 3 P ( a )= 3 - 2 - 1 43,
*
(6.28)
D"P(a) = n! qn.
From these equations it is easy to find the coefficients q k . Some caution is
needed: generally speaking, all coefficients of q k in (6.28) are distinct from
zero6 only if F contains N. This, of course, happens in the most important
cases when F = Q or R. The Taylor formula then takes the form
(6.29)
The importance of this equation lies in the fact that it gives explicit formulae for the coefficients of powers of X - a. In practice, however, the process
is sometimes reversed: one finds D k P ( a )as q k by using the scheme (6.23).
If one has a computer and Maple available everything becomes easy. The
command
taylor(P(X) ,X=a,n)
6For example n! = 0 mod p if n 2 p .
Introduction to Mathematics with Maple
184
produces expansion (6.24) with the coefficients q k evaluated. It does that
even if the coefficients of P ( X ) contain some parameters. It is advisable, as
we mentioned earlier, to add the command convert. Hence the complete
command is then
taylor(P(X) ,X=a,n):convert(%,polynom) ;.
Exercises
Exercise 6.5.1
of x3for a = -1.
Use the binomial theorem to find the Taylor expansion
Exercise 6.5.2
polynomials
Find the Taylor expansion at a = -2 for the following
(1) P ( X ) =
x5;
(2) P ( X ) = x5
+ 2x4 + 3 x 2 + 2x + 1.
Decide in each cme which formula is more convenient to use, (6.23) or
(6.29).
Exercise 6.5.3
Use Maple to find the Taylor expansion of X 2 at a =
2b/3.
6.6
Factorization
In this section we shall assume that F always denotes a field. If not stated
otherwise every polynomial is automatically assumed to belong to F [ X ].
In Section 2.5.1 we established some results on divisibility in a Euclidean
ring and applied it to polynomials with rational coefficients. Since F [ X ]is
also a Euclidean7 ring these results remain valid. We recapitulate some of
them. The polynomial P ( X ) is a multiple of H ( X ) and H ( X ) is called a
divisor of P ( X ) if there exists a polynomial Q ( X ) such that
(6.30)
The word factor is often used instead of divisor. If H ( X ) is a divisor of
P ( X ) then we also say that H ( X ) divides P ( X ) ,or that P ( X ) is divisible
by H ( X ) . If the polynomial D ( X ) is a divisor of both PI ( X ) and P 2 ( X )
it is called a common divisor: if every other common divisor divides D ( X )
then D ( X ) is called the greatest common divisor of P l ( X ) and P 2 ( X ) . For
~
7With N ( r ) equal to the degree of the polynomial.
Polynomials
185
any two polynomials P l ( X ) # 0 and P 2 ( X ) in F [ X ]the greatest common
divisor always exists. It is the polynomial of the smallest degree which is
of the form
with H l ( X ) and H 2 ( X ) in F [ X ] .The greatest common divisor is uniquely
determined in the following sense: if D l ( X ) and D 2 ( X ) are two greatest
common divisors of the same two polynomials then there exists an element
in c E F such that D1 ( X ) = c D 2 ( X ) .
A polynomial P ( X ) of degree n 2 1 is called reducible over (in) F if
there exists a polynomial H ( X ) E F [ X ]of degree greater than zero but
less than n such that Equation (6.30) holds. A polynomial P ( X ) of degree
n 2 1 is irreducible over F if Equation (6.30) implies that d e g H ( X ) is
either n or 0. It is important to realize that the concept of being reducible
or irreducible depends not only on the polynomial P ( X ) but also on the
field in question. For example, X 2 - 2 = ( X - &)(X
&) is reducible in
R [ X ] ,but it is left as an exercise to prove indirectly that it is irreducible
in Q [ X ] .It follows from the definition that every polynomial of degree one
is irreducible over F [XI.
Our aim is to show that any polynomial can be decomposed into a
product of irreducible polynomials and that such a decomposition is in
a certain sense unique. To this purpose we first list some properties of
irreducible polynomials.
+
(a) If P ( X ) is irreducible in F [ X ]and 0 # c E F then c P ( X ) is irreducible.
(b) If H ( X ) is a divisor of an irreducible P ( X ) then either N ( X ) is of
zero degree or H ( X ) = c P ( X ) for some nonzero element of F.
(c) If (6.30) holds, and P ( X ) is divisible by some irreducible polynomial
D ( X ) ,then D ( X ) is a divisor of either H ( X ) or Q ( X ) .
If D ( X ) does not divide H ( X ) then the greatest common divisor of
D ( X ) and H ( X ) is 1 and by (6.31) we have
+
1= D ( X ) A ( X ) H ( X ) B ( X )
for some A ( X ) and B ( X ) . Multiplying by Q ( X ) gives
186
Introduction to Mathematics with Maple
The right hand side is divisible by D ( X ), and so then is Q ( X ).
(d) If P ( X ) = Q l ( X ) Q 2 ( X ) Q n ( X ) and P ( X ) is divisible by an irreducible F ( X ) then F (X ) is a divisor of one of the Qi ( X ).
This follows by an easy induction from (c).
(e) Every polynomial of degree at least 1 is divisible by some irreducible
polynomial.
The statement is true for polynomials of degree 1. We proceed by
complete induction. If the polynomial is not irreducible then it is a
product of two polynomials of smaller degree and by the induction
hypothesis one of these is divisible by an irreducible polynomial and
consequently so is the original polynomial itself.
6
.
If d e g P ( X ) > 0 then by (e) we have P ( X ) = P I ( X ) Q l ( X )with an
irreducible P l ( X ) . If Q l ( X ) is not of zero degree, then again Q l ( X ) is
divisible by some irreducible polynomial, by continuing with this process
we must finally arrive at a polynomial of zero degree and we have
P ( X ) = P1 ( X ) P 2 ( X ) P l ( X ) .
* * *
(6.32)
where all the polynomials Pi ( X ) are irreducible.
Theorem 6.4
For every polynomial P ( X ) E F [ X ] of degree at
least one there exist polynomials P i ( X ) , i = 1, 2, . . . , I , irreducible
over &"XI, such that (6.32) holds. If
P ( X ) = Q l ( X ) Q 2 ( X ). * . Q m ( X )
(6.33)
is another representation of P ( X ) with all Q i ( X )irreducible over F [ X ]
then m = 1 and, with appropriate renumbering (if necessary), there
are non-zero ci E F such that P i ( X ) = c i Q i ( X ) for i = 1, 2, . . . , 1.
An informal way of stating the theorem in an easy to remember form
is: the decomposition of a polynomial into irreducible factors is unique.
Proof. The proof is inductive. The theorem is true if P ( X ) is of degree
one, because such polynomials are irreducible. Let us now assume that the
theorem is true for all polynomials of degree less than n and let P ( X ) be
of degree n. Since P l ( X ) divides P ( X ) it must, by (d), divide one of the
Q i ( X ) . Renumbering it Q l ( X ) (if required, and the others correspondingly), we have Q l ( X ) = c l P l ( X ) with a non-zero c1 because Q l ( X ) is
irreducible over F [ X ]and P ( X ) is of positive degree. Substituting this
187
Polynomials
into Equation (6.32) we obtain
P 1 ( X ) P 2 ( X ). . . f i ( X ) = c I P I ( X ) Q ~ ( X
...Qm(X))
It follows that
P 2 ( X )-
*
- e ( X )= (ciQ2(X)) - Q m ( X ) * *
Since the polynomials in this equation are of degree less than n the theorem
can be applied: it follows that I - 1 = rn - 1, consequently I = m and also
P 2 ( X ) = c2Q2(X), - .. , 4 ( X ) = czQz(X).
0
Remark 6.1
The theorem does not assert that the polynomials P i ( X ) are
distinct. If we require that the leading coefficients in all polynomials are 1and
group together identical polynomials, the factorization takes the form
P ( X ) = .(PI (X))"' ( P . ( X ) ) n z
* *
- (PS(X))"".
The polynomials P i ( X ) ,the natural numbers ni and
F , are uniquely determined,
(6.34)
c, a non-zero element of
In Theorem 6.4 we established the existence and uniqueness of the decomposition of a polynomial into irreducible factors. Actual finding of the
factors can be quite difficult, even in (?[XI. Merely to decide whether or
not a polynomial is irreducible can also be quite difficult. Muple can be of
great help with these tasks.
The command to use is
f a c t o r ( P ( X ) , option) ;
A shorter name can be given for P ( X ) using the assignment operator.
If o p t i o n is left out Muple tries to factorize the polynomial in the smallest
field which contains the coefficients. For option we discuss here three
possibilities.
One types in the word real. The polynomial is then factored in R [ X ] ;
A number or a list of numbers or a set of numbers is inserted for
option. The number or each number in the list or set should be a
root of a polynomial with rational coefficients, for example
The factorization is carried out in the smallest field which contains
the given number or all the numbers in the list or set, respectively.
Similar to (b) but the numbers are given with RootOf. For example,
instead of fi as in (b) the option is RootOf ( z 2- 2).
a,m.
The following session illustrates the use of f a c t o r .
Introduction to Mathematics with Maple
188
>
>
# to save typing we name the polynomial;
p:=xn4+5*x-3+5*x+1:
>
>
#
then we try to factor it;
factor(p1;
x4
+ 5 x3 + 5 x + 1
Muple returned the polynomial to be factored. This means that the
polynomial is irreducible over the rationals. Now we try to factor the
polynomial in the smallest field which contains square roots of the first
few primes. First we produce a list of these roots, the Maple command
ithprime produces what it promises, the i-th prime.
>
L:=[seq(sqrt(ithprime(i)) yi=l..5)1;
>
>
#
Then we try to factor again;
factor(p,L);
4
(2 x2 + 5 x
+ fih
x + 2) (2 x2
+ 5x
-
dii h x + 2)
We were lucky and found a factorization. A natural question arises as
to how many primes should we include in the list. It is hard to guess; it is,
however, not advisable to make the list too long. It can take a long time
or Muple can refuse t o do it, because the task is just too big. Finally, we
factor the polynomial over the reals.
>
factor(p,real);
(x+ 5.179201362) (x+ .1930799616) (x2- .3722813232 x + .9999999999)
Two things should be noted. Firstly, the factorization uses floating
point approximations and is not exact: it is not an exact factorization in
189
Polynomials
the smallest possible subfield of reals. Secondly, the polynomial
2x2
+ 52 + f i h x + 2
can be factorized by solving the quadratic equation and a complete exact
factorization in the smallest possible subfield of reals obtained.
>
solve (2*xn2+5*x+1in(1/2) *3-(1/2)*x+2) ;
-5-1
4 4
4
JLZGZYZ, 54
----1
4
f i f i - 14 J
4
2
a
Denoting the first root as a and the second as b we then have
p = (x - a)(x - b)(2x2
+ 5x - f i h x + 2).
The capitalized command F a c t o r works similarly: it factorizes a polynomial in the field of polynomials with coefficients integers modulo a prime.
>
Factor(x^4+1) mod 101;
(x2
+ 10) (x2 + 91)
We are unable to prove it here but it is an interesting fact that the
polynomial X4 1 is reducible in the field of integers mod p for every
prime p and is irreducible over Q.
+
Exercises
Exercise 6.6.1 Let F be a field and Pl(X),
P2(X) two polynomials in
F I X ] o f degree n and m respectively. Assume that the greatest common
divisor of PI( X ) and P2(X) is o f the form (6.31). Prove that the polynomials H I (X) and H2 ( X ) can be so chosen that deg H1 (X)<_ m - 1 and
degH2(X) 5 n - 1.
Exercise 6.6.2
Find the greatest common divisor o f each of the following pairs of polynomials
+
+ +
+ +
+
+
+ +
+
+
(1) x6 3x5 3x4 3x+4x2 62 4, x4
(2) x6 - 3x5 x3 2x2 - x 3, x3 - 2x2
(3) X1O0 x2 + p x q, x2 + p x q;
+ x3 - 3x2
+ 32 - 1;
-
x
+ 2;
190
Introduction to Mathematics with Maple
in the field Q[x]and in the fields of equivalence classes mod 3 and mod 5.
Exercise 6.6.3 Prove that the polynomial x2 +px+ q is irreducible over
the reals i f and only ifp 2 - 4q < 0.
+
Exercise 6.6.4 Show that the polynomial X4 1 is irreducible over Q
and is reducible in the smallest field which contains
a.
Exercise 6.6.5
Find all integers a for which the polynomial x3 + ax + 1
is reducible in Q[x].[Hint: fl must be the roots.]
+ + + + + + + +
Exercise 6.6.6 Factorize X 8 X7 X 6 X 5 X4 X 3 X 2 X 1in
Q [ X ]and over the field which contains the root of the polynomial X2+X+1.
Chapter 7
Complex Numbers
We introduce complex numbers; that is numbers of the form a+ bz
where the number z satisfies z2 = -1. Mathematicians were led to
complex numbers in their efforts to solve so-called algebraic equations; that is equations of the form u,xn an-lxn-'
- - - a0 = 0,
with arc E R, n E N. Our introduction follows the same idea although in a modern .mathematical setting. Complex numbers now
play important roles in physics, hydrodynamics, electromagnetic
theory, electrical engineering as well as pure mathematics.
+
7.1
+ +
Field extensions
During the history of civilisation the concept of a number was unceasingly
extended, from integers to rationals, from positive numbers to negative
numbers, from rationals to reals, etc. We now embark on an extension of
reals to a field in which the equation
J2+1=0
(7.1)
has a solution. This will be the field of complex numbers.
Let us consider the following question. Is it possible to extend the field
of rationals to a larger field in which the equation E2 - 2 = 0 is solvable?
The obvious answer is yes: the reals. Is there a smaller field? The answer is
again yes: there is a smallest field which contains rationals and
namely
the intersection of all fields which contain Q and the (real) number
Can this field be constructed directly without using the existence of R?
The answer is contained in Exercises 7.1.1-7.1.3.
The process of extension of a field can be most easily carried out in the
case of a finite field. Let us consider the following problem: is it possible
a,
191
a.
Introduction to Mathematics with Maple
192
to extend G F ( 2 ) in such a way that the equation'
has a solution in the extended field. The following Table 7.1 describes
addition and multiplication in GF(2).
Table 7.1 Addition and multiplication in G F ( 2 )
Add
Multiply
Denoting by k the solution of the equation we shall try to augment
these tables by additional rows and columns in order to obtain addition
and multiplication tables for the extended field. Firstly, we add a row and
column headed by k . The entry in the second row and third column of
the addition table cannot be equal to 0 or to 1 or to k . So it is another
'new' element, let us denote it by 1 k and augment the table by another
column and another row. (See Table 7.2.) It is now easy to fill the rest of
the addition table: remember 1 1 = 0 in G F ( 2 ) and this must also hold
in the extended field.
+
+
Table 7.2 Addition in G F ( 4 )
Add
k
l+k
k
l+k
l+k
k
l+k
0
1
1
0
Filling the augmented multiplication table is a bit more involved and
we use the Equation (7.2) to do it. For instance, k2 = -k - 1 = k 1,
(1 k ) k = k2 k = -1 = 1. (See Multiply in Table 7.3.) If the extension
existed, addition and multiplication would be described by the augmented
+
+
'It is easily checked the the equation is not solvable in G F ( 2 ) .
+
193
Complex Numbers
Table 7.3 Multiplication in GF(4)
Multiply
+
tables. We now reverse the process. For the four elements 0, 1, k, 1 k we
define addition and multiplication by Tables 7.2 and 7.3. The verification
of the field axioms for the new field, which is called GF(4), is easy and we
leave it to the readers.2 We succeeded in our task of extending GF(2) in
such a way that in the extended field the Equation (7.1) has a solution.
Exercises
+
Prove: The set {a b f i ; a E Q, b E Q } c R, together
with the same addition and multiplication as in R constitutes a field. This
field contains Q and 4 and is the smallest field with this property. This
field is often denoted by Q(&). [Hint: ( a + b J z ) ( a - b f i ) ( a 2 - b2)-l = 1.3
@ Exercise 7.1.1
@ Exercise 7.1.2
Let FG be the set of ordered pairs of rational numbers
with the following definitions of addition and multiplication
(a, b)
+
(C,d)
= (a
+ b,c+ d),
+ 2bd, bc + ad).
(a, b)(c,d ) = (ac
Prove that F a is a field.
Q(a)
@ Exercise 7.1.3
Show that the field
from Exercise 7.1.1 is isomorphic to the field FJZfrom Exercise 7.1.2. [Hint: ( a ,b) --+ a b f i ]
+
Exercise 7.1.4
Write down the addition and multiplication tables for
the field of three elements (denoted by G F ( 3 ) ) . [Hint: The third element
must be equal to 1 1. Denote it by 2 (or by 2, if you wish to emphasize
that it is not the natural number 2). Then realize that 2 1 = 0 and
2 x 2 = 1 and the rest is easy.]
+
+
2E.g. the inverse for addition for the element 1
plicative inverse to k is 1 k .
+
+ k is this element itself, the multi-
194
Introduction to Mathematics with Maple
Exercise 7.1.5
Extend GF(3) to a field of nine elements in which the
Equation (7.1) is solvable. [Hint: Use the same method of extending the
addition and multiplication tables which we used for construction of GF(4).]
7.2 Complex numbers
The field of complex numbers C is the smallest field which contain R and
the root of the Equation (7.1). Before we construct it, we examine its
structure, assuming that C exists. Firstly, if P ( X ) is a polynomial with real
coefficients and z a solution to the equation x 2 1 = 0 then all the numbers
P(z) must be in C. We now show that there are no other elements in C by
showing that these numbers3 themselves form a field and so they form the
smallest field which contains the reals and the solution to Equation (7.1).
Only axioms A3, A4, M 3 and M 4 from Table 4.1 need verification. If 2 is
the zero polynomial then axiom A3 is satisfied with the zero element Z ( i ) .
The inverse element to P(z) under addition is -P(z), hence we have A4.
Verifying axiom M3 is also easy: if
+
then U ( i ) is the multiplicative unit. Before we deal with
following statement:
M4
we prove the
If P ( X ) E R[X] then
P(z) = 0
if and only if X 2
(7.3)
+ 1 divides P ( X ) .
The ‘if’ part is obvious. Let (7.3) hold. Dividing P ( X ) by X 2
+ +
P ( X ) = Q ( X ) ( X 2 1) bX
+a,
+ 1 gives
(7.4)
+
with a, b E R. Substituting4 X = z gives a bz = 0. Now b = 0 otherwise
z = -a/b E R. It follows that a = 0 and consequently P ( X ) is divisible by
1.
In order to show that P(z) # 0 has a multiplicative inverse we note
first that P ( X ) is not divisible by X 2 1. Since X 2 1 is irreducible over
R, the greatest common divisor of P ( X ) and X 2 1 is 1 and there exist
x2+
+
+
+
~
3Addition and multiplication of these elements is understood in the obvious way,
namely Pi(%) P2(2)= (Pi P2)(2)and Pi(z)P2(2)
= (PiP2)(2).
4See Substitution rule on page 173
+
+
Complex Numbers
195
polynomials H ( X ) ,H1 ( X ) such that
+
P ( X ) H ( X ) ( X 2 + l ) H l ( X )= 1.
(7.5)
Putting X = 2 shows that H(2) is the multiplicative inverse to P(2).
The similarity in addition and multiplication of numbers P(z) and polynomials with real coefficients leads naturally to the idea of using R [ X ]for
the construction of the complex number field. There is, however, a crucial
difference. Polynomials are equal if and only if they have the same coefficients. As we have just seen P(z) = Q(2) if and only if P ( X ) - Q ( X ) is
divisible by X 2 1. Motivated by this we introduce an equivalence relation
into R [ X ]by saying that P ( X ) and Q ( X )are equivalent if their difference is
divisible by X 2 + 1. We denote that by P ( X ) = Q ( X ) mod ( X 2 + 1). This
relation is indeed an equivalence: it is obviously symmetric and reflexive.
The proof that it is transitive is easy and left as an exercise.
Addition and multiplication in W[X] is consistent with equivalence
mod ( X 2 1). More precisely, if P l ( X ) = & ( X ) mod ( X 2 1) and
Q l ( X ) EE Q 2 ( X ) mod ( X 2 1)then
+
+
+
+
We mentioned in Remark 3.1 that elements of an equivalence class are
sometimes said to be equal. For example, by the equivalence for common
a
2a
fractions we identify - with - In the same vein we identify here X 2 with
b
26 '
-1 since X 2 - (-1) EE 0 mod ( X 2 l), and more generally, we identify
P ( X ) with Q ( X ) if P ( X ) --= Q ( X ) mod ( X 2 1). If, in addition we
replace the summands by equal5 elements the result will be only seemingly
affected: by (7.6), no matter how the result appears, it will always consist
of equal elements. Similarly for multiplication. Since R[X] is a ring, it is
clear that with addition, multiplication and identification just explained the
resulting algebraic structure is a ring. We denote it by C, call its elements
complex numbers and in the course of this section we shall prove that C is
a field. Since we identified X 2 with -1 it follows that the indeterminate X
itself is a solution to Equation (7.1) and as such we denote it by 2. Every
element of C is of the form a b2 with a, b E R. Indeed, by (7.4) we have
cx = P(z) = a bz. We call a , b the real part of Q! and the imaginary part
of a , respectively. In symbols, a = %a, b = %. It is worth noting that
+
+
+
~~~
5That is, equivalent
mod ( X 2
+ 1).
+
Introduction to Mathematics with Maple
196
the imaginary part of a complex number is a real number. If b # 0 then a
is called an imaginary number6: if a = 0 then a is purely imaginary. The
number a - bz is called the complex conjugate of a bz, and we denote it
by 6. It is easy to see that for a , p E C
+
It follows that if P ( X ) is a polynomial with real coefficients then P ( 6 ) =
( P a ) . This is very often used.
Absolute value of a complex number
7.2.1
The number d
m is called the absolute value of the complex number
a = a bz. We denote it by 101. It is always non-negative and if b = 0 then
a is real and the notation is consistent with that introduced in Section 4.3
for the absolute value of real numbers. Obviously la1 2 %a,la1 2 Sa and
+
la1 = 161.
Proof.
d(ap)(aP)=
= IalIPI. To prove (iii) we first note that ap + tip is real and
The first item is obvious. For (ii) we have laPl =
am
consequently it is equal to its real part. Then we have
6This terminology comes from times when mathematicians regarded complex numbers
as mysterious. These days an imaginary number is a concrete mathematical object.
197
Complex Numbers
In the last step we used (ii) and (i). Now it is sufficient to take square
roots.
Since the proof of (iv) is similar to the proof of (ii) of Theorem 4.3 we
0
leave it as an exercise.
By (i) we have ad = laI2 and if a
-
tive inverse of a is
a
-,
d
# 0 then a- = 1. The multiplicalaI2
consequently C is a field. Let us emphasize
laI2
C is the set of numbers of the form a + bz, where a , b are
real and z2 = -1.
C is a field.
Exercises
@ Exercise 7.2.1
Prove: If n E N and z E C then lznl =
then this equation also holds for n E Z.
1 . ~ 1 ~ .If z #
0
@ Exercise 7.2.2
Prove (iv) of Theorem 7.1. [Hint: Mimic the proof of
(ii) of Theorem 4.3.1
@ Exercise 7.2.3
7.2.2
For complex numbers 0 1 ,
a2,. . .
,a,
prove:
Square root of a complex number
In this subsection we prove that every complex number a has a square root,
that is a number whose square is a. Let us recall that if a is real’ and nonnegative then we agreed in Definition 5.2 to denote by fi the non-negative
number p such that p2 = a. If a < 0 then we define fi =
Now we
turn our attention to the case a = a zb with b # 0. If p2 = a then also
(-p)2 = a , hence in our search for the square root of CY we can look for a
p with a non-negative real part. The following calculation yields the real
part of p.
+
73:a = 0.
2m.
Introduction t o Mathematics with Maple
198
To determine the imaginary part of P we note first that 28,B 3 P = 3 P2
and then by using Equation (7.10) we have
Consequently
Ja+JaZ+b2+2b
J-.
lbIJ2
P=1
JZ
(7.17)
Let us summarize:
Theorem 7.2 (Square root of a complex number)
For a real
number a # 0 there are two distinct square roots. For a > 0 these are
(Definition 5.2) and -fi:for a < 0 these are
and
For an imaginary number Q! = a zb with b # 0 there are also two
square roots: one is given by Equation (7.17) and the other is -P.
6
~
z
m -za.
+
~~
~~~
~~
~~
~
We deliberately avoided the use of the symbol f i for the
square root of the complex number a , although this symbol is commonly used.
Readers are warned that the rules which govern operations with square roots
of positive reals as given in Theorem 5.6 are not valid for complex numbers.
Some counter-examples are given in Exercises 7.2.4 and 7.2.5.
Remark 7.1
Exercises
Give an example showing that the equation fifi =
Exercise 7.2.4
is false if z , w are complex numbers. [Hint: Choose z = w = -1.1
J.w
199
Cornpl ex Nurnbers
Exercise 7.2.5
7.2.3
Show that the equation
8;
- = - is false for z = -1.
Maple and complex numbers
Maple uses the letter I for 2. The arithmetic of complex number in Maple
brings nothing new. Readers must not forget the operator * when entering
a complex number, for instance 1 32 must be typed as 1+3*1.To obtain
Z enter conjugate (2). For instance, conjugate ( (4+6*1)/ (1-1) ) ; yields
1 51. The absolute value of a complex number z is entered into Maple as
abs (z), for instance abs (l+I) ; gives f i . If the square root of a complex
number is relatively simple expression then Maple provides the exact answer. For instance, sqrt (2*I); returns 1 I and sqrt (24-10*1); gives
5 - I. Quite often Maple returns the question, e. g. sqrt (4+I); gives only
d m . In such a case we have two options. We can use a floating point
approximation, sqrt (4.0+1)yields 2.015329455 .24809839341. Or if we
insist on obtaining an exact result we use the command evalc (sqrt (4+I) 1.
The command evalc simplifies a complicated complex number to the standard form a + bz. We can also use Example A.2 in Appendix A which
provides a Maple program which calculates the exact value of the square
root of a complex number according to the formula (7.17).
+
+
+
+
7.2.4
Geometric representation of complex numbers.
Trigonometric f o m of a complex number
A complex number a = a1 +za2 is represented in the plane with coordinate
axis 2, y as a segment with the initial point at the origin and end point
( a l , a2). The length of this segment is lal. The sum a+/3 with /3 = bl+zb2
is then represented by the diagonal of the parallelogram constructed with
sides a and /3; see Figure 7.1. The plane in which the complex numbers are
depicted is often referred to as the complex plane or the Gaussian plane or
the Argand diagram.
A very useful representation of complex numbers is achieved with the
help of trigonometric functions. They are rigorously defined and their properties established in Chapter 14. In this and the next subsection we rely
on readers’ knowledge of trigonometric functions from their previous work.
With the notation as in Figure 7.2 we have
*
Introduction to Mathematics with Maple
200
Y
X
0
Fig. 7.1 Sum of two complex numbers
z = IxI(cosq5
+ zsinq5).
(7.18)
For z # 0 the angle q5 is determined up to an integer multiple of 27r: it is
uniquely determined by Equation (7.18) if it is additionally required that
--T < 4 5 T and then it is called the argument of x. In Muple we have
argument ( z ) . The inequalities 0 5 4 < 27r together with Equation (7.18)
also uniquely determine 6.If w = Iwl(cos$ zsin$) then we obtain for
the product
+
+ zsin$)(cos$ + zsinq),
= ~ z ~ ~ w ~ ( c o-ssin4sin$
~ c o s ~ + zcos+sin$ + zsin4cos$),
zw = IzIIwI(cosq5
= lzllwl (cos(6
+ $) +
2 sin($
+ $)I.
The absolute value of the product of two complex numbers is the product of
absolute values. The sum of the arguments can be used as the angle which
201
Complex Numbers
Y
z
I
I
I
I
I
I
I
I
I
I
Izl sin+
I
I
I
I
I
I
I
I
I
I
I
I
I
0
Fig. 7.2 Trigonometric form of a complex number
is subtended by the representation of the product and the real axis.8 This is
illustrated in Figure 7.3, where for sake of simplicity we took Izl = IwI = 1.
For z = w we have
and by simple induction, for n E N,
zn = 1x1” ( cos(n4)
+ z sin(n4)).
(7.19)
This equation is called Moivre’s formula.
8 0 n e has to be careful not t o say that that the argument of the product is the sum
of the arguments of the factors: it is, however, if the sum lies in the interval ] - 7r, 7r].
Introduction to Mathematics with Maple
202
Y
0
.
0
/
1
I
X
O I
Fig. 7.3 Multiplication of complex numbers
Exercises
Use Equations (7.18), (7.19) and the Binomial Theorem
Exercise 7.2.6
to express sin 54 in terms of sin 4 and cos 4.
Exercise 7.2.7
Express sin4 4 in terms of cos 2 4 and cos44. [Hint: Set
z = cos 4 + z sin4 and then calculate ( z + 2)* in two different ways. The
Maple command (combine( (sin(x))4) ; can also be used.]
7.2.5
The binomial equation
The equation of the form
xn = a
(7.20)
with n E N is called the binomial equation. In this subsection we assume
a # 0, since the case a = 0 is trivial. Let us try Maple for a = 1 and some
n, say n = 3 and n = 10.
>
solve (x13=1);
Complex Numbers
203
1 1
1 1
1,--+-I&,----I&
2 2
2 2
This is a nice result, but unfortunately for larger n Maple provide a
complicated answer.
-/
4
4
/W,/W
/W,J
%1:=J
C
f
i
dCT5
%2 :=
The result is complicated but can be simplified by using the command
evalc. One way to do it is to to put the solutions in a list first, say,
L:=Csolve(x^(lO)-l,x)J and employ seq(evalc(L[i]),i=i. .lo). Fortunately there is a simple way of dealing with the binomial equation for any
n. The key is the Moivre formula (7.19). We use it for z = Izl(cos ++z sin 4)
and write a = lal(cos a z sin a ) with 0 5 a < 27r. Equation (7.20) is satisfied if and only if
+
m
1x1 =
cos n$ = cos a
sin n4 = sin a.
(7.21)
(7.22)
(7.23)
The last two equations in turn are satisfied if and only if
1
4 = -(a
n
+ 2k7r),
(7.24)
for some integer k . Obviously not all these values of 4 can be distinct: by
the uniqueness of the trigonometric representation they will be if
05a
+ 2k7~< 2nn.
(7.25)
Introduction to Mathematics with Maple
204
This leads to the condition
(7.26)
Consequently k = 0, 1, 2,
. . . , n - 1. Now all the numbers
+n2k7r + sin
xk =
(COS CY
"")>
n
are distinct and therefore they represent all solutions to Equation (7.20).
For a = 1 they are denoted &k and by Moivre's formula &k = E ! . We shall
call ~1 the nth primitive root of unity. In the Gaussian plane the numbers &k
are equidistantly placed on the unit circle: &k+l is obtained by rotating &k
by an angle 27r/n. The solutions to Equation (7.20) are called nth roots of a
and are denoted by @. There are n roots and in order to give a well defined
meaning to the symbolg
some choice must be made. In order to avoid
confusion or even a mistake the following convention is made: whenever
a choice f o r the symbol fi is made that choice must be kept consistently
during a calculation or a particular investigation. It is possible to derive
some rules for computation with fi.One such obvious rule is ( fi)"
=a
for any complex a. Some other rules are left for Exercises 7.2.10-7.2.12. One
must be aware that if some choice is made for the symbol fi this choice
might be inconsistent with previous definitions, in particular with Definition
5.2. This is undesirable and it is best to avoid the use of the symbol
for
a complex a as much as possible. We explained it because this notation is
commonly used and because it helps to understand what Maple does with
nth roots of a. For instance, we agreed on Page 154 that
would be
-2/3 but if you ask Maple root [31(-1) ; or root [31(-8/27) ; the answers
2
are (-1) and - (- 1) ; Maple simplifies what can be simplified but leaves
3
the choice of (-1)t =
to the readers. However, if we use the floating
point approximations with the root command, Maple will made a choice
for us! It finds the floating point approximation of the root for which the
argument has the smallest absolute value and for which the imaginary part
is non-negative. The following Maple worksheet illustrates this; Maple's
choice might surprise you.
G
G
v v
'For n = 2 we write simply
f i instead
of
G.
Complex Numbers
205
The choice is up to us.
>
root [31(-1 .O) ;
.5000000001+ 3660254037 I
fi.
The number 3660254037 is a floating point approximation to
We
2
did not get the answer -1.000 because this root has the maximal argument
and Maple chooses the approximation with the smallest argument.
>
(-1+1)-3;
24-21
but
> root [3] (2.0+2.O*I);
1.366025404
+ .3660254037 I
This is an example where @ # a , as shown by Maple.
Exercises
Exercise 7.2.8
Show that Equations (7.22) and (7.23) are satisfied if
and only if (7.24)i holds. [Hint: multiply Equation (7.22) by COSQ and
Equation (7.23) by sina! and add.]
Exercise 7.2.9
Solve Equation (7.20) for
(1) a = 1 and n = 8;
(2) a = -1 and n = 4;
(3) a = -2
22 and n = 3.
+
Exercise 7.2.10
tion (7.20) then
Prove: If a
# 0 and fi is a fixed solution to Equa-
is the set of all solutions to Equation (7.20), provided ~1 denotes the nth
primitive root of unity. [Hint: All the numbers in the set are solutions to
Equation (7.20) and are distinct.]
Exercise 7.2.11
Prove: If a , b are complex numbers, n E N and the
choice of value for each of f i l
and
is fixed and if ~1 denotes the
nth primitive root of unity then there exists a Ic E N such that fiG =
~
206
E;
Introduction to Mathematics with Maple
m.[Hint: Both numbers f i Gand m are solutions to the equation
xn - ab = 0. Use the previous exercise.]
@ Exercise 7.2.12
Prove: If a , b are complex numbers, n E N and the
choice of value for each of G,
is fixed then it is possible to define &&
in such a way that f i G =
Consider the case of a = b = 2. [Hint:
x = f i G satisfies Equation (7.20).]
m.
%(
Chapter 8
Solving Equations
In this chapter we discuss existence and uniqueness of solutions
to various equations and show how to use Maple t o find solutions.
We deal mainly with polynomial equations in one unknown and
add only some basic facts about systems of linear equations.
8.1
General remarks
Given a function f , solving the equation'
f (4= 0
(8.1)
means finding all x in dom f which satisfy Equation (8.1). Any x which
satisfies Equation (8.1) is called a solution of Equation (8.1). In this chapter
we assume that dom f is always part of R or C and it will be clear from the
context which set is meant. Solving an equation like (8.1) usually consists
of a chain of implications, starting with the equation itself and ending with
an equation (or equations) of the form x = a. For instance:
1
x+l
1
+= 0 =2 x + 2 + x +
1 = 0,
x+2
=2 22 + 3 = 0 ,
(8.2)
3
j x = -2'
For greater clarity we printed the implication signs, but implications are
always understood automatically. It is a good habit always to check the solution by substituting the found value back into the original equation. This
'A more complicated equation of the form F ( z ) = G(z) can be reduced to the form
given by taking f = F - G.
207
Introduction to Mathematics with Maple
208
is not a mere verification of the calculations, it has a far more fundamental
reason. Solving an equation starts with an assumption that the equation
is satisfied for some x, in other words, it is assumed that a solution exists.
If it does not then the chain of implication can lead to a wrong result.2
Consider the following example3
x - 1= J i T Z ,
x2 - 22 + 1 = 1 - 22,
x2 = 0,
x = 0.
(8-3)
Substituting 0 in the original equation gives -1 = 1. The conclusion is
clear and easy: the original equation had no solution.
It is preferable to use equivalences rather than implications. It is then
possible to conclude that the value found in the last equation is indeed a
solution. The verification is not necessary from the logical point of view
but it is still useful for checking the calculations.
It is not always possible to employ equivalences and this was the case
in Equations (8.3): the second equation does not imply the first. If we do
not use equivalences then the found value(s) need not be a solution and we
must perform the check. Let us illustrate it by one more example.
x - 1 = dG%,
x2 - 2 2
+ 1 = 5 - 22,
x2 = 4,
x = 2 or x = -2.
(8.4)
Back substitution shows that 2 is a solution and -2 is not.
Exercises
Exercise 8.1.1
Solve the following equations
x
+-(I) 2-1
X
X t l
X
(2)
x
+-=-
2-1
x+l
-
4x-2
22-1;
92-7
22-1'
2Remember, a false proposition can lead t o anything.
means the square root of a
31n this book unless otherwise stated the symbol
non-negative number y: if y contains an unknown or unknowns then the search is for
quantities which make y non-negative.
Solving Equations
Exercise 8.1.2
Equations (8.3).
(
209
Solve the equation z - 1 = &?%.
d x- 1)2 = a - 2 d a . I
[Hint: Mimic
Maple commands solve and fsolve
8.2
Maple can efficiently solve practically any equation. There are two commands: solve provides an exact solution and fsolve gives a numerical
solution. Although generally speaking it is better to obtain an exact solution, for some equations this might take too long and for some equations it
might even be impossible. Moreover, sometimes f solve gives a more useful
result than solve. For example, it is usually preferable to use f solve on an
equation like z2 z - 1 = 0 rather than solve it exactly. An exact solution
can be converted to a floating point result by the command evalf. The
following Maple session illustrates t h k 4
+
-1.618033989, .6180339887
>
solve (xA2+x-i=0);
1 1
--+-fil
-1- -1 - f i
2
>
2
2
2
evalf(%);
.6180339890, -1.618033989
Both commands have some options and sometimes it is important to
employ these options. The structure for solve is
solve (equation,unknown),
or if there are several equations and several unknowns
solve(equations, unknowns).
The structure for f solve is
f solve (equation, unknown, range).
4Note that the Maple floatign point results differ in low decimal places. This is a
natural result of computational inaccuracies.
Introduction to Mathematics with Maple
210
The options unknown or unknowns identify the unknown or unknowns for
which the equation should be solved. If there is only one unknown in
the equation, this option can be omitted. However, if there are several
unknowns or equations they should be entered as sets, for instance with
{ and 1 around them. The range could be given as a. .b, where a, b are
real numbers, or could be - i n f i n i t y , i n f i n i t y . If the option range is
given as above, Maple will find a solution5 although there might be many
solutions in that range. The range can also be given by the word complex.
In this case, fsolve searches for a complex solution. If the equation is a
polynomial equation then f solve will find all real solutions, or all complex
solutions if the option complex is given. There is additional useful and
important information on solve and f solve in Help.
Example 8.1
>
Now we use Maple for solving (8.3) and (8.4).
Eq:=x-l=sqrt(l-2*~);
Eq := x - 1 = JTZG
>
solve(Eq,x) ;
Maple correctly did not return any result: there is no result t o be obtained.
As we know, the equation has no solution.
>
eqn:=x-i=sqrt (5-2*~);
eqn := x - 1 = JTTEi
>
solve(eqn,x) ;
2
This is very pleasing: Maple returned the correct answer and omitted the
spurious 'solution' x=-2. Maple can also check the solution.
>
x:=2;eqn;
x := 2
1=1
Maple has its own command eval for checking.
>
eval(eqn,x=2) ;
51f there is one.
21 1
Solving Equations
1=1
Example 8.2
An agent offers an annuity of 12 years duration with yearly
payments of $10 000 for $103 000. We wish t o compare this offer with other
investments and therefore want t o know the interest rate. In order t o find it
we use fsolve and annuity.6
>
with(f inance) ;
>
fsolve(annuity(1OOOO,x, i2)=1O3OOO) ;
-1.781236788
~~
~
An interest rate of -178% is clearly wrong. We shall look for an explanation
shortly, but first we try t o employ options with fsolve. We know that the
rate of interest must be positive and less then loo%, hence we try
.02432202661
Why was the first result wrong? The explanation lies in the formula for
annuity.
>
annuity(IOOOO,x,12);
1
10000 - J:
10000
J:(l+J:)12
So the equation to be solved reads l O ( 1 +z)l2- 10 = 103x(1+ x ) 1 2 . This
is a polynomial equation and f solve will provide all real solutions.
>
fso~ve~10*(l+x)~(~2)-l~-103*x*(l+x~n(12))
;
-1.781236788, O . , .02432202661
‘The finance package was introduced in Example 1.4.
Introduction to Mathematics with Maple
212
The discrepancy has disappeared. The annuity problem leads to a polynomial equation with three real solutions. The first attempt picked up the
negative root of the polynomial equation, and this root was not the solution
of the original problem. The second attempt was successful. The moral of this
story is that we should always use the options with fsolve, provided we are
certain in what range the desired solution lies.
Example 8.3 This example was used by mathematicians to tease lay people. It is not difficult but it requires a numerical solution.
0
Fig. 8.1 Grazing goat
A goat is tied up a t the circumference of a circular meadow of radius one.
How long should the rope be which allows the goat to graze an area of half
the circle? Figure 8.1 shows half of the circular yard, with the goat tied a t the
angle x. Using the notation from this figure we immediately recognize that
the angle x = n/4 produces rope that is too long and that the angle x = n/3
produces rope which is too short. The length of the required rope is 2cosx
and the area the goat can graze is 42 cos2 x n - 22 - sin(n - 22)
+
>
>
fso~ve(x*2*(cos (~))-2+Pi/2-~-(1/2)*sin(2*x) =
Pi/4,x,Pi/4. .Pi/3) ;
,95284’78647
>
X:=(%);
X := .9528478647
>
2*cos(X);
1.158728473
*
213
Solving Equations
The value for the length o f the rope looks about right: however t o be sure
we found the right result, we calculate the area which the goat cannot graze
and compare it with the area of the half-circle, which is 7r/2.
>
Pi-4*X*(cos(X)) ^2-2*((Pi-2*X)*(I/2)-(I/2)*sin(Pi-2*X))
;
1.570796326
>
evalf ( P i / 2 ) ;
1.570796327
This shows that we obtained the correct answer.
8.3
Algebraic equations
An equation of the form
P(.) = 0 ,
(8.5)
where P is a polynomial, is called algebraic. The degree of P and the
coefficients of P are also the degree of the equation and the coefficients of
the equation, respectively. We shall consider only equations with complex
coefficient^.^ A zero of P is obviously a solution to (8.5) and is also called
a root of Equation (8.5). From ancient times mathematicians tried to solve
algebraic equations and it was only the German mathematician K. F. Gauss
at the end of the eighteenth century who proved that algebraic equations
always have solutions.
Theorem 8.1 (The Fundamental Theorem of Algebra) An
algebraic equation with complex coefficients and of degree at least one
always has a solution within the field of complex numbers.
We shall not prove the theorem here, since it requires too much preparation. A proof accessible with our level of knowledge can be found in Kurosh
(1980, pp. 142-151). The name is slightly misleading: a better name would
be “The Fundamental Theorem of algebra of complex numbers”, but this
name is still not perfect. There were many proofs given, however all of them
use the concept of continuity, which will be considered in Chapter 12, and
of completeness of reals. These parts of mathematics are not considered to
belong to algebra.
7This, of course, includes also the real and the rational coefficients.
*
Introduction to Mathematics with Maple
214
An important consequence of the Fundamental Theorem of Algebra is
that the only irreducible polynomials in C[X]
are linear. Indeed, if P ( X ) E
C[X]
is of degree at least two then it has a root, say a , and is therefore
divisible by X - a. Hence it is not irreducible. Writing P ( z ) as a product
of its irreducible, that is linear, factors we have
Not all xi need be distinct, however it follows that an algebraic equation
of nth degree has n complex roots, provided each root is counted as many
times as its multiplicity. Multiplying through on the right hand side of
Equation (8.6) and comparing coefficients leads to the so-called Vieta's
formulae'
The polynomials on the right hand sides of Equations (8.7) are called
elementary symmetric polynomials. The so-called fundamental theorem on
symmetric polynomials states that any symmetric polynomial, that is a
polynomial which is not changed by a permutation of indeterminates, can
by expressed as a polynomial in elementary symmetric polynomials. We
do not have much use for this otherwise important theorem, so we do not
prove it. However, we illustrate it by a simple example. If W = ( 2 1 - ~ 2
then it is easy to see that W = a12 - 21-22.
Rational roots
It is clear (for instance from the Vieta formula for a n ) that if the equation
P ( x ) = 0 has an integer root xi then zi divides a n . The next example
shows how to use this for finding an integer root.
Example 8.4
>
>
First we define the polynomial
f:=y->y^6+ 2*y^5 + 435*yn4 + 11025*~^3
- 37125*yn2 - 253125*y + 759375;
8Readers are probably familiar with these for n = 2, when P(x)= x 2 + p z
p = -(21 2 2 ) , q = 2122.
+
+ q and
)
~
Solving Equations
f := y + y6 + 2 y5
215
+ 435 y4 + 11025y3 - 37125 y2 - 253125 y + 759375
Next we use the Maple command divisors t o find the positive divisors
of the absolute term. For that we need the number theory package. We must
not use letter D to denote the divisors, since it is protected. Maple provides
the set of divisors, which we put into a list we call podi (for positive divisors).
>
with(numtheory, divisors) ;
> podi :=convert (divisors (759375),list) ;
podi := [l,3, 5, 9, 15, 25, 27, 45, 75, 81, 125, 135, 225, 243, 375, 405,
625, 675, 1125,1215, 1875, 2025, 3125, 3375, 5625, 6075, 9375, 10125,
16875, 28125, 30375, 50625, 84375,151875,253125, 7593751
The next step is to create a list of all divisors.
> di:=[op(podi) ,op(-podi)] ;
This gives a long list di, from which we select those z for which f(z)= 0.
The procedure for selecting elements from a list according to a given condition
was discussed in Subsection 3.3.3 on page 80.
> good:=x->is(f (x)=O) ;
good := x -+ is(f(x) = 0)
>
select (good,di) ;
[3, -51
Since there are very many very large entries in di t o do this work with pen
and paper would be almost forbidding, but with Maple it was easy.
Rational roots can be found similarly. Let
~ ( 2=
) aOzn
+ u1xn-l + . + an.
*
*
(8.8)
If p and q are relatively prime integers and p/q is a zero of Q then p divides
an and q divides ao. Indeed, we have
Since p and q are relatively prime, p divides an. It follows similarly that q
divides ao. Therefore the rational zeros of Q can be found among fractions
of the form
divisor of an
divisor of a0
(8.9)
Introduction to Mathematics with Maple
216
+
(See also Exercises 8.3.2-8.3.5.) If Q(x) = 4x2 4z - 3 we have to check
12 possibilities to find that -3/2 and 1 / 2 are the rational roots. Obviously,
for a polynomial of higher degree with large coefficients the computer is
needed. The next example shows the use of Maple for finding rational roots.
The method we employ is not recommended when using mere pen and
paper, but with Maple it is easier to implement. We make a substitution
x = y/uo and seek the integer roots of u;-lQ(y). For justification see
Exercise 8.3.1.
Example 8.5
222 - 35.
>
Let us now find the rational roots of Q ( x ) = 6 x 3 - 17x2
+
Q:=x->6*xn3-17*xn2+22*x-35;
Q := II: + 6 x3 - 17 x 2
+ 22 x - 35
>
x :=y/6 :36*Q (XI ;
>
>
>
~:~~->~n3-17*~n2+132*~~1260~
with(numtheory,divisors):
L:=convert (divisors(l260) ,list) :div:=[op(L) ,op(-L)I :
>
good:=t->is(P(t)=O) :
>
select(good,div) ;
y3 - 17y2
+ 1329 - 1260
P has a root 14, consequently, the rational root of Q is 7 / 3 .
Maple has a ready-made command for finding rational roots of polynomials. It is roots,and Maple answers with a list of pairs [root, multiplicity].
If there are no rational roots Maple returns an empty list. We now compare
roots with our previous results.
“3, 11, [ - 5 , 111
>
roots(P(t1);
217
Solving Equations
Multiple roots
It is important that solving of an algebraic equation can always be reduced
to solving another algebraic equation with simple roots. Let k be a positive
integer and
with Q ( u ) # 0. Then by Equation (6.27)
D P ( x ) = k ( x - U)"'&(X)
+ ( X - u)'DQ(x) = ( X - u)"-'F(x),
(8.11)
with F ( a ) = kQ(a) # 0. Consequently a is a root of D P ( x ) of multiplicity
k - 1. A root of P of multiplicity k is a root of multiplicity k - 1 of the
greatest common divisor of P and D P . Thus P ( x ) divided by the greatest
common divisor will have only simple roots.
> P:=X->X^~-X^~+X^~-X^~-X^~-X^~+X+~;
+
x7 - x5 x6 - x4 - x3 - x 2 + x + 1
To investigate the multiplicity of roots of P , we look at9
P
>
:= x
4
gcd(P(x) ,D(P) (x));
+
x3 x 2 - x - 1
This polynomial is of degree three, so P must have three multiple roots.
To find whether or not some have multiplicity greater than two, we again
look for the greatest common divisor of this polynomial and its derivative
> gcd(%,3*~^2+2*~-1);
x+l
-1 is a triple root of P and it is easy to conclude that 1 is a double root.
We could also find by inspection that 1 was a root of P , and use the
Taylor polynomial to determine its multiplicity.
>
taylor(P(x) ,x=l,8) :convert(%,polynom);
+
+
+
+
+
16 ( x - 1)2 40 ( x - 1)3 44 ( x - 1)4 26 ( x - 1)5 8 ( x - 1)6 ( x - 1)7
+
x5 3x4
-1 is now an obvious root
>
+ 4x3 + 4x2 + 3 2 + 1
taylor(%,x=-l,6) :convert(%,polynom);
gThe command D(P)
(XI
in Maple produces the derivative of P(x>.
Introduction t o Mathematics with Maple
218
+
+
+ +
2 (z 113 - 2 (z 114 (J:
As before we obtained that -1 is a triple root.
115
Real roots
In this subsection we consider only polynomials with real coefficients. If a
is an imaginary root of P then so is a. Indeed
since Ei = ai because the coefficients are real. This leads to
Theorem 8.2 If a is a root of multiplicity k of an algebraic equation
with real coefficients then so is 6. The number of imaginary roots of
an equation with real coefficients is even.
Proof. We have just seen that the theorem is true for k = 1. We proceed
by complete induction. If P has an imaginary root a then P ( x ) = (x2 (a 6)x aii)Q(x),with Q having a as a root of multiplicity smaller than
that of P. By the induction hypothesis the multiplicity of 6 for Q is the
0
same as that of a and the assertion follows.
+
+
Mathematicians throughout the ages found many theorems which help
to determine the number of real roots of a given polynomial and isolate
them in separate intervals. The best known are the theorems of BudanFourier, Sturm and the so called rule of Descartes. The interested reader is
referred to Kurosh (1980). Maple has a tool for an application of the Sturm
Theorem. We shall not discuss these matters here, but mention a Maple
command realroot. It responds with a list of intervals each containing
just one root, selects in some reasonable way the length of these intervals
and sometimes returns an interval [a,a] which indicates that a is a root.
Multiplicity of roots is not taken into account.
>
realroot (x^6+x^5-xA2+1);
"-1, -11, 1-27 -111
Solving Equations
219
This means the polynomial has a root -1 and a real root in the interval
[-2,-11.
If there are no real roots the command returns an empty list.
> r e a l r o o t (xn4+I) ;
The concept of an algebraic solution
By an algebraic solution of an algebraic equation we understand formulae
for the roots which use only the coefficients of the equation and only operations of addition, subtraction, multiplication, division and extraction of
integer roots. This is a useful description but it is not precise enough.
We shall say that we found an algebraic solution to the equation
with complex coefficientslO or that the equation is solvable in radicals if we
define a finite chain of binomial equations
...
with the following properties:
(1) the numbers mi, i = 1,..,k are positive integers;
(2) A1 is an element of a field F which is an extension of Q by the
coefficients of (AE);
(3) Ai for i = 2, . . . , k is an element of the smallest field which contains
F and the roots of previous binomial equations;
(4) all the roots of Equation (AE) lie in the smallest field which contains
F and roots of the binomial equations (A1)-(Ak).
Example 8.6
Consider the equation (x3 - a)2
+ 2 = 0. We see that the
"The concept of an algebraic solution to an equations with coefficients in an arbitrary
field can be defined rather similarly but we shall not do it here.
220
Introduction to Mathematics with Maple
required chain of binomial equations is
It might be plausible to think that every algebraic equation is solvable in
radicals: the equation was formed from the roots of the equation using addition, subtraction, multiplication, division and taking powers so it should
be possible by using only addition, subtraction, multiplication, division and
by extracting roots from complex numbers to untangle the equation and get
back to the roots of the equation. Mathematicians for a long time held this
false belief. It was the Italian mathematician Paulo Ruffini who lived in the
second half of the eighteenth and first half of the nineteenth century who
proved that there are equations of the fifth degree which cannot be solved
algebraically. This was followed by important works of N.H. Abel and E.
Galois. Today we have a theory, called the Galois theory (interested readers are referred for example to Hadlock (1978): however we add a warning
that at this stage they might not be sufficiently equipped to handle it) by
which it is possible to decide whether or not a given algebraic equation can
be solved algebraically. The impossibility of solving an equation in radicals
should not be confused with the existence of the solution. By the Fundamental Theorem of Algebra the solution always exists no matter whether or
not a solution in radicals can be found. Solving an equation algebraically
means finding the chain of Equations (A1)-(Ak); that is, finding the roots
of the equation by using very special equations, namely the binomial equations. From this point of view it would be surprising if every algebraic
equation was solvable in radicals. Obviously it is easy to be wise after the
event. Equations of degree up to four are always solvable in radicals, other
equations might or might not be algebraically solvable. Maple is fantastic
in this regard: if there is a solution in radicals, Maple will find it."
Quadratic equations
In this section we show that the 'usual' formula for the quadratic equation
with which the readers are probably familiar from school is valid for a
quadratic equation with complex coefficients. Rewriting the equation x 2
+
l1Within
reasonable limits of your computer capabilities.
Solving Equations
221
px + q = 0 in the form (x+ P-)2
1
= -D with D = p 2 - 4q we see immediately
2
4
that if D = 0 the equation has a double root 3 and if D > 0 then the
2
roots are
-P 1
2
+1? fand
i ---a.
2
2
2
(8.12)
fiis understood in the sense of Definition 5.2, the square root of a positive
real number. However, the Formulae (8.12) also hold if D is negative. The
symbol
then means
Finally, if D is imaginary, we know that
there are two complex numbers which squared equal to D. We denote
one of them12 @ and the formulae (8.12) still hold. It is customary to
choose fias p in Equation (7.17). For example, the roots of the equation
x2 222 3 are z and -32, the roots of the equation x2 - 2x 1- 2 2 are 2 z
and -2. It is useful to know by heart the Formulae (8.12) for the solution
but Maple provides the solution instantly, also for an equation of the form
ax2 bz c = 0. However, for an equation with numerical coefficients
the formulae for the solution should not be used but the command s o l v e
applied directly to the given equation.
zm.
+ +
+
+
+ +
Cubic equations
Scipione del Ferro, who lived between 1496 and 1526, was the first to solve
the general cubic equation. H. Cardano published formulae for the solution in his book Ars Mugnu in 1545 and the formulae for the solution are
now often referred to as the Cardano formulae. Solving the cubic equation
was undoubtedly a great intellectual achievement but the Cardano formulae themselves have little practical value. Often they give the solution in
a rather complicated form and in case of an irreducible equation with real
coefficients and three real roots the formulae involve cube roots of complex
numbers and an effort to simplify them leads back to solving the equation. For this reason this case is called cusus irreducibilis (Latin for “the
irreducible case”).
Let us consider a cubic equation in more detail. An equation
xn + a l X n - l
121t does not matter which.
+ + a,
* *
’
=0
(8.13)
Introduction to Mathematics with Maple
222
can be rewritten in the form
+):
n
+ terms of degree less than n = 0.
After the substitution y = x + a1/n, Equation (8.13) takes the form
(x
In particular, for cubic equations, only the cubic
x3
+ px + q = 0
(8.14)
needs to be solved.
>
ceq:=xn3+p*x+q=0 ;
ceq := x3 + p z + q = o
The Maple command solve can now provide the Cardano formulae,
not in the original form, but Maple's form has an advantage. The Cardano
formulae require the (possibly imaginary) cube root of 12p3 81q2; the
correct choice of root has to be made. In the Maple formulae, this root can
be chosen arbitrarily out of three possible values. However the same value
must be kept throughout the formulae. The result is valid only for p # 0,
but this is no obstacle since for p = 0 the equation is binomial.
+
>
Sol:=[solve(ceq,x)] ;
+
+
%1:= -108 q 12 J12p3
81 q2
For p = 3 and q = -4 the equation has an obvious root 1. But if we
substitute p and q into the previous Maple result, we get:
>
p:=3;q:=-4;Sol[l] ;
p := 3
q := -4
6
16 (432 + 12 d m ) ( ' I 3 ) - (432 + 12 di%%)(1/3)
223
Solving Equations
>
simplify(%) ;
+ &)(2/3) - 1
+ &)(1/3)
(2
(2
>
rationalize (%);
((2
>
+ &)(2/3)
- 1) (2
+ &)(2/3)
(-2
+ 6)
simplify(%) ;
+ a)
+
+
((2 &)(2/3) - 1) (2 &)(2/3) (-2
We still cannot obtain 1. Of course, our approach was clumsy. Applying
solve directly to the equation with numeric coefficients immediately gives
the correct answer.
>
solve (x^3+3*x-4=0,x) ;
1 1
1 1
1,--+-Ifi,----Ifi
2 2
2 2
If x1,x 2 , x 3 are roots of the equation then ( x 1 - ~ 2 ) ~ ( - ~x 31) 2 ( x 2 - ~ 3 )
is called the discriminant of the equation. By the fundamental theorem on
symmetric polynomials (see page 214) the discriminant can be expressed in
terms of the coefficients. This is a laborious task and we let Maple do it for
us. We use the command simplify in a new way, specifying eqs as a list
of equations, which Muple should use in expressing SP in a new form, thus
simplify (PS,eqs) ;
>
>
eqns := [x [ll +x [2]+x [31=0,x [l] *x [2]+x [l] *X [3]+X [2]*X [3]
=p,-x[ll*x[2]*x[ 3]=qI;
+ +
+
+ x3 == P, - 2 1
w)-2* (x [21-x [31)“2;
eqns := [XI 2 2 x3 = 0 , xi x 2 xi ~3
> disc : = (x [l] -x [2] ) ^2*(x [I] -x
disc := ( 2 1 - ~
> simplify(disc,eqns) ;
2 (XI) -~~
2 2 x 3 = Q]
22
3 ( 2 )2 -~~
3
)
~
-4p3 - 27q2
If p , Q are real then the equation has three real roots if and only if
the discriminant is positive, however the Cardano formulae then contains
complex numbers (in a rather complicated way). But solve might also give
the roots in a complex form. Consider the equation
~
Introduction to Mathematics with Maple
224
>
Eq:=x^3-3*~+1=0;
EQ := x3 - 3 z + 1 = 0
Looking at the graph or evaluating the discriminant -4p3 - 27q2 = 81
shows that this equation has three real roots. However
>
solve(Eq,x) ;
1
2
-%1+
2
4 I &i)(1/3)
’
(-4
1
1
-- % 1 51 I & ( s1 % l - (-4
4
(-4
4I fi)(1/3)
1
1
1
1
-- % 1 - -I
%14
(-4
4I4)(1/3) 2
(-4
+
+
+
+
2
+ 4 I &)(1/3)
2
+ 4 I &)(ll3)1
One way to proceed is to use f solve.
> fsolve(Eq,x) ;
, .3472963553,1.532088886
- 1.879385242
If we want an exact solution we use the command evalc similarly as we
did in Subsection 7.2.5 with the binomial equation.
> L:=[solve(Eq)l :seq(simplify(evalc(L[i])) ,i=l. . 3 ) ;
2
2
2
2
2
2 cos(-- T ) , -cos(- n) - &sin(- n),-cos(- T ) &isin(- T )
9
9
9
9
9
For an irreducible cubic equation it is almost invariably better to use
f solve rather than solve, not only in the case of three real roots. On the
other hand if the equation is reducible in the field implied by the coefficients,
solve will find all roots in a neat form even if all roots are real.
+
>
solve (8*xn3-6*x+sqrt (2)) ;
1
1
1
1
- 4- l h + p ,- z l h - z & ,
1
,Jz
The case of an irreducible cubic equation with three real roots is called
cusus irreducibilis. The Cardano formulae (or solve) express the roots
Solving Equations
225
in a complicated form involving imaginary numbers and it can be shown
that no amount of algebraic manipulation can simplify it to the standard
form a bz. It is therefore very pleasing that with Muple you can obtain
satisfactory result by using evalc together with solve. However Muple
does not always do it ‘algebraically’: note that in the previous example
trigonometric functions were used.
+
Equations of higher orders and fsolve
8.3.1
Although equations of fourth order can be solved algebraically and equations of order five or higher, generally speaking, cannot, we recommend,
unless the equation has a strikingly simple form, to use f solve rather than
solve. For comparison consider the next equation which can be solved algebraically (by solve).
>
1
solve (~-5+x+i);
1
1
1
1
2
--+-I&,----I&,--%l-2 2
2 2
6
3 (100
1
-1% l +
12
(100
+
3
12 &%)(1/3)
+
1
3
12 r n ) ( 1 / 3 )
+
1
12 r n ) ( 1 / 3 )
2
3
(100
12
(100
3
(100
+ 12
rn)(1/3)
+
12 &3)(1/3)
f solve (x^5+x+i) ;
%1:= (100
>
+ 12 4%)(1/3)
2
-
-
-1% l +
1
+ 3’
- ,7548776662
>
fsolve (xA5+x+i,x, complex) ;
-. 7548776662,
+
-.5000000000 - .8660254038 I , -.5000000000 .8660254038 I,
.8774388331- .7448617666 I , .8774388331+ .7448617666 I
Although we recommend using f s o l v e for solving equations of degree
higher than three, even with the aid of a computer, numerical calculations
can have problems of their own. The next example illustrates this.
Introduction to Mathematics with Maple
226
>
Digits:=5;
Digits := 5
>
badpol:=expand( (x-1.0001)n2*(x-1.0002)n2*(x-l.0003)n2) ;
>
badpol := x6 - 6.0012 x5 15.006 x4 - 20.012 x3
$15.012 x2 - 6.0060 x 1.0012
f solve (badpol) ;
+
+
l., l., l., l., l., 1.0012
The results says that the polynomial has a root of multiplicity 5 (which
it does not) and the error for the last root is fairly large. This was caused
by the number of digits used and rounding of all numbers to 5 digits.
However similar phenomena can occur with any number of digits. It shows
that the analysis of rounding errors can be important, but a lot of deep
mathematics is needed for that. To see the reason why we obtained such
an unsatisfactory result we graph the polynomial.
>
plot(badpol,x=0..2, scaling=CONSTRAINED);
iI
X
Solving Equations
227
We see that the graph is almost indistinguishable from the x axis between 0.6 and 1.4 and therefore to locate the zeros is difficult. By increasing
the number of digits we obtain a perfect result.
>
restart;
> Digits:=30;
Digits := 30
> badpol:=expand((x-l.OOOl)~2*(x-l.OOO2)^2*(x-l.OOO3)^2);
+
>
+
badpol := x6 - 6.0012 x5 15.00600058 x4
- 20.012002320144 x3 15.0120034804320193 x2
- 6.00600232043203860132 x
+ 1.001200580144019301320036
f solve (badpol) ;
1.00010000000000000000000000000,1.00010000000000000000000000000,
1.00020000000000000000000000000,1.00020000000000000000000000000,
1.00030000000000000000000000000,1.00030000000000000000000000000
~~
It should be realized that increasing the number of digits may not always
be a viable option and, when the numbers come from experimental data,
could be meaningless.
Exercises
)
Exercise 8.3.1
Prove: A rational root of a polynomial with integer
coefficients and leading coefficient 1 is an integer. [Hint: q divides 1.1
Exercise 8.3.2
Test the numbers of the form (8.9) to find the rational roots of 2x6 x5 - 9x4 - 6x3 - 5x2 - 72 6 and then compare
your results with that obtained by the Maple command roots. [Hint:
Make a list L of divisors of 2 and a list K of positive divisors of 6. Then
Cseq(op(L) /K [il ,i=l . .nops(K) ) 1 is the list to be tested.]
+
+
Exercise 8.3.3
Let S be a polynomial with integer coefficients, a a
rational root of S and S(x) = (x - a)H(x). Show that H has integer
coefficients. [Hint: Compare coefficients!]
1 Exercise 8.3.4
Use notation as in the previous exercise with a = p / q
and p , q relatively prime. Prove that p - q and p q divide S( 1) and S (- l),
+
Introduction to Mathematics with Maple
228
+ q.]
respectively. [Hint: q S ( 1 ) = ( q - p ) H ( l ) . Similarly for p
Exercise 8.3.5
Use the previous exercise t o find the rational roots of
S ( x ) = 6 x 5 l l x 4 - x3 5 x - 6 .
+
+
Exercise 8.3.6
2x3 + 2x2 + 1.
+
Find the multiple roots of P ( x ) = x g 2x7
n;!?,
+ 2x5 + x4 +
+
Use f s o l v e on the equations
( x - j ) 10/11 = 0 ,
Exercise 8.3.7
n%P,(x- j ) + 1.1 = 0. Increase the number of Digits to 30 and repeat.
8.4
Linear equations in several unknowns
Linear equations in several unknowns are important because many problems in Science and engineering lead ultimately to solving large systems of
linear equations. For instance, in shipbuilding, equations with over 40000
unknowns were encountered. The branch of mathematics which deals with
linear equations is linear algebra and Maple has two useful packages, linalg
and Linear Algebra. This section is not meant even as a modest introduction to linear algebra or to these packages. It is a simple guide on how
to solve small systems with exact coefficients.
We start with a simple example which exhibits the behavior of linear
systems. Consider the system
(8.15)
(8.16)
Subtracting the first equation from the second gives ( c - l ) y = b and if
c # 1 , y is uniquely determined as y = b / ( c - 1 ) and x is then uniquely
determined from the first equation x = - b / ( l - c ) . It is easy to check that
the found values are indeed a solution. So we have
The first case: The system has a unique solution.
If c = 1 and b
have
# 0 then
the second equations contradicts the first. So we
The second case: The system has no solution.
And if c = 1 and b = 0 then the second equation is superfluous: we can
choose y arbitrarily and x = - y is a solution. So we have
Solving Equations
229
The third case: The system has infinitely m a n y solutions and some of
the unknowns can be chosen arbitrarily.
These three cases are typical for linear systems no matter how large the
number of unknowns. Linear sytems have another feature: the solution
always lies in the same field as the coefficients. We now look at some
examples. The first one would require a lot of work with pen and paper,
but using Maple the only work needed is to type the equations correctly.
eqs :=
2 ~ + 3 ~ + 4 ~ + 5 =~ 1 +, 26 ~~3 ~ + 4 ~ + 5 ~ + 6 wv =
+ -2,
3 2 + 4 y + 5 2 6~ + V
2~ = 3 , 4 ~ 5 y + 6 2 u 2 v 3 w = -4,
5 x 6 y z 2~ 3 v 4 w = 5 , 6 ~ y 2 2 3~ 4 v 5 w = - 6 )
{X
>
+
+
+
+
+ + + + +
+
+ + +
+ + + + +
so1ve(eqs,{x,y,z,u,v,w}) ;
w = - 10 z = - -32 u = -8 V = - -6
21’
21 ,
7’
7
Returning to our introductory example
> eql :={x+y=O ,x+c*y=b} ;
{
,
-25
y = - 38 x = -1
21’
21
eql : = { x + y = O , x + c y = b }
>
solve(eqi,{x,y});
b
b i Y = - x = --1
c’
-l+c
+
Maple returned the answer only for the ‘general’ case. This shows that
even in simple examples when symbolic solution is obtained, caution is
needed.
>
>
restart;
equations:={x+y+z=l,x+y+a*z=l,x+b*y+z=l};
equations := {x
>
+ y + z = 1, x + b y + z = 1 , x + y + a z = l }
solve(equations,{x,y,z});
Introduction to Mathematics with Maple
230
(a: = 1 , z = 0, y = 0)
Maple again provided the solution in the general case. Unfortunately,
in this example, it did not give any hint as to which values of a, b are
exceptional. However, subtracting the first equation from the second and
from the third leads to ( b- l ) y = 0, (a - 1)z = 0. If b = 1 and a # OI3 then
z = 0, x = -y, and y can be chosen arbitrarily. This illustrates two facts:
Maple did not provide a complete solutions for all possible values of a and
b and in the third case some unknowns can be chosen but the system might
determine which unknowns cannot be chosen. In this example z cannot
be chosen but z or y can. Finally, if a = b = 1 any two unknowns can be
chosen, say x , y and the third one is then uniquely determined, z = 1-x- y.
Exercises
Exercise 8.4.1
Use Maple to solve the systems
+
+
(1) x - z = 0 , y - u = 0, -x z - 2) = 0, -y
u - UI = 0, -2
0,-u+w=o;
(2) 2 - z + w = o , y - u + w = o , x - y + v - w = o , y - z + w =
0 , x - u + 2) = 0;
(3) x y z = 1, ax by cz = d , a2x b2y c2z = d2.
+ +
+ +
13The case a = 1 and b # 0 is similar.
+
+
+
2,
=
Chapter 9
Sets Revisited
In this chapter we introduce the concept of equivalence for sets and
study countable sets. We also discuss briefly the axiom of choice.
9.1
Equivalent sets
Two finite sets A and B have the same number of elements if there exists
a bijection of A onto B. We can count the visitors in a sold-out theatre by
counting the seats. The concept of equivalence for sets is an extension of
the concept of two sets having the same number of elements, generalised to
infinite sets.
-
Definition 9.1 A set A is said to be equivalent to a set B if there
exists a bijection of A onto B; we then write A B.
Example 9.1
(a) The set
N and the set of even positive integers are equivalent.
-
n H 2n is a bijection of N onto (2, 4, 6, . . .}.
(b) Z N. Let f : n H 2n for n > 0, and f : n
Indeed,
+
H -2n
1 for n 5 0.
Then f is obviously onto and it is easy t o check that it is one-to-one.
Hence it is a bijection of Zonto N.
(c) 10, I[-10, m[. The required bijection is 2
-
1
I-+
- - 1.
X
We observe that the relation A
B is reflexive, symmetric and transitive, and thus it is an equivalence relation. Reflexivity follows from the
use of the function idA, which provides a bijection of A onto itself. If the
function f is a bijection of A onto B then it has an inverse and f-1 is a
bijection of B onto A , so A B + B A . Finally, i f f and g are bijections
-
-
231
Introduction to Mathematics with Maple
232
of B onto C and of A onto B , respectively, then f 0 g is a bijection of A
onto C, so ( A B,B C) + A C. In view of the symmetry property
we may put A B into words as ‘the sets A and B are equivalent’.
Displaying the first few terms of a sequence often gives a good insight
into the behaviour of the sequence. For example, for the sequence from
Example 9.1 (b) we have
N
N
N
N
0, 1, -1, 2, -2, 3, -3,
...
(9.1)
Etom this it is clear that each n E Zappears in a uniquely determined place
in (9.1), which means that this sequence is a bijection of N onto Z.
A set A is said to be enumerable if it is equivalent to N,that is, if
there exists a bijection of N onto A; this bijection is called an enumeration
of A . The set of even positive integers and Z are both enumerable by
Example 9.1. A set is called countable if it is finite or enumerable. Note
that ‘countable’ in mathematics differs from the meaning this word may
have in other contexts.
Warning: Some authors use the words ‘enumerable’ and ‘countable’ with
slightly different meanings. Also the word ‘denumerable’ is sometimes used
instead of ‘countable’.
Theorem 9.1
The range of a sequence is countable.
Let the sequence be n H un. We list the members
and cross out all repetitions when ‘moving along the sequence (9.2) from
left to right’. We are then left with either a finite sequence or we obtain an
enumeration of rgu. The formal proof is as follows.
Proof.
The theorem is obvious if r g u is finite. Define
n1
= 1, and if
. . . , n k have been defined let n k + 1 be the smallest integer n such
that U n $ { un, , un,, . . . , unk }. Then the sequence k t+ U n k is an enumern l , 722,
ation of rgu.
0
It is worth noting that in the course of this proof we have tacitly used
the recursion theorem (Theorem 5.13) and the well ordering principle (Theorem 4.10).
Sets Revisited
Theorem 9.2
sequence.
233
A non-empty countable set is the range of some
Proof. If A is enumerable then A is the range of its enumeration. If A is
finite then A = { a l , a2, . . . , U N } for some N . In this case define a, = U N
for n > N . Then n I+ a, is a sequence and A is its range.
0
Theorem 9.3
countable.
I f A is countable and f : A
H
B is onto, then B is
If A is finite then so is B. If A is enumerable let u be the enumeration of A. The function f o u is a sequence and its range is B . Hence
B is countable by Theorem 9.1.
0
Proof.
Theorem 9.4
If B
c A and A is countable then so is B.
This is rather intuitive: a subset of a countable set is countable.
Proof. If B = 0 or B = A there is nothing to prove. Otherwise choose
some element b E B. Now consider the identity function on B , idB, and let
h be the extension of this to A such that h ( ~=) 6 for x E A \ B. Clearly
h : A I-+ B is onto, and hence B is countable by Theorem 9.3.
0
Theorem 9.5
N x N is countable.
~
We can arrange the elements of
N x N into an infinite table.
Introduction to Mathematics with Maple
234
By following the arrows we can arrange the elements of N x N into a sequence
and this sequence enumerates N x N. For a reader who feels uncomfortable
with this proof, because we have not explicitly defined the enumeration, we
give another proof. However, a formal proof based on the above ideas can
also be given.
Proof. Let u : (n, k) H 2n-1(2k-1). By Example 5.7 and Exercise 5.9.6
u is one-to-one and onto N. Consequently u-1 is an enumeration of N x N.
0
u
00
Let Ak be countable for every k
Theorem 9.6
E
N. Then
Ak
k=l
is countable.
A simple memory aid for Theorem 9.6 is: a countable union of countable
sets is countable.
Proof. Without loss of generality we can assume that each Ak is nonempty. By Theorem 9.2 each Ak is the range of some sequence, say
ak: m + + a k ( m ) .
Let w be an enumeration of N x N,w : n H (m, k ) . Then n I-+ ak(m)maps
u
00
N onto
Ak, and by Theorem 9.1 this set is countable.
0
k=l
Theorem 9.7
Proof.
onto
For k E
The set Q is countable.
N
Ak, the set Ak
define
Ak
=
(f;
mE
z}.
Since m
++
is countable by Theorem 9.3. Since Q =
m
- maps
k00
u
z
Ak, we
k=l
see that Q is countable by Theorem 9.5.
Theorem 9.8
10, 1[ is not countable.
0
Sets Revisited
235
The method of proof used here is known as Cantor's diagonal process
(or Cantor's diagonalization process), and is often used on other occasions
(see, for example, Exercise 9.1.6). The proof uses decimal fractions for real
numbers, as expounded in Example 5.8.
Proof. Assume 10, I[ is countable, then 10, 1[is a range of some sequence
a : n H a,. With each a, we associate its decimal fraction 0.aya;a:. . .
(here n is an upper suffix, not the index of some power). We now define
a sequence b, by b, = 5 if a: # 5 and b, = 1 if a: = 5 . This process is
illustrated in the following diagram where a+ # b l , a; # b2, a: # b3, . . . ,
a%# bi, . . . , are marked, showing the reason for the adjective "diagonal".
1 1 1 1 1
a1 = 0.#1a2a3a4a5..
.ai1 . . .
a2 = 0.a1#2a3a4a5..
2 2 2 2 2
. ai2 . . .
a3 = O . a ~ a ~ & a. .~.a:
a ~. . .
a4 = ~ . a4 ,4a ~
4 a4~4g t. ~
.aat ,. . .
Let B
have
= SUP
{ Bn; B
bl
-- 10
b2
b3
++lo2 lo3
+
.
a
,.
+&}.
10,
For m > n we
b7n
.. .+ lom '
--
lo
B 5 Bn+ 9
5 10
1" < Bn+--.1 From
It follows from these inequalities that
10"
the definition of B it is obvious that B, 5 B , and so the decimal fraction
Introduction to Mathematics with Maple
236
associated with B is 0.blb2b3.. .; for the definition of decimal fraction see
Example 5.8. It is also easy to see that 0 < B < 1. The decimal fraction
for B differs in at least one place from the decimal fraction for any a,.
Consequently B # a, for every n E N, contradicting the assumption that
0
the range of a was 30, l[.
An obvious consequence of Theorem 9.8 is that R is not countable. Any
x-a
interval ] a , b[ is not countable either because b : x I--+ -is a bijection of
b-a
] a , b[ onto 10, I[. Also the set of irrationals, that is, R \ Q is not countable:
if it were then R would be countable as the union of R \ Q and Q.
Theorem 9.8 shows that there is at least one infinite set which is ‘larger’
than the set of natural numbers.
The next theorem describes enumerable sets as the smallest infinite sets,
or, in other words, that the natural numbers are equivalent to any of the
smallest infinite sets.
I/ Theorem
9.9
Every infinite set contains an enumerable part.
l
Proof. [First attempt] Let A be infinite, and choose a1 E A . The set
A \ { a l } is non-empty (it is even infinite) and we can choose a2 E A \ { a l } .
Continuing with this process indefinitely we select
Then B = { a l , a2, a3, . . .} c A and n H a, is an enumeration of B.
0
The phrase “continuing with this process indefinitely” in the above ‘proof’
looks suspect. Can the proof be made impeccable by replacing the offending
phrase by a reference to one of the recursion theorems? The trouble is that
we have no function g as in Theorem 5.2 or gn as in Theorem 5.13 at our
disposal, and since the theorem we are trying to prove refers to “every
infinite set” there seems little hope of devising such functions.
Arguments such as those used in the above ‘proof’ had been freely used
in mathematics until it was realised that their acceptance hinges on the
validity of the following statement-which has come to be known as the
axiom of choice.
Axiom of Choice If S is a family of non-empty sets then there exists a
function G such that G ( S )E S for every S E S.
Sets Revisited
237
The great German mathematician David Hilbert compared the family
S and the function G to a group of societies each selecting its president.
From this point of view the axiom of choice is very plausible. However, the
difficulty with its acceptance in mathematics lies in its great generality: it
asserts the existence of G, for any infinite family of sets S, and, furthermore,
each of the sets S in the family may also be infinite.
We now return to the proof of Theorem 9.9: this time, using the axiom
of choice, we can provide an impeccable proof.
Proof. From the axiom of choice there exists a function G which associates with every non-empty subset B c A an element G ( B )E B . We now
define
and inductively, for n E N,
The sequences n H An and n
an are uniquely determined by the recursion theorem. To see this we define f , S and g in Theorem 5.13 as
follows:
Now a, becomes the first element in the couple f ( n )and n
enumeration of a part of A .
an is an
0
Exercises
3
Exercise 9.1.1
below.
Define a bijection for each of the pairs o f sets given
Introduction to Mathematics with Maple
238
N,No
(1)
(3)
(0,4 R;
[o, 13, 30, I[;
(5)
(7)
@
Exercise 9.1.2
zx z,N;
(2)
(4)
(6)
(8)
[o, 13, [3,71;
[a, bl, [d, el;
Z x Z,Z;
Q x Q, N.
Prove that ifA is not countable and B is countable then
A \ B is not countable.
Exercise 9.1.3
Show that the following sets are countable:
(1) { a + b&; a E Q, b E Q};
(2) {x: x 2 + p x + q = o ; p E Q , q E Q } .
I f n E N and A l , A2, A S , . . . , A, are countable, then
the set A1 x A2 x A3 x - - x A, is also countable. Prove this.
0 Exercise 9.1.4
0 Exercise 9.1.5
countable, then so is
I f L is countable and for every 1 E L the set A1 is
A1 . Prove this slight generalisation o f Theorem 9.5.
u
IEL
0 Exercise 9.1.6
Use the diagonalization process from Theorem 9.8 to
prove the following theorem. Let S be the set o f all sequences having
(0, 1) as their ranges. Then S is not countable.
0 Exercise 9.1.7
Let S be the set o f real numbers in 10, I[ such that their
decimal fractions do not contain the digit 7. Show that S is not countable.
[Hint: Most of the work has already been done in the proof of Theorem 9.81.
0 Exercise 9.1.8
Prove, without using the axiom of choice, that the set
B = {x; x irrational, 0 < x < 1) has an enumerable part.
1
0 Exercise 9.1.9
1,
n E N}.]
The range of the sequence
1 2 1 2 3 1 2 3 4 1 2 3 4
2’5’3’ 3’ 3’ 4’4’4’4’5’5’5’5’- -
contains all rationals between 0 and 1. Use this sequence t o define a bijection Q onto N.
Chapter 10
Limits of Sequences
In the early sections of this chapter we introduce the idea of the
limit of a sequence and prove basic theorems on limits. The concept of a limit is central to subsequent chapters of this book. The
later sections are devoted to the general principle of convergence
and more advanced concepts of limits superior and limits inferior
of a sequence.
10.1
The concept of a limit
If we observe the behaviour of several sequences, for example
(10.1)
(10.2)
(10.3)
(10.4)
we see that as n becomes larger the terms of each sequence approach a
certain number: for Sequences (10.1)' (10.2) and (10.4) it is 0, for (10.3) it
is 1. In Sequences ( l O . l ) , (10.2), (10.3) and (10.4) there is a clear pattern
in this approach. In the next example the terms of the sequence approach
a certain number quite irregularly. The following table is a record of an
experiment: casting of two dice and noting as a success whenever the sum
is 7. The columns headed n in Table 10.1 indicate the number of throws,
and the other columns gives the values of s / n where s is the number of
239
Introduction to Mathematics with Maple
240
successes in n trials.
Table 10.1 Results from throwing two dice
n
1000000
1000001
1000002
1000003
1000004
1000005
1000006
1000007
1000008
1000009
1000010
1000011
1000012
1000013
1000014
1000015
n
.166553
.166552833
.166552667
.1665525
.166552334
.166552167
.166553001
.166553834
.166553668
.166553501
.166553334
.166553168
.166553001
.166552835
.166552668
.166553502
1000016
1000017
1000018
1000019
1000020
1000021
1000022
1000023
1000024
1000025
1000026
1000027
1000028
1000029
1000030
.166553335
.166553169
,166553002
.166552835
.166552669
.166552502
.166552336
.166552169
.166552003
.166551836
.16655167
.166551503
.166551337
.16655117
,166552003
There is no discernible pattern this time but (assuming the dice are unbiased) the terms of the sequence of values of s / n approach the number
.16666.. . = 1/6. What is characteristic is that after one million terms,
all subsequent terms of the sequence are approximately equal t o .1666.. .
within three decimal places. If we required higher accuracy we would have
to keep casting the dice longer but the same phenomenon would occur;
after casting the dice sufficiently often the terms of the sequence will be
approximately equal t o 1/6 within the required accuracy. This leads us to:
Tentative temporary definition. The sequence n I+ zn has a limit 1 if,
for any prescribed accuracy, there exists N such that for n > N , the terms
x, are approximately equal t o 1.
In the example of casting two dice for accuracy to three decimal places
the number N was one million. In Sequences (10.1) and (10.2) the limit is
clearly zero and for accuracy of k decimal places the number N would be
Limits of Sequences
-
241
2 lok. For Sequence (10.3) the limit is again zero but it is not so obvious
what N should be. Since
that
’
-I- ( - ‘ ) n
2n
1
+ (-1)”+’ 5
1
<
2n
1
;
for n > 3 it is clear
will be approximately equal to zero to k decimal places if
n > 2.1Ok. Hence 2.10kwill do for N . Perhaps this N is unnecessarily large
but that is of no importance; the tentative definition requires existence of
some number N (with the required property) and soon as some N is found,
the job is done.
It is undesirable and often would be inconvenient to use a decimal fraction to measure accuracy. It is simpler to use a positive number, say E , and
say that xn is approximately equal to I within E if
\
Definition 10.1 (Limit of a sequence) A sequence n H xn E CC
is said t o have a limit I if for every positive E there exists a real number
N such that
/
whenever n > N . If the sequence n H xn has a limit I we write
lim = I
n+m
or
xn
-+
I.
A sequence which has a limit is called convergent. A sequence which is
not convergent is called divergent. Definition 10.1 is often referred to as the
E-N definition of the limit.
The phrases “the sequence converges to 1” or the “sequence approaches
I” are used with the same meaning as “the sequence has a limit 1 ” .
The arrow in the notation xn -+ l indicating that I is the limit of the
sequence x must not be confused with the arrow indicating the functional
dependence as in n H x n . Hence n H I defines a sequence with every term
equal to I , whereas xn -+ l states that I is the limit of the sequence n H 2,.
Example 10.1 The sequence n H xn = c has a limit c. Since Ixn -cc( = 0
for every n we can choose any number for N , for instance N = 1, and
0 = lxn - I1
whenever n
>N.
<&
Introduction t o Mathematics with Maple
242
Example 10.2
1
The sequence n
1 .
n+l
is not bounded from above by Theorem
H
- IS convergent t o zero, t h a t is
+ 0. Let E > 0. The set N
n+l
4.8 and consequently there exists N E N such that I/E
< N . If n > N then
1
1
1
0 < -< - < E . This proves that lim -- 0.
n+l
N
12-00
n 1
+
1
Theorem 10.1
Every sequence has a t most one limit.
~~
I'
-11
'I
Proof.
Assume, contrary to what we want to prove, that the sequence
> 0. Choose E = $1L - Z I .
Then by the definition of the limit there exist numbers N1, N2 such that
lxn - LI < E whenever n > N1 and Ixn - lI < E whenever n > N2. Let
N = Max(N1,N2). If n > N then
x has two distinct limits l , L , with IL - lI
This is manifestly wrong and we reached a contradiction.
0
We now make some important comments concerning the definition of a
limit. (Definition 10.1
( A ) The number N from the definition depends on E : this is sometimes
emphasised by writing N ( E ) .Generally, the smaller the value of E,
the larger the value of N .
(B) If n H xn has a limit l then there are many N such that Ixn - lI < E
for n > N . As soon as we have a suitable N , then, for example,
N + 1, N + 13 and 2N are also suitable: any number greater than
N is suitable.
(C) The meaning of Definition 10.1 is not changed if, instead of permitting N to be any real number we restrict it to N E N.
(D) The symbol lim xn is to be understood as an indivisible quantity.
n-00
It simply denotes the limit of the sequence x and does not introduce
the symbol 00 in itself. In particular 00 does not denote a number.
(E) The definition makes it possible to check whether or not a number
1 is the limit of a specified sequence. It provides no assistance with
finding the value of 1. If we can guess what the limit should be then
we can use the definition to prove that our guess was correct, or
show that our guess was incorrect. Obviously this might be difficult
or not feasible. It is, however, possible to prove powerful theorems
Limits of Sequences
243
concerning limits from the definition, and then to use these theorems
to find limits. Some such theorems are given in the next section.
(F) Consider now a real sequence x , that is, a sequence whose terms are
real numbers Xn. Since the inequality Ixn - 11 < E is equivalent by
Theorem 4.4 to
all terms of the sequence with an index greater than N lie in the
interval 31 - E , 1 E [ . This means that outside this interval can lie
only terms of the sequence with n 5 N , which are finitely many. If
the terms xn are graphed as points on the number line then they
cluster near 1 in the interval 11 - E , 1 E [ (see Figure 10.1).
+
+
Fig. 10.1 Limit of a real sequence
For a complex sequence x , that is, a sequence xn E C for all n, the
inequality lxn - 11 < E means that the distance between xn and I is
less than E . Geometrically this means that, in the complex plane,
xn lies in the circle centered at I and of radius E . If the sequence
has a limit 1 the points xn in the complex plane cluster near 1 in the
circle centered at 1 and of radius E , see Figure 10.2, where the terms
of the sequence starting with 2 5 lie within the circle.
It goes without saying that the letter n in the symbol lim Xn = I
n-+m
can be replaced by any other letter without altering the meaning.
Thus lim Xn = lim xk = lim Xm.
n+m
k+m
m-mo
Sometimes
lim xn is abbreviated to limx,.
We shall not do
n+m
that in this book. If several letters are involved in the formula
for xn, confusion can arise. It is not clear what is the mean-
i).
(5
+ i) +
+
ing of lim ( n
lim
n-+m
=1
n+2
(J> The notation xn
It is left as an exercise to show that
'
-+ 1
2
n
has the same disadvantage
" as abbreviatinc
u
Introduction to Mathematics with Maple
244
Complex plane
0
Fig. 10.2 Limit of a complex sequence
lim
xn
n+co
in l / n
to lim xn. However, if no confusion can arise, as for example
--+
0, we shall use this notation for its convenience.
(K) It is easy to see by comparing the definitions that xn --+I if and only
if Ixn - I1 -+ 0. In particular, for I = 0, we have that Xn -+ 0 if and
only if
Ixnl -+
0.
Let a be a sequence of complex numbers and
members of both sequences
bn = an+l.
Listing a few
(10.5)
and
bl = a2, a3,
a4,. . .
(10.6)
shows that the sequence b is obtained from a by omitting the first term and
renumbering.
Limits of Sequences
245
Theorem 10.2 With the notation above lim
n+m
if lirn bn exists and then both limits are equal.
an
exists if and only
n+m
Proof.
Since the wording of the theorem includes 'if and only if' the
proof comes in two parts.
I. If lim an = 1 then for every E > 0 there exists N such that Ian - 11 < E
n-mo
> N. Since bn = an+l, it follows that Jbn - I1 < E for n > N.
11. If lim bn = 1 then for every E > 0 there exists Nl such thatlb, - I1 < E
n+m
for n > N1. This means that Ian - 11 < E for n > N 1 + 1.
0
for n
The sequence b is obtained from a by omitting the first term. It is clear,
by using Theorem 10.2 repeatedly, that omission or change of finite number
of terms affects neither the existence nor the value of the limit. Because
of this we extend convergence, divergence and limits to functions defined
not on all of N but only on N \ A , where A is a finite set and we also call
such functions sequences. For instance, if a n = l / ( n - 5 ) we would say that
lim l/(n- 5) = 0 although the function a is not defined for n = 5 .
n+m
The Sequence (10.3) is convergent t o 1, that is,
1
1
lim -- 1. Consider that -. Since lirn -= 0,
Example 10.3
'I=
n
12-00
n 4- 1
by Example 10.2, for every
InP1
E
> 0 there exists N
such that 0
n > N . Consequently
for n
<
+
n 1
1
-< E for
n+l
n+m
>N.
In Example 10.3 we used our knowledge of one sequence to establish the
limit of another sequence. The same idea is contained in the next theorem.
Theorem 10.3 Assume that 5, E R and
X n -+ I , yn + 1 and there is an N1 such that
Yn
E R for all n E N. If
xn L zn L Yn
for n
Proof.
(10.7)
> N1 then Zn is convergent to 1.
For every E > 0 there exist
1-&
N2
and N3 such that
< x, < l + &
(10.8)
Introduction to Mathematics with Maple
246
for n
> N2 and
for n > N3. Let N = Max(N1, Nz, N3). If n > N then all Inequalities (10.7), (10.8) and (10.9) hold. Consequently
and this implies
IZn
- ZI
< E.
0
We shall call Theorem 10.3 the Squeeze Principle. This is quite intuitive:
zn is squeezed between xn and Yn, and as xn and gn approach I , so does
zn -
Example 10.4
n
1
dm
To prove it we use the Squeeze principle but estimate
first. We
k=l
have
and
Now we set
Application of the Squeeze Principle completes the proof.
Example 10.5
that for every k E
1
n
Since lim - = 0 it follows from the Squeeze Principle
n-mo
1
N,n-oo
lim - = 0.
nk
The Squeeze Principle dealt with real sequences. It has, however, an
important consequence for complex sequences.
Limits of Sequences
247
Corollary 10.3.1 Let z be a complex sequence and y a real sequence. If IZn - 11 < Yn for n E N and lim Yn = O then lirn Zn = 1.
n+w
Proof. Since 0 5 IZn - 11 <
lim IZn - 11 = 0 and by (K) lim
n+m
Example 10.6
zn
Let z 2 k - 1 = 1-
terms of the sequence are
1
the Squeeze Principle implies that
Yn
n+m
n4m
= 1.
0
1
Ic -I5,
z 2 k = -for k E N.The first few
k
3 3 7 4 1 5 5
2’2, 4’2’;’
$7
16’p - ’
It is not difficult to see that zk + 1. We prove it. Since l z 2 k - l - 11 =
1
1
2
- Iz2k - 11 = - we can, in applying the corollary, choose yk = - (since
2k
’
k
k
by Exercise 5.1.11 we have 2‘ 2 k ) and then I z ~- 11 5 yk for every k E
Obviously y k --+ 0 and it follows from Corollary 10.3.1 that Z k + 1.
N.
Theorem 10.4 Assume that z is a complex sequence and 1 a complex number with Xn = %Zn, Yn = %Zn, p = % I and q = 91, Then
zn
=I
(10.10)
lim
xn
=p
(10.11)
lim
yn
= 4.
(10.12)
lim
n+m
if and only if
n-co
n+co
Proof.
I. Assume (10.10). Since
(10.13)
and the right hand side of (10.13) approaches zero by (K) we can apply
Corollary 10.3.1 again and we have Equation (10.11). The proof of Equation (10.12) is similar.
11. For e > 0 there are numbers N 1 , N 2 such that lxn - pl < e/2 and
gn - 4)< e/2 for n > N1 and n > N 2 , respectively. Hence
~
l A formal proof is similar t o the one given in Example 10.2.
Introduction to Mathematics with Maple
248
0
This proves (10.10).
Not every sequence has a limit. For instance the sequence n I-+ (-l)nis
divergent. Assume, for an indirect proof, that ( - l ) n-+ 1. Then for E = 1
there exists a number N such that l(-l)n- ZI < 1 for all n > N . Choose a
natural number k with k > N.Then
I(-l)'"
-
ZI < 1
and
I(-l>"+'- ZI
< 1.
Consequently
2=
I(-ly - z + z - (-1)"+'1
5 I(-ly - ZI + I(-l)"+' - ZI < 2.
This contradiction completes the proof that the sequence has no limit.
Exercises
Exercise 10.1.1
Prove that
C
(1) lim - = O for every c E C;
n+oo n
1
( 2 ) lim n+W
= O;
n2 2
(3) lim -= 0.
fi
+
n-m n 3
+n
[Hint: For (1) mimic the proof of Example 10.2.
Use that the set
n2+2
2
n3-1-n
n
{ fi;n E N} is not bounded for ( 2 ) . For (3) show that -< - and
apply the Squeeze principle.]
4n+ 1
Write down
2n 1
the first eight members of the sequence a. Find N1 such that lan - 21 < l o 3
for n > N l . For E > 0 find N such that lan - 21 < E for n > N. [Hint:
Simplify Ian - 21 .]
Exercise 10.1.2
Let
~
~
4n+1
2n
= -1
and ~2~ =
-.
+
Exercise 10.1.3 Let a1 = 100 and an++'= (a1- n)an for n E N. Write
down the first five terms of the sequence. Find the limit.
Exercise 10.1.4
Show that the sequence with terms
1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 ,...
has no limit.
Limits of Sequences
@ Exercise 10.1.5
249
Prove that the meaning of Definition 10.1 remains
unchanged if
(1) the inequality n > N is replaced by n 2 N ;
(2) the inequality lx, - ZI < E is replaced by lx, - Z I 5 E ;
(3) for some positive EO the requirement that E is positive is replaced by
the requirement that 0 < E 5 E O .
0 Exercise 10.1.6
Prove: If 1 E R and for every k E N there exists N
such that LIOkxnJ= l l O k l ] for n > N then x, + 1. Is the converse true?
@ Exercise 10.1.7
inequality la1 < E
Let a E (C have the property that for every E > 0 the
is true. Prove that a = 0. [Hint: For an indirect proof
choose 2~ = la1 .]
@ Exercise 10.1.8 Let x be a sequence with the property that for every
E > 0 there exists a number N such that Ix, - I [ < E , for n > N and some
I E .R. Prove that xn = I for n > N . This shows the importance of the
order in which E and N appear in Definition 10.1. [Hint: Use the previous
exercise.]
10.2
Basic theorems
Definition 10.2
bounded above on a s e t S if the set f ( s ) is bounded, bounded below,
bounded above, respectively. The function f is said to be bounded if it
is bounded
~
~~
This means that a complex sequence x is bounded if and only if there
exists a number K such that
Theorem 10.5
Every convergent sequence is bounded.
Proof. Let xn + 1. For
> N . It follows that
n
E
= 1 there exists
N such that Ixn
- I1
< 1, for
Introduction to Mathematics with Maple
250
for n > N . Let S1 = { x ; n > N } and S2 = { x ; n 5 N } . The set S1 is
bounded by (10.14) and S2 is bounded because it is finite. Consequently,
rgx = S1 U S2 is bounded.
0
xn
(iv) if m # 0 then - --+
Yn
-.1
m
An important special case of (iii) is Y n = c for every n E N.
This yields cx, --+ CZ. By taking c = -1 we obtain -xn + -I and then it
follows from (ii)that xn - Y n -+ 1 - m. By taking Y n = xn it follows from (iii)
t h a t x: --+ Z 2 and by simple induction that xk -+ Z k for k E N. For x, = 1 it
follows from (iv) t h a t l / y n --+l/m. It follows similarly from (iii) and (iv) t h a t
y i k --+ m-IC,provided m # 0.
Remark 10.1
Proof.
(i) by (K) of Section 10.1 I X n - I [ -+ 0 and since
follows from Corollary 10.3.1 that IZnl --+ 111.
(ii) For 7 > 0 there exists Nl and N2 such that
IIXnI
- 1111
5
-ZI
it
>N
=
IXn
and
for n > N1 and n > N2, respectively. Consequently, for n
Max(N1, N2)
l(x,
+ Y n ) - (1 + m)l IIxn - + IYn - ml < 27 -
(10.17)
&
> 0, choose 7 = - and for this particular r] find iV1 and
2
N2 as above. Then for n > N we have from (10.17) that
Now take
E
This proves (ii).
0
Limits of Sequences
251
Remark 10.2
We now pause and reflect on the proof completed thus far.
Starting with (10.15) and (10.16) we finish with (10.17). This inequality is
almost the one required by the definition of lim (2,
Yn) = I
m, except
n-mo
+
+
that we have there 27 instead of 7. The fact that we used 7 instead of E is
irrelevant, it was an arbitrary positive number. Only a simple adjustment was
then needed to remove the factor 2. In a situation like this when we succeed
in showing that there is N such that
for al! n > N and some fixed
of Xn + L as completed.
M (independent of n) we shall regard the proof
We now return to the rest of the proof of Theorem 10.6.
Proof.
(iii) Since xn --+ I the sequence x is bounded by Theorem 10.5, that
is there is a K such that Ixnl 5 K for all n E N. Now, we have
obviously that
and it follows that
For E > 0 we can find N1 and N2 such that (10.15) and (10.16) hold
for n > N1 and n > N2, respectively. If n > N = Max( Nl , N2) then
In view of Remark 10.2 this proves (iii).
1
1
(iv) It suffices to prove that - --+ - because as soon as this is esYn
tablished (iv) follows by applying (iii) to Y n replaced by
m
1
-. Let us
Yn
choose E = - then there exists N such that (10.16) holds for n > N .
2
Consequently
for n
> N . In particular
Yn
# 0. It follows that
1
2
Introduction to Mathematics with Maple
252
for n
>N.
Now we have
By Remark 10.1 and (K) of Section 10.1
and by Corollary 10.3.1
1
- --+
1
-.
m
Yn
0
Theorem 10.6 is important in itself for the further development of the
theory but it is also a powerful tool for calculating limits.
Example 10.7
lim
72-00
I + ; 4- ?
n2+4n-7
2n2 - n + 1
Example 10.8
=
lim
n-oo
7
+ -$
2-$
-1imn+m(l+?-$)
-1
-
-
limnjOO (2 -
+ $)
2'
We wish t o find
1 - 2 -l-3 - 4
lim
First we realize that 1- 2
-1
<
+
- 2n
$7XG
n-mc
+3 -4 + - - -
1- 2 -I-3 - *
- 2n
= -n, then we estimate
- - - 2n < -- n
YiTCFL
(10.18)
n+l'
Now
n
lim -= lim
n-mn+l
n-oo
1
- 1.
1+n
Using the Squeeze principle and Equation (10.18) above yields
lim
1 - 2 + 3 - 4 + . - . - 2n
ViTZi
n-oo
-
~~
~
Theorem 10.7 If X n
I and Yn
then there exists N such that
=+
Xn
for all n
>N.
= -1.
+m
< Yn,
with X n ,
Yn
E R and 1
<m
Limits of Sequences
Proof.
Let
E.
=
253
m-1
-, then there exists iV1 and N2 such that
2
and
l+m
m-&=< Y n < m+E,
2
for n > Nl and n > N2, respectively. For n
from these inequalities
> N = Max(N1, N2) we obtain
0
A memory aid for Theorem 10.7 is: Inequality between limits implies a
similar inequality ultimately for the terms of the sequences.
Corollary 10.7.1
for n > N .
Proof.
If ym
-+
m > 0 then there exists N such that Y n > 0
It suffices to take x , = 1 = 0 for every n in Theorem 10.7.
Corollary 10.7.2
< m for n > N .
If xn
-+
0
1 < m then there exists N such that
X n
Proof.
It suffices to take Y n = m for every n in Theorem 10.7.
Theorem 10.8
for n E N then 1
If xn
I m.
+ 1,
yn
-+
m with
Xn,
Y n E Iw and if xn
0
IYn
Proof. If m < 1 then by Theorem 10.7 (with the roles of x n and Y n
interchanged) Y n < xn for some n E N,contrary to our assumption.
0
Taking Y n
=m
yields
Corollary 10.8.1
If X n
+1
and X
n
5 m then 1 5 m.
The next two examples illustrate the role of strict and non-strict inequalities in Theorems 10.7 and 10.8.
Example 10.9
If X n = 0 and Y n = - l / n then 1 5 m since 1 = m = 0.
However it is not true t h a t xn 5 Y n for sufficiently large n; we even have
yn < xn for all n E N.
Introduction to Mathematics with Maple
254
If xn = - l / n and Y n = l / n then xn
However the inequality I < m is false since I = m = 0.
Example 10.10
< yn
for all n E N.
Exercises
Exercise 10.2.1
Find the following limits
+
+
+
1000n3- 2n 1
n-*
1
10n 5- loon3
17'
-_
(3n 2)2 - (n I ) ~
(3) lim
72-00
(3n 2)3 (n 1)3
1+2+3+-+n
(4) lim
2
n-oo
n+2
( 2 ) lim
+
(
+ + +
-'>
@ Exercise 10.2.2
Prove: For every c E Iw there are sequences xn +
c with xn E Q, Y n 4 Q. [Hint: Consider LncJ/n and
LncJZ1/ ( n f i ) . ]
C,
yn
+
Exercise 10.2.3
Prove that
[Hint: Use the inequalities LkxJ 5 kx, k x - 1
principle.]
@ Exercise 10.2.4
If X
n +
< [kxj and the Squeeze
Xn+1
I # 0 then + 1. Prove this. Also give
X n
examples of convergent sequences n I--+ xn such that
(1)
Xn+1
X n
diverges; (ii)
Xn+l
+c#1.
X n
Exercise 10.2.5
Give examples of divergent sequences x and y such
that (i) x y is convergent; and (ii) x y is convergent.
+
Exercise 10.2.6
For A > 0, B > 0 and q E N prove
k=O
Limits of Sequences
@ Exercise 10.2.7
255
Using the previous exercise and the Squeeze principle
show that
5, + 1
for q E
-+
6+ 4 q
N.
@ Exercise 10.2.8
x,
> 0 =+
Use the previous exercise to prove that x:
4
'I if
1 and r E Q.
@ Exercise 10.2.9
Find the following limits
(1) n+m
lim
(diFTi - d z ) ;
(a) lim
f-7
n+w
8n2-1
'
[Hint: If you are finding some exercises difficult, we will show how to use
Maple to solve them in the next Section.]
Exercise 10.2.10
Prove: If x, y are real sequences with zn + 1 and
y, --+ m then Max(z,, y,) + Max(l, m) and Min(z,, yn) + Min(1, m).
[Hint: use either Definition 10.1 or the identity Max(a, b) = i(a+b+la-bl).]
10.3 Limits of sequences in Maple
Maple can find limits of sequences quickly and efficiently even if the limit
is hard to find otherwise. Simply, instead of writing lim f(n) we issue
n+w
the command limit (f (n) ,n=inf inity). On most occasions this would
be sufficient but the command is incomplete: we have to tell Maple that n
(or m or whatever notation we use) is a positive integer. This can be done
Introduction to Mathematics with Maple
256
either with assume or assuming. We illustrate both possibilities. We wish
to calculate lim (n 4- ‘I2 and lim
~
n-oo
3n2
71-00
(d-
-
>
assume(n: :posint) ;
>
limit ( ( (n+2)-2)/ (3*n-2),n=infinity) ;
d m ) .
1
-
3
limit (sqrt(nn2+2*n+3)-sqrt (nn2+n-i),n=infinity);
>
1
2
-
n := n
It is a good habit to clear the assigned variable if it is not needed any
more. For calculating just one limit it is more convenient to use assuming.
In the next example we need a cubic root. The command to use for odd
roots is surd, since using fractional exponents can bring in complex roots
which are undesirable. Now we calculate the limit from Exercise 10.2.9 part
(ix).
~
>
limit(m+surd(l-mn3,3),m=infinity)
~~
assuming m::posint;
0
> limit ( (k-2)* (k+surd( l-k^3,3) ) ,k=inf inity) assuming
k::posint;
1
3
-
Neglecting the assuming part of the command can lead to incorrect
results. For instance lim sin(n.n) = 0 but
n+oo
~
>
~
~~
~~~
limit (sin(n*Pi) ,n=infinity);
-l..l
This result does not look right. We have to accept that we ask a wrong
question and got a wrong answer.
Limits of Sequences
257
Exercises
Exercise 10.3.1
Use Maple to evaluate the limits in Exercise 10.2.9.
@ Exercise 10.3.2
What happens if you use Maple to solve Exercise
10.2.3. [Hint: Maple cannot solve this problem directly.]
3n
Exercise 10.3.3
Let x, =
and yn = q n 3 + 6 n 2 f 8 n 2n 1
q n 3 n2 - 9. Find lim MaX(X,, yn).
+
+
n-cc
10.4 Monotonic sequences
We shall assume in this section that all functions (and sequences) are real
valued
Definition 10.3
tion f is said to be increasing on a set S if for every 51, x2 E S
21
5 x 2 =+ f ( X 1 )
I f(x2).
(10.19)
f is said to be increasing if it is increasing on dom f . By replacing the
implication (10.19) by
21
< 5 2 =+ f (21) < f ( 2 2 )
we obtain the definition of a strictly increasing f (on S ) , and by reversing the inequality f ( X I )
f (x2) or f (x1) < f(x2) we obtain the
definition
<
For example the function x H x2 is strictly increasing on the interval 10, oo[,the function x H x3 is strictly increasing and l p , that is, the
characteristic function of the interval 10, oo[,is increasing but not strictly
increasing.
Sequences are functions defined on N. Consequently Definition 10.3
applies to sequences as well. Clearly a sequence x is increasing if and only
if x,
x,+1 for every n E N. It should be clear what is meant by a strictly
increasing, decreasing or strictly decreasing sequence.
A function which is either increasing or decreasing is called monotonic.
Monotonic sequences play an important role in the theory of limits.
<
Theorem 10.9
Every bounded monotonic sequence is convergent.
Introduction t o Mathematics with Maple
258
Since a strictly increasing sequence is increasing, a strictly decreasing
sequence is decreasing, and -x is increasing if x is decreasing, it is sufficient
to prove
Theorem 10.10
If n H xn is increasing and bounded above then
it is convergent and
Proof. Denote by 1 the supremum on the right hand side of (10.20). Let
E > 0. Since 1 - E < 1 there exists, by definition of the least upper bound,
a number N such that 1 - E < X N 5 1. Since xn 2 X N for n > N we have
for n
>N.
Remark 10.3
0
If n H x , is decreasing and bounded below then
Remark 10.4 For an increasing sequence n H xn with limit 1 we shall
write xn 7 1 , and similarly if xn decreases with limit 1 we shall write xn -1 1.
Although Theorems 10.9 and 10.10 sound theoretical their application
often enables us to find limits.
Example 10.11 We show for b E CC that bn -+ 0 if lbl < 1. We define
xn = lbl". Obviously xn+1 = I b l X n , hence the sequence n I-+ X n is decreasing,
it is also bounded below by zero so it has a limit, say 1. Passing t o the limit in
the previous equation gives 1 = IbJl. Consequently 1 = 0 and lbln .1 0. By (K)
of Section 10.1 it follows that bn -+ 0.
Remark 10.5 If IcI > 1 then n H cn cannot be bounded. If it were then
lcnl 1 1 and the same argument as in Example 10.11 would show that cn + 0,
which is clearly impossible.
Example 10.12
for a
> 0.
We show that
The case a = 1 is obvious.
Limits of Sequences
259
fi
Consider first a > 1. The sequence n -+
is decreasing and bounded
-1 c. Then 1 5 c 5
and
from below by 1. Hence it is convergent,
consequently 1 5 cn 5 a. This means that the sequence n + cn is bounded
so clearly c = 1.
and therefore IcI 5 1. Hence we have IcI 5 15 c 5
If 0 < a < 1 then 1/u = b > 1. By Equation (5.33) and by what we have
fi
fi,
just proved
fi
--j
1. By Remark 10.1 we have
=
1
- -+ 1, proving the
result.
b
v
The use of the existence assertion from Theorem 10.9 or Theorem 10.10
in Examples 10.11 and 10.12 is essential. Consider the sequence n H (-l)n
and denote its limit I . Since ( -l)n+l = -( -1)" we obtain by passage to the
limit that I = -I, that is I = 0. This conclusion is false since the sequence
in question has no limit.
The sequence n H (1
Example 10.13
+
i)n
is convergent and its limit
is denoted by e. This number plays an important part in mathematical analysis.
We first prove that the sequence is bounded. By the binomial theorem
(1
+
;)n
=
2 (;)$
k=Q
=
l+nL
n + L(12!
+L(l-;)
n!
i)+ $ A) :) +...
(l-;)...(l-G)
(1-
(1-
1
2!
1
3!
< 1+ 1+ - + - + For n
(1
> 2 we have n! > 2n-1
+ ;J
* *
1
+n!
(10.21)
'
and consequently
1
< 1 + 1+ + . . . + -1
2!
n!
1 1
< 1 + 1 + ~ +22- + " ' + -2"-1
< 3 (10.22)
We next prove that the sequence is increasing. In the arithmetic-geometric
mean inequality (Theorem 5.10)
(10.23)
Introduction to Mathematics with Maple
260
n+l
n+l
(10.24)
Example 10.14
Let yn = 1
1
1
1
+ 1+ +
- + - - - -.
2!
3!
n!
The sequence
Yn
is obviously increasing and by (10.22) it is also bounded. Thus it has a limit,
Y n E, say. We are going to show that E = e. Firstly, we have from (10.21),
on using Theorem 10.8, that e 5 E. let us now estimate
(1 -
A)
(1 -
;)
... (1 -
+)
from below. Using Bernoulli’s inequality (Theorem 5.9) we have
r-1
for r, n E N, 1 < r
c
n
since
1
21--
r(r - 1)
n
5 n. Hence
<3
by (10.22). Passing to the limit in the last inequality
r-2
and using Theorem 10.8 again we have e
2 E.
Consequently e = E and hence
1
1
+);
+ 1+ +
-+ -.2!
3!
1
n+m
=e.
This example enables us to calculate e with high accuracy. Firstly we
have Yn < e. Secondly, for m > n, we have
Ym
= yn
1
+ (n +1 I)! +-+...+(n + 2)!
~
1
m!
1
1
1
1
I Yn + -7
- yn + -.
n!n+l I-=
n!n
Limits of Sequences
261
1
This shows that Y n - is an upper bound for ym with m
n!n
1
therefore e 5 yn -.
Combining these results yields
+
> n and
+ n!n
1
1+1+ 2!
1
+3!
1
+
-
e
m
+
-<e
n! -
5 1+1+
1
2!
-
1
1
+ -+-..+
7 + L.(10.25)
3!
n. n!n
1
The number l / ( n ! n )decreases rapidly, for instance - <
4
1
10!10
1
+ 1+ 1
+ - - + 2approximates e with an error less than
2!
l O!
3
-,
108
hence
The cal-
culation will show that e 2.7182818. With the aid of computers e has been
evaluated to billions of decimal places. With the commands Digits :=2OO
and exp(i) you obtain e accurately for 200 decimal digits.
We now prove that e is irrational. It follows from Inequalities (10.25)
that 0 < e - yn 5 l / ( n ! n ) . If e were rational then e = m / n for some
n, m E N and then
The middle term in this inequality is an integer and this is a contradiction.
The French mathematician C. Hermite proved more, namely that e cannot be a root a of an algebraic equation with rational coefficients. This fact
is expressed by saying that e is not algebraic or that e is transcendental.2
The proof is out of our reach at this stage and the interested reader can
find the proof in Spivak (1967, Chapter 20).
Exercises
Exercise 10.4.1
Prove that the sequence
nw-
1
n+l
+ - +1. . . +
n+2
1
2n
-
has a limit. [Hint: Show that the sequence is increasing and bounded above
by 1.1
2A number is called algebraic if it is a root of an algebraic equation with rational
coefficients. By multiplying the relevant equation by a sufficiently large integer it will be
then a root of an equation with integer coefficients. Trivially every rational number a / b
is algebraic as a root of the equation bx = a. All concrete irrational numbers encountered
so far in this book were algebraic and it is not easy to produce a transcendental number.
262
Introduction to Mathematics with Maple
Exercise 10.4.2
Prove that lim - = 0 for any c E C. [Hint: Prove
Exercise 10.4.3
Find the following limits
Cn
n!
Icln/(n!) -1 0 b y an argument similar to that of Example 10.11 and then use
(K) of Section 10.1.1
(1) n-co
lim (1
(2)
+
$-ir (1 +
n-cc
:In+';
a)'";
[Hint:
(1) (1+
;)n+5
= (1+
;)n
(1+
;)5;
(2) ( l + a ) 2 n =[(1+;)n]2;
(3) (1-
(l+-) 1
n-1
n
(4) (1 -
$)
= (1
(5) Show that (1
+
-n
+ ;)"(1-
a>
n"
;
;);
L e, then (1 +
-$) L $.
Use the Squeeze principle and Example 10.12.1
Exercise 10.4.4
Find the following limits
+
+
+
5" j n
5n+1'
2n 3"
(3) lim
12-00
3n 5n'
(2) lim
n-+m
[Hint: Divide by the dominating term.]
Exercise 10.4.5
Find the following limits
n
Limits of Sequences
263
(3) lim q 2 n + 5 n + 13,;
n-+m
(4) Iim
n+m
qi7-F
[Hint: For (2)-(4) use the Squeeze principle.]
@ Exercise 10.4.6
Let a
> 0,
51
= 1, X n + l
(
2 &
=
2
X n
+:n)
. Prove
by the arithmeticthat lim x , exists and equals &. [Hint: x,+1
n+m
geometric mean inequality. Show that the sequence is decreasing after the
second term, then pass to the limit in the defining equation.]
@ Exercise 10.4.7
Prove that nan -+ 0 for la1 < 1. [Hint: Use the
method of Example 10.11. Let x , = nlaln. Then xn+l/x, = ( n l)lal/n
and consequently the sequence n ++ xn is decreasing.]
+
Let p E N. Prove that nPun -+ 0 for la1 < 1. [Hint:
use a similar procedure to that used in the previous exercise.]
@ Exercise 10.4.8
Exercise 10.4.9
x,
-+
10.5
Prove that if x ,
> 0 for n E N and %+'--+ 1 < 1 then
0. [Hint: x,+1 < 2, for large n, if x ,
X n
+L
then L = 1L.I
Infinite limits
In this section we shall consider only real sequences. Consider the first few
terms of the following sequences.
TL H a,
:
1, 2, 4,8, 16, . . . ;
3
5
7
n ~ b , :1 , 4 , -, 16, -, 36, -, . . . ;
2
2
2
(10.26)
(10.27)
nHc,:
1 , 2 , 1 , 2 , 1 , 2, . . . ;
(10.28)
n-d,:
1,2,1,3,1,4,1, ...;
(10.29)
None of these sequences has a limit but (10.26) and (10.27) behave quite
differently compared with (10.28) or (10.29). It is natural to say that the
terms of sequences (10.26) and (10.27) increase without limit. We would not
say that about (10.29) although this sequence, too, is obviously unbounded.
Introduction to Mathematics with Maple
264
Gefinition 10.4 (Positive infinite limits) A sequence x is s a i d 2
have the infinite limit +00 (or just 00) if for every K there exists N
such that
xn
for all n
,Xn
> N . If
>K
x has the infinite limit
(10.30)
00
+ 00.
we write lim xn = 00 or
n-mo
The limit in the sense of Definition 10.1 is sometimes called finite, particularly if one wants to emphasize that the limit is not infinite.
Sequence (10.26) has the infinite limit 00 since an = 2n-1 2 n. For a
given K it is sufficient to set N = K .
Sequence (10.27) also has limit 00, for we have bn 2 n / 2 and it is
sufficient t o choose N = 2K.
Sequence (10.28) is bounded and therefore cannot have an infinite limit.
We know that it has no finite limit either.
Sequence (10.29) is unbounded but has no limit either because all odd
terms are eaual 1.
\
/
Definition 10.5 (Negative infinite limits) A sequence x is said
to have the infinite Eimit -00 (or just -00) if for every k there exists
N such that
Xn
<k
for all n > N . If x has the infinite limit
Or X n + -03.
(10.31)
-00
we write lim x n = -00
n-cc
Instead of saying that a sequence has limit 00 (or -00) we may occasionally say that the sequence diverges to 00 (or -00).
Obviously lim (-n) = -00 whereas the sequence n H (-1)"n has no
n+m
limit, finite or infinite.
We emphasize again that we have not introduced any infinite numbers.
The statement lim x n = 00 or -00 refers to the behaviour of the sequence
n+oo
00
x and the symbol
itself has no meaning of its own. Perhaps we should
add that in mathematics infinite numbers are indeed introduced and used
advantageously, but very little, if anything could be gained by introducing
them here and now.
There is some similarity between the definitions of finite and infinite
limits. Some theorems concerning finite limits can be extended to infinite
Limits of Sequences
265
limits but not all can and care is needed. For instance, changing finitely
many terms of the sequence affects neither the existence nor the value of
the limit. Also a sequence can have at most one limit, finite or infinite.
The next theorem is the analog of the Squeeze principle.
Theorem 10.11 If xn 2 Y n for n E N and Y n
then xn 4OC) (yn + -00) also.
+= 00
(xn +
-00)
Proof. Let K be given. There exists N such that Y n > K for n > N.
It follows from the assumption that x n > K for n > N , which proves that
xn += 00. The proof for xn
--OO is similar.
0
--f
Theorem 10.12
Yn
If xn
+= 00 (-00)
and Y n is bounded, then xn
+
+= 00 (-00)-
Proof. There exists M such that lynl < M for all n E N. Since xn -+ 00
for an arbitrary K there exists N such that 2, > K
A4 for n > N.
Consequently X n Yn > K M - lYnl > K for n > N . The proof for
xn + -00 is similar.
0
+
+
+
If n + Yn is not bounded (for instance if Y n + -00) then the conclusion
of Theorem 10.12 need not hold. Let 2, = 2n, Y n = a - 272, Z n = -371,
un = -n, wn = -(2
(-l)n)n. Then xn += 00 and all the other sequences
diverge to -00, while
+
Xn
Xn
Xn
xn
I
Theorem 10.13
infinite).
+ Yn
+ zn
+
+
a,
+ -00,
U n += +00
+ w,
has no limit, finite or infinite.
(10.32)
(10.33)
(10.34)
(10.35)
Every monotonic sequence has a limit (finite or
Proof. It suffices to consider an increasing sequence n I+ xn. If it is
bounded then it has a finite limit by Theorem 10.10. If it is not bounded
then for every K there exists N E N such that X N > K . Since n + xn is
increasing it follows that xn > K for n > N .
0
Introduction to Mathematics with Maple
266
Exercises
@ Exercise 10.5.1
Prove that the meaning of Definition 10.4 remains
unchanged if
(1) the inequality n > N is replaced by n 2 N ;
(2) it is additionally required that N E Ni
(3) it is additionally required that K E N;
(4) the inequality X n > K is replaced by xn 2 K ;
(5) it is additionally required that for some fixed number a (for example,
a=O)K>a.
Some of the following exercises are stated as theorems. The task is to prove
them.
@ Exercise 10.5.2 If xn + 00 and Y n + c and c > 0 then X n Y n
c < 0 then X n Y n + -00.
Exercise 10.5.3
and
Give examples o f x , and yn such that xn
-+
-+ 00,
+
Prove: if zn + 00 or X n + -00
@ Exercise 10.5.5
Prove: if Ixnl +.
then 1xn1 -+
1
00
then - + 0.
Xn
@ Exercise 10.5.6
Exercise 10.5.7
(1) lim
n2
Prove: if a
> 1 then an -+
00.
Find the following limits
+ 3n - 10000
n+Jn
1 - 2
(2) lim
n-00 n2
1On - 1'
n-mc
7
+
(3) for Q
2 O evaluate
lim n"
12-00
Exercise 10.5.8
If xn
> 0 and f i + 1 > 1 then xn
Exercise 10.5.9
If xn
> 0 and xn+l
I > 1 then xn
-+
Xn
-+
00.
+ 00.
If
Yn +0
(1) X n Y n
00;
( 2 ) for a given a E R it is true that xnyn + a.
( 3 ) the sequence n -+ xnyn has no limit, finite or infinite.
@ Exercise 10.5.4
00.
00.
267
Limits of Sequences
10.6
Subsequences
If k
I-+ n k is a strictly increasing sequence of natural numbers, that is
E N and n k < n k + l , then the sequence k H X n , is called a subsequence
of the sequence n H xn. If we list the terms of the sequence
nk
(10.36)
we obtain a record of the terms of a subsequence by omitting some members
in (10.36). For instance
are terms of a subsequence of (10.36).
It is intuitive that if xn approaches a number then so does xnk. This
leads to
I
Theorem 10.14 If a complex sequence x has a limit 1 then any
subsequence k I-+ xnk also has the limit 1.
Proof.
For
k > N then
E:
nk
> 0 there exists N such that
2 k > N and therefore
1xn - 11
< E for
n
l
> N . If
Ixn, - ZI < E .
This proves that lim
Xn,
n-ca
= 1.
0
One can prove similarly that if a sequence diverges to 00 (or to -00)
the so does every subsequence.
Theorem 10.14 can often be used conveniently for proving that a given
sequence is not convergent by finding two subsequences with distinct limits.
For instance the sequence n H 1 ( - l ) nhas two subsequences k
1
(-1)2k -+ 2 and k H 1 (-1)2k+1-+ 0 and is therefore divergent.
+
Example 10.15
Since for n
23
+
+
Introduction to Mathematics with Maple
268
the sequence is decreasing after the third term. It is also obviously bounded
below, hence convergent. Let the limit be a. The subsequence k I-+
has by Theorem 10.14 the same limit. By Remark 10.1, Exercise 10.2.7 and
'a
6 = ( 'm)-+
2
Example 10.12 we have
so we have a = a2. Moreover a - 2 1since
a2 and also
=
fifi -+a,
fi 2 1. Consequently a = 1.
Exercises
@ Exercise 10.6.1
Use the method of Example 10.15 to show that
(1) an -+O for 0 < a < 1;
(2) VZ --+1 for o < b;
0 Exercise 10.6.2
Find lim
n+oo
(fi- 1)".
[Hint: Use Example 10.15 and
the Squeeze principle.]
0 Exercise 10.6.3
we have (1 +
a>
10.7
Prove that lim n (fi
- 1) = 00. [Hint: For K E N
n-oo
n
+ eK
< n for large n. It follows then K < n ( fi - l).]
Existence theorems
Monotonic sequences3 have the nice property that they always have limits.
With a sequence n I-+ xn E R bounded below we can associate an increasing
sequence n I+ a n as follows:
Similarly, if the sequence is bounded above, there is a decreasing sequence
(10.38)
an
L xn I Pn
3Monotonic sequences are automatically assumed to be real.
(10.39)
Limits of Sequences
for every n E N. We define: lim
as lim inf xn and lim
an
n-mc
269
n-oo
n-mo
Pn as lim sup xn.
n-oo
These are called respectively the limit inferior and limit superior of the
sequence n H 2,. Clearly
lim inf zn 5 lim SUP xn.
n-oo
We say that limsupx, =
00
n+oo
if
Xn E
Iw and
the sequence is not bounded
n-oo
above. We say limsupz, =
-00
if lim xn =
-00.
x3n-1 = 0,
23n
n+cc
n-mo
We employ similar
conventions for the limit inferior.
Example 10.16
have that
for every
Let x3,-2
n and consequently a
= 1,
= -1,
liminf xn = -1
n+oo
Example 10.17
If Zn
liminf
n-oo
2,
n E N. We
p = 1, hence
and
limsupx, = 1
n-oo
T
liminf xn = 1. Similarly if xn
n-oo
= -1 for
1 then clearly a n =
Xn,
pn
=
I , limsupz,
=
n+oo
I 1 then a n = I , pn = xn and again limsupx,
=
n-oo
= 1.
. The following Table 10.2
4121 1
displays z n , a n and pn for the first few n. It is left as an exercise t o show
that limsupx, = 1, liminfz, = -1.
Example 10.18
n-oo
Let xn = (-l)n+l
n-oo
+
Introduction to Mathematics with Maple
270
Theorem 10.15
A real bounded sequence n
xn
I-+
has a limit if
and only if
lim sup xn = lim inf xn
n-00
n-m
and then
lim sup x n = lim inf Xn = lim
n+co
12-00
Proof.
Xn
n-oo
.
Again we separate the proof into two parts.
I. If lim
xn
n-oo
= 1 then for every
E:
> 0 there exists N such that
for n > N . Consequently
This proves that
11. If limsup xn
=
n+ca
Principle since
liminf zn = 1 then lim
n-oo
an
n-oo
5 xn 5 pn.
xn
= 1 by the Squeeze
0
Remark 10.6 Theorem 10.15 can be extended t o any real sequence. In
other words, the Theorem remains valid if the word bounded is omitted. For
instance, liminf x n = 00 if and only if lim x n = 00.
n+m
A
71-00
sequence is said to be a Cuuchy sequence or simply Cuuchy if for
every E
> 0 there exists N such that
lzn
-x,1
<E
(10.40)
for all n > N and m > N . The limit of a sequence can be described
informally as a number to which members of the sequence are ultimately
approximately equal within a prescribed accuracy. A Cauchy sequence has
the property that ultimately the members of the sequence are approximately equal to each other within any prescribed accuracy. It is fairly
Limits of Sequences
271
obvious that a convergent sequence is Cauchy. Indeed, if x,
every E there exists N such that
k1.
- 11
&
<2
for
k
--+
I then for
> N.
Now we have
lx, - zmI 5 Ix, - 11 + lxm - 11 < - + - = E ,
for n > N and m
yielding
~~
> N.
&
&
2
2
The converse is also true, and is proved below,
~~
Theorem 10.16 (Bolzano-Cauchy) A sequence with complex
terms is convergent if and only if it is Cauchy.
l
Proof. We need to prove the if part. Let n H x, be a Cauchy sequence.
We first assume that xn are real. There exists a natural N such that
for n and m greater than N . Choose a particular m
rn = N
1) then
+
>n
(for instance
for n > N . This means that all terms of the sequence with TI > N are
bounded by the (fixed) number Ixml 1. Since the finite set of those xn
with n 5 N is also bounded, say Ixnl < K for n 5 N , we have
+
for all n and hence the sequence is bounded. This allows us to make use of
Theorem 10.15. Assume for an indirect proof that
= lim inf x,
x n --roo
Set
E
=
< lim sup x,
-
= 1.
X,--rOO
( d - i ) / 2 . Since xn is Cauchy there exists N1 such that
for n > N1 and m > N1. In (10.41) we keep m fixed and run n through
k , k 1,.. .. We obtain
+
Introduction to Mathematics with Maple
272
By the definition of limit superior and by Corollary 10.8.1 on preservation
of inequalities it follows
i- E 5 xm.
1- E is a lower bound
for xm for m > N1 and therefore
<inf{~rn,~rn+l,~m+2).
Taking the limit as m -+00 gives i-
E
5 I. Since d-
E
+
= (1 i)/2 it follows
This contradiction completes the proof in case of a real sequence.
For a Cauchy sequence with complex terms xn we define U n = !RXn and
21, = 3 x n . Clearly
Consequently, the sequences n --+ un and n 4 vn are Cauchy and by what
we have already proved for real sequences they converge. By Theorem 10.4
0
it follows that the sequence x converges.
The Bolzano-Cauchy Theorem is often referred to as the general convergence principle or the Cauchy convergence principle.
Xn
+
Xn+1
. If we knew
2
that the sequence were convergent with limit 1 we could find 1 easily. Indeed
Example 10.19
Let x1 = 1,
+1
~ n + 2
= Xn+l+
5
2
z is Cauchy. Firstly we have
and therefore I
+ -121
TXn+1
= -, 1 =
22
= 2, xn+2 =
1
2
-Xn
= *.. = 22
1
+ -x1
2
5
=2'
5
3'
- We prove the convergence by showing that
-
(x2 - q )= (-l)n.
2n
2n
(10.42)
Obviously zn+2 lies between xn+1 and 2, and ~ n + 3 remains there by lying
between ~ n + 2and xn+1. It is now clear by induction that for m 2 n 2 the
number zm lies between xn+l and xn. By Equation (10.42) we have
+
lxn - x m l
IlXn+1
-znI =
1
3,
Limits of Sequences
273
> n. For every E > 0 there exists N such that 2-N+1
2-n+1 + 0. Consequently for n > N and m > N we have
<
for m
E
since
The next theorem is very plausible and is often used. It says that shrinking
closed intervals have a point in common.
Theorem 10.17 (Nested Intervals Lemma) Let [an, bn] be a
sequence of closed bounded intervals such that bn - an -+ 0 and
[an+l, bn+l] C [an, bn]. Then there exists a unique c such that
c E [an, bn] for all E N.
Remark 10.9
The conclusion of the Theorem can be restated as
00
n [an, bn] = { c }
n=l
Proof. The sequence n -+ an is increasing and bounded from above by
bl and therefore has a limit, say c and then an 5 c for all natural n.
The sequence n + bn is decreasing and has the same limit, since bn =
an (bn - a n ) and bn - an -+ 0. Since bn+l 5 bn, it follow^ that c 5 bn for
all natural n. We have now proved that an 5 c 5 bn for all n E N.
If also y E [an, bn] for n E N then Iy - CI 5 bn - an for all natural n.
Sending n -+ 00 gives Iy - cI 5 0 , that is y = c.
0
+
The proof of the next theorem contains a typical application of the
Nested Intervals Lemma.
Theorem 10.18 (Bolzano-Weierstrass)
quence contains a convergent subsequence.
Proof.
We assume first that the sequence z is real. Let A 2 xn
all n E N. Set a1
and
Every bounded se-
~.
= A , bl = B
[F,
B]
< B for
and n1 = 1. One of the intervals
L
contains infinitely many terms of the sequence, we denote
this interval by [a2, b2] and choose n2 > n1 such that xn2 E [a2,bz]. Continuing with this process we obtain a sequence of nested intervals [ a k , b k ]
Introduction to Mathematics with Maple
274
such that bk - a k = ( B - A)/2-"'
and X n k E [ a k , b k ] . By the Nested
Intervals Lemma there exists c E [ak, bk] for all k E N and then
C
(bk - a k )
5 ak 5 xn, 5 bk 5 c
The Squeeze principle implies lim
Xn,
k-mo
+
(bk - a k ) .
= c. This completes the proof for
real x.
For a bounded complex x with un = 9222, and V n = 3 Z n we have that the
sequence n H un is also bounded. By what we have already proved there
is a convergent subsequence, U n k + &. Similarly, the bounded sequence
k H V n , has a convergent subsequence vnkj + ij. By Theorem 10.4 it
follows that
--3
0
Remark 10.8 Although we did not mention it explicitly we used the Recursion Theorem 5.2 in defining the sequence of intervals [an, bn].
Exercises
+
Exercise 10.7.1 Let x1 = 1, xn+l = -1 x i / 2 . Prove that n H xn is
Cauchy and zn 3 1 [Hint: Calculate 2 2 , x3. Use induction to prove
that 2 3 5 xn 5 2 2 . Then show that Ixn+1 - x n I 5 glxn - x n - i I , for n 2 3.
Deduce lXn+1-xn) 5 7n-181-n. Thenuse Izn-Xn+lI+".+/~m-xm-11
5
7n-182-n to prove that it is Cauchy. The limit I must satisfy I = -1+Z2/2.]
a.
Exercise 10.7.2
Prove that for real sequences n
X n 5 Yn for all n E N
H
xn, n
H
Y n with
(10.43)
(10.44)
Give an example such that
lim inf Y n
n+oo
[Hint: If limsupy, =
00
{yn, Y n + l , . . .} and use pn 5
( - I ) ~xn, = yn/2.]
set Y n = 1
+
n+m
there is nothing to prove. Otherwise set
n+m
SUP
< lim sup xn.
pn
=
pn. Similarly for liminf. For the example
Limits of Sequences
@
Exercise 10.7.3
that
Let n H
xn
275
be convergent and n I+
Yn
bounded. Prove
State and prove a similar theorem for limit inferior. [Hint: Let xn -+ 1.
For n > N obtain I - E Yn 5 xn Yn 5 1 E Yn then pass to the limit
+
+
+ +
superior .]
@ Exercise 10.7.4
liminf
n-w
xn
Prove that for bounded sequences
+ lim inf
n+oo
Yn
lim inf
lim inf Xn
n-ca
lim sup ( x n
n-mo
n-00
+
Yn)
+ lim sup
n+oo
Yn
5 lim sup xn + lim sup Yn. (10.45)
n+oo
n-00
Give examples showing that all inequalities may be strict. [Hint: For lim sup
obtain sup { x k y k ; Ic 2 n } 5 sup { x k ; Ic 2 n} sup { y k ; k 2 n } then pass
+
+
to the limit as n --+ 00. For an example set
Xn
= -Yn =
(- I)n-I
Let x1 = 1, 52 = 2 and xn+1 = -/.,
Prove that
the sequence x is Cauchy and find its limit. [Hint: 1 5 xn 5 2, IXn+l xnl = f i ( d ~f i ) - ' I x n
- Xn-11 I
Ixn - x n - l I / f i ) x n + 1 f i
=
X n J G = ' * . =
2.1
Q) Exercise 10.7.5
+
@ Exercise 10.7.6
1
n]O,-[.
00
cE
n=l
n
13
Intervals 0,-
,n
E
N are nested but there
Similarly, intervals [n,oo[are also nested but
n
is no
00
[n,00[=
0.
n=l
Why this does not contradict Theorem 10.17.
@ Exercise 10.7.7
Prove: If n I-+ xn is a bounded sequence and m is
not the limit of this sequence then there exists a number 1 # r n and a
subsequence converging to 1. [Hint: There is a positive number a and a
subsequence n I-+ xnj such that lxnj - ml 2 a. Set y j = x n j , by the
Bolzano-Weierstrass Theorem there is a subsequence y j k --+ 1 and 11 - ml 2
4
10.8
Comments and supplements
A number X is said to be an accumulation point of a sequence if there
exists a subsequence converging to A. If there is a subsequence with an
Introduction to Mathematics with Maple
276
infinite limit, for instance 00, we say also that 00 is an accumulation point
of the sequence. For a bounded sequence it is clear that the set of all
accumulation point is also bounded. Moreover, using the notation (10.37),
(10.38) and(10.39) we have a n k 5 x n k 5 o n k . It follows from Theorem 10.8
that for any accumulation point X
lim inf xn 5 X 5 lim sup xn.
12-00
(10.46)
72-00
We now show that lim sup x n is the largest accumulation point by defining
n-mo
a subsequence converging to it. For every n E
an -
N there is a j n such that
1
- < ~ j , ,5 an.
n
By the definition of limit superior and Theorem 10.8 clearly
xjn
lim SUP x n .
n-00
Similarly the limit inferior is the smallest accumulation point. By the
Bolzano-Weierstrass Theorem every bounded sequence has an accumulation point and if it has only one then it is convergent by Theorem 10.15.
If a sequence, not necessarily bounded, has only one accumulation point
then it has a limit.4 A sequence can have many accumulation points; the
sequence with the following terms
1,
1 1 2 1 2 3 1 2 3 4
5’3’ 3’ 4’4’4’5’ 5’p ”
$7
has any number between 0 and 1 as an accumulation point. The introduction of infinite limits superior and inferior enables simple formulation of
some theorems, for instance, a sequence has a limit if and only i f its limit
inferior and limit superior are equal.
Exercise 10.8.4 asks for the proof of the following theorem: If 27.1 > 0
for all n E N then
lim inf
n-co
%
5 lim inf
xn
n-oo
%+I
65 lim
sup 6 5 lim sup -.
n400
12-00
(10.47)
Xn
In inequalities like these we interpret the symbol oo as larger than any
real number and --oo as smaller than any real number. If, for instance
~ 2 n - 1= 1 and ~ 2 =
, n then, with this convention, we may write
0 = liminf
n-co
Xn+1
zn+l<
liminf 6= 1 = limsup 6< limsup -= oo.
Xn
n+oo
4 P ~ ~ ~ ioc,
b lory
n-mo
-00.
n+oo
Xn
Limits of Sequences
277
Exercises
@ Exercise 10.8.1
Define a sequence for which all real numbers are accumulation points. [Hint: 1/2, -1/2,1/3, -1/3,2/3, -2/3,4/3, -4/3,5/3, -5/3,
1/4,-1/4,3/4, . . . ,11/4, -11/4,. . . ]
0 Exercise 10.8.2
Prove: If yn
-+Y
2 0 and n
-+
xn is bounded then
lim SUP Ynxn 5 Y lim SUP xn.
n-oo
n+oo
State a similar theorem for liminf. [Hint: xnyn 5 YnPn. Consider separately Y = 0.1
0 Exercise 10.8.3
Prove:
lim inf x n 5 lim inf
n+co
21
n+co
+ + +
~2
Xn
n
5 limsup
21
+ + +
~2
n
n+oo
+ c:’”
+
. * *
(x:
+
Xn
5 lim sup xn.
n-oo
[Hint:(l/(n p ) )
xk I ( l / ( n p ) )
xk pPn), let P -+ 00 using
Exercises 10.7.3 and 10.7.4 as well as Theorem 10.15. Then let n -+ m.]
0Exercise 10.8.4
Prove Inequalities (10.47).
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Chapter 11
Series
In this chapter we introduce infinite series and prove some basic
convergence theorems. We also introduce power series-a very
powerful tool in analysis.
11.1
Definition of convergence
Study of the behaviour of the terms of a sequence when they are successively
added leads to infinite series. For an arbitrary sequence n H an E C we
can form another sequence by successive additions as follows:
sn = a1
+ a2 + - - + an = z
*
n
a i
(11.1)
i=l
To indicate that we consider n I-+
sn
rather than n H an we write
c
ai.
(11.2)
The symbol (11.2) is just an abbreviation for the sequence n
Sn, with
sn as in (11.1). We shall call (11.2) a “series” or an “infinite series”; an is
the nth term of (11.2); Sn is the nth partial sum of (11.2).
It is usually clear from the context what the partial sums for a series
1
like (11.2) are. On the other hand, if ai = - for some positive integer k
ka
279
Introduction to Mathematics with Maple
280
and every natural number i then
sn =
However the symbol
1
1
k-a is ambiguous. On occasions like this we would
k-2 rather than
write
1
+ - + ... +-k”
k k2
k - i . The letter i in (11.2) can be replaced
a
by another letter without altering the meaning of the symbol; for example
we can write
aj or
arc instead of
ai. For instance, the geometric
series, that is the sequence n I+ sn where S n = 1 q q2 - - - qn-’, is
denoted by
qa-’.
+ + + +
De finition 11.1 (Convergence)
convergent to a sum S if
The series (11.2) is said to
2
n
lim
n+ca
Cai= S.
(11.3)
i=l
The notation
00
Cai=S
(11.4)
i=l
is used as an abbreviation for series (11.2) to be convergent to a sum
S
Obviously, by Theorem 10.1 a series can have at most one sum, so
we may speak of ‘the sum’ rather than ‘a sum’. The phrases ‘the series is
convergent to the sum S’, ‘the series has the sum S’ and ‘the series converges
to S’, or variants of these, are used interchangeably. Equation (11.3) has,
of course, the same meaning as1
lim sn = S.
n-cc
The notation and terminology for infinite series comes from the times
when the theory was rather vague, but the notation has persisted ever since.
The usage of the term sum2 is a bit unfortunate because the sum of a series
is not the usual sum, but the limit of the sequence of partial sums. Instead
lWith sn given by (11.1).
21t comes from times when the sum of a series was not well defined but regarded
vaguely, intuitively and imprecisely as a sum of infinitely many terms of the series.
281
Series
of (11.2) or (11.4) the following suggestive notation is often used:
+ a2 + + - - .
(11.5)
+ a2 + a3 + ... = S.
(11.6)
a1
a3
or
a1
The notations of (11.5) and (11.6) have the advantage of being rather indicative of the terms of the series: for instance one gets a better impression
of the series
(11.7)
by writing
1
-+1+
3
-1+ - +1 - + -1+ - +1 - + -1 + . .1.
32
22
33
32
34
42
1
35
rather than (11.7). When using (11.6) one has to realize that the same
symbol, namely
has two different meanings-it
n
H
c
ak and also lim
k=l
denotes the sequence of partial sums
n
n
n+ca
c
ak. Perhaps we should add that most authors
k=l
ca
use the symbol
ak instead of our symbol
c
Uk,
which, of course, has
k=l
the same disadvantage as (11.5), namely denoting the series and its sum by
the same symbol.
Since a series is nothing but the sequence of its partial sums, many
definitions for sequences carry over to series. For example, a series is called
divergent, divergent t o 00 (or -00) according to whether the sequence of
its partial sums diverges, diverges to 00 (or -00) respectively. Similarlyas with sequences-the change of finitely many terms of a series does not
affect its convergence or divergence, but changes the sum in the obvious
way.
In particular, the series
282
Introduction to Mathematics with Maple
converges if and only if, for N E
N
does, and then
or better still
(11.8)
00
By analogy with the finite sum we shall often write
ui instead of
i=N+1
00
i=l
Example 11.1
It is an immediate consequence of Definition 11.1and the
result of Example 10.14 that
1
1+1+ 2!
Example 11.2
1
++ -1 + * * . =e.
3! 4!
The geometric series Cq2-l converges t o
Indeed, sn = 1 + q + - . . + q n - '
=
for q
#
1
1-if lql < 1.
1 and lim sn = r 1 i since
n+oo
00
lim qn = 0 for lql
n-ws
< 1 by Example 10.13. Consequently C qi-'
i= 1
Example 11.3
=
&
Let us consider the series
1
1.3
1
+ 3 1. 5 + +..-.
5.7
- -
We need some useful expression for the partial sum
Sn
=
1
1.3
+ 3 1. 5
- -
+
s
e
e
+
1
(2n - 1)(2n
+ I)
'
By using the identity
1
(2k-l)(2k$l)
we obtain
s,=-1
( l - - +1- - - +
1 . . .1+ - - 2
3 3 5
1
2n-1
1
1
)=-2n+1
2
1
2(2n+1)'
Series
Since sn
4
283
4 we have
m
1
(2k
- 1)(2k
k=l
c
+ 1)
1
--
2
Example 11.4
z
> 0.
Let ao.ala2a3... be the decimal fraction for z E
We know from Example 5.8 that
a.
IR,
a1 + . .. + an < z < a0 + a1 + ...+ an + 1
+10
10n -
10
10n
10n
It follows from the Squeeze Principle and Example 10.13 that
n
ai
5
= z,
i=O
that is
(11.9)
Consequently, if a0 . ula2as.s. is the decimal fraction for z, then z is the sum
of the series (11.9).
For z > 0 it is customary, in contrast t o what we have done in Example 5.8
to call a0 . alaza3.. . the decimal fraction for z if (11.9) holds, where a0 E No,
and ai E {0,1,2.. ., 9 } for i E N. Every real z can then be represented
by a decimal fraction, however this representation is no longer unique. For
example, if z > 0 and z = a0 . a1a2a3.. .aN999. ' . with aN < 9, then also
z = a0 .a1u2a3.. .GNOOO..
where G N = aN
1. This is obvious because
+
+
-aN
+
10N
c -=alv.
k=N+1 10k
10N
Similar comments apply with appropriate changes made to binary and ternary
fractions and obvious modifications should be made for negative reals, for
4 .
instance the decimal expansion for -- IS -1.33333.. . .
3
Example 11.5
and sn + 00.
1
+ -+ +. .
Jz&
The series 1
1
is divergent t o
00.
We have
Introduction to Mathematics with Maple
284
We shall see in the next section that sums of series share many properties, but not all, with ordinary sums (that is sums over a finite number
of terms). For example, it is not permissible to remove (infinitely many)
parentheses in a series. To see this, consider the series
(I - 1)+ (1- 1)
+ . - ..
All the terms of this series are zero, hence the series converges to zero. By
removing parentheses we obtain the series
1- 1 +1 - 1 + - . .
with partial sums s1 = 1, s2 = 0 , s3 = 1, .. ., s2k = 0 , s2k+l = 1,
which is clearly divergent. On the other hand, inserting parentheses into
a convergent series is permissible, because this amounts to a selection of a
subsequence of partial sums, and we know that every subsequence converges
to the same limit as the original sequence.
Exercises.
Exercise 11.1.1
Decide which of the following series are convergent
and which are divergent, and find the sums of the convergent series.
(1)
1
*
1'2 2.3 3.4
1
1
1
*
(3) -+-+-+...
1 . 4 4 . 7 7.10
1
1
1
+-+....
(4)
1-2.3 2.3.4 3'4.5
1
2 +-+
4...'
(5)
1000 1001 1003
(2)
1
1
-+-+-+...
++-
(6)
(7) x
+ C ( +' $G
@ Exercise 11.1.2
-
~)
with x
> 0.
Prove that if an = zn - xn+1 and lim xn = I then
the series C an is convergent and
n+cc
00
C an = XQ - 1.
n=O
285
Series
@ Exercise 11.1.3
The series
1 - (1- 1) - (1- 1) - - . *
converges to I. By inserting parentheses differently we obtain the series
(1- 1)
+ (1 - 1)+
* . -
which converges to zero. Explain this seeming paradox.
@ Exercise 11.1.4
then
ak
k=l
If
Cak
and
C bk
5
ak
5
bk
for k E N
bk.
Prove this result, and show that if in addition that
k=l
00
a,
converge, and
00
00
< b, for some m E IV then
00
ak
<
k=l
bk.
k=l
k+l
Exercise 11.1.5
Find the approximate sum of the series
- to
2%
three decimal places. [Hint: use the previous exercise and (11.8); since
k+l
2
k+l
O0 k + l
is approximately equal to
< - the sum
- on
2kk
2k'
2
k
2kk
k=l
k=l
c
three decimal places if
c &<
c(i+
2
k=N+1
00
0 Exercise 11.1.6
Prove that
@ Exercise 11.1.7
Prove that
1
1
l ) x i = -for 1x1 < 1. [Hint:
i=n
(1- x)2
use Exercises 10.4.7, 5.3.6 and Example 10.11. ( This exercise can be done
easily with the help of Theorem 13.25 of Chapter 13.)]
(1) Partial sums of a convergent series form a bounded sequence;
( 2 ) If C U k converges then for every positive E there exists an M E
11.2
N
Basic theorems
The following theorem is an immediate consequence of Definition 11.1 and
theorems on limits of sequences, especially Theorem 10.6.
Introduction to Mathematics with Maple
286
I( Theorem 11.1
I/
(i) If
C a k converges, and c E C, then so does C c a k , and
k=l
(ii) If both series C a k and
verges, and
l
Remark 11.1
Example 11.6
k=l
C bk
converge, then
00
00
00
k= 1
k=1
k=l
C ( a k
+ b k ) con-
Using (i) with c = -1 and then applying (ii) gives
Consider the series
+ x3 + x4 + x6 - x7 + x9 + X 1 O + 212 - - - .
For 1x1 < 1 both of the series 1 + x3 + x6 + ... and -x + x4 - x7 + - . 1-x
*
converge and by Remark 11.1we have
1-
+ 2 3 + 2 4 + x 6 - 27.. . = 1+ 2 3 + x 6 + . . . - + 2 4 - 27 + .
- 1
---1-23
Theorem 11.2
If x
a k
1+x3'
converges then lim
k+oo
ak
= 0.
Proof. Let S n = C i = l a k and ~ ~ . . , a k = S . Since lim
72-00
lim s n - 1 = S we have lim an = lim (sn - s n - l ) = 0.
n--tm
n+oo
* .
X
n-mo
Sn
=
0
00
Example 11.7
The series x ( k ~converges
) ~
if and only if x = 0. Indeed,
k=l
if
J:
# 0, then there exists a K
1
such that K
22
1x1 -
Then for
k3K
we have
Series
287
# 0, and the series
( k z [2 1, therefore I ( k ~ ) 2~ l1. Consequently, lim
k-mo
must diverge by Theorem 11.2.
Remark 11.2
series
The converse of Theorem 11.2 is false. For example, the
1
- from Example 11.5 diverges although lim
A
k-mo
1
- = 0.
A
Theorem 11.3
A series with non-negative terms converges if and
only if the sequence of partial sums is bounded.
Proof. The assumption that the terms are non-negative automatically
implies that they are real. The sequence of partial sums is increasing because the terms are non-negative. Hence it converges if and only if it is
bounded, by Theorems 10.9 and 10.5.
0
Example 11.8
The series
1
1
=I+-+-+...+2"
3"
1
- I + - + - + .1
..
3" 5"
1
1
< 1 + 2" -4"+ - . .
-+
converges if a
> 1, a E Q.
We have
1
(24"
1
1
1
-(1+ ( 2 n - 1)"
2"
2"
1
1
+
+
+ -+ 2"
1
1
++ . . -+ 7
)
3"
-Sn
(271)"
I 1 + -2"S 2 n .
L
Consequently, for a
> 1 we have
2"-1
5
2"-1 -
1'
( 11.10)
Since ~ 2 n - l 5 ~ 2 it
n follows from (11.10) that the sequence of partial sums is
bounded and the series converges by Theorem 11.3.
Example 11.9
The series
cE,
1
which is called the harmonic series,
diverges. We will prove this indirectly, by assuming the contrary. Let
0°1
k=l
-=
k
Introduction to Mathematics with Maple
288
1
are bounded above by S, hence this series
2k-1
S. The partial sums of
0
0
also converges; let
k=l
4
1
-= T . Then, and by Theorem 11.1,
2k - 1
1
s=1 + -1 + -1 + ... = 1 + -1 + -1 + ...+ ?1 ( l +-1 + -1 + ...) = T + -s.
2
3
3
5
2
3
2
Consequently, S = 27'. On the other hand,
S = l + -1+ -1+ . . . = I + - 1
+ - +1 . . . + - +1 - +1- . .1.
3 5
2 4 6
2 3
1 1
1
< 1 + - + - +..-+ 1 + -1 5
3 5
3
+ + ... = 2T
(11.11)
which is a contradiction.
Theorem 11.4 (Comparison test) If 0 5 a k 5
the convergence of C bk implies the convergence of
~
bk
for k E N,then
Cak.
_ _ _ _ _ _ ~
/I
Proof. If C bk converges, then its partial sums are bounded. This implies
that the partial sums of C a k are also bounded, which in turn implies that
C a k converges.
0
Remark 11.3
divergence of
The conclusion of Theorem 11.4 can be rephrased as the
of
bk.
ak implies the divergence
Example 11.10
The series
1
72" diverges for a 5 1, a E Q. For a = 1
this was established in Example 11.9.
For a
divergence follows from the comparison test.
<
1
1 we have nQ
1
> n
and
Exercises
Use the comparison test to prove the convergence or
divergence of the following series.
Exercise 11.2.1
1
Series
@ Exercise 11.2.2
289
Give examples of series C U k such that
(1) ak + 0 and the series diverges;
( 2 ) the partial sums are bounded and the series diverges;
( 3 ) the partial sums are bounded, ak 4 0 and the series diverges.
[email protected]
Exercise 11.2.3
> 0, b, > 0
If a,
for n E N and lim
n-00
5
=L > 0
b,
prove that the series a k and C bk either both converge or both diverge.
What conclusion can be drawn if L = O? [Hint: There exists N E N
such that ILlb,/2 < a, < 31LIbn/2. Use the comparison test. If L = 0
then convergence of C b, implies that of C a, but C b, might diverge and
C a, converge.]
11.3
Maple and infinite series
For a few series it is easy to find the sum. Generally speaking, however, it
is difficult to find a sum of an infinite series exactly and on many occasions
only a numerical approximation of the sum can be found.
Maple can find sums of many series almost instantly. For instance the
sum of
00
c
1
1
+ l)(n + 2 ) . . . (n+ 10)
(n
can be found by the same method as in Example 11.3 but would require far
more work. Finding the sum of an infinite series in Maple is very similar
to finding a sum of finitely many terms. For example
>
sum(l/product((n+i),i=l. .lO),n=l..20);
>
715357
23363003664000
sum(i/product ((n+i) ,i=i . .IO> ,n=i. .infinity) ;
1
32659200
Maple can find sums of infinite series for which we have not yet developed enough theory. For instance
>
sum( I/n^2,n=I. . infinity) ;
Introduction to Mathematics with Maple
290
1
6
- 7r2
In other situations we can even obtain a result hard to understand.
For example
Mup2e returned the result in terms of the hypergeometric function but
we need not to go into the study of this function: we can obtain a numerical
approximation easily with the evalf command.
1.279585302
Exercises
00
Exercise 11.3.1
Find the sum of the series
from the first
i=l
principles and by Maple.
Exercise 11.3.2
Find
O01
to fifteen decimal places.
[Hint:
i=l
Digits=l5;evalf(sum(l/nA3,n=l..infinity)).]
11.4
Absolute and conditional convergence
Theorem 10.16, the so-called Bolzano-Cauchy convergence principle, leads
to
291
Series
Theorem 11.5
The series
a k converges i f and only if for every
positive E , there exists an N such that
(11.12)
I
Ik=n+l
for every natural number p and n > N .
Remark 11.4 Theorem 11.5 is also referred to as the Bolzano-Cauchy
Theorem, or as the general principle of convergence.
Proof.
Condition (11.12) is equivalent to the partial sums forming a
Cauchy sequence. Hence, the sequence of partial sums converges if and
only if (11.12) is satisfied.
0
I/
Corollary 11.5.1
I f a k converges, then for every positive number E there exists an M such that for n > M
l
This result was proved directly from the definition of convergence in
Exercise 11.1.7 part (ii). Here we provide another proof.
Proof.
There exists an M such that
for n E N, p E
N,n > M . By taking limits as p
Example 11.11
1
C k . Clearly
00
we obtain
W e prove again t h e divergence of the harmonic series
1
k=n+l
-+
1
Introduction to Mathematics with Maple
292
Consequently, the Bolzano-Cauchy condition is not satisfied for
p = n and the series must diverge.
Theorem 11.6 (Leibniz test) If a 1 2 a 2 2 a3 2
series C ( - - - ~ > ~ - l u k converges if and only i f a k -+ 0.
A series x(-l)k-'ak with
Uk
-
E
=
$
and
2 0 then the
2 0 for every k E N is called an alternating
series.
Proof.
11. Let
I. The 'only if' part is an immediate consequence of Theorem 1 1 . 3 .
s, = C L = l ( - l ) k - l a k .
The sequence n I-+ s2, is increasing and
the sequence n I--+ s2,-1 is decreasing. Indeed we have a 2 n + l - c 1 2 ~ + 2 2 0
and consequently s2,+2 = ~ 2 , (a2,+1 - a2,+2) 2 s2,. Similarly, -a2,
a2,+1 L 0 and san+l = ~ 2 ~ --1a2n
mn+l 5 s2,-1.
Moreover, ~ 2 ~ 4 -=1
s2,
U 2 n t l L S 2 n . Thus
+
+
+
+
+ I ] and s2,+1 - SZ, =
The bounded closed intervals [ s ~ ~ , s ~are~ nested
a2,+1 -+ 0. By Theorem 6.7.3 there exists an S E [Q,, sa,+l] for every
n E N. Since an -+ 0 for every positive E , there exists an N such that
0 5 a 2 ~ + 1< E . I f rn > 2N 1 then
+
Remark 11.5
The sum of an alternating series with decreasing terms is
trapped between ~ 2 ,and s2,-1 for every n E N. Consequently, the error made
in replacing S by a partial sum, that is S - s,, does not exceed in absolute
value the first omitted term, and has the same sign.
Example 11.12
Since n-"
-1 0 for a > 0, the series
I - - +1- - - +1 . . . 1
2" 3" 4"
converges for a
> 0, a E Q.
Using Remark 11.5 with a = 1 yields
293
Series
or, with a, = 2 and
c
100
n=l
n = 100
1
1 Theorem 11.7
c
< "
(-l)n+ln2 -
(-l)n+l-
1
n2
n=l
c
100
5
1
(-ly+l-
n=l
I f C lak I converges, then so does
n2
1
+-1012
l
C ak .
Proof. By the Bolzano-Cauchy Theorem, for every positive E there exists
an N such that
k=n+l
for n E
N,p
E
N and n > N .
Since
I
Ik=n+l
the series
k=n+l
0
ak converges, by the Bolzano-Cauchy Theorem.
For series with real terms there is another proof of Theorem 11.7 which
employs an idea often used on other occasions.
Proof.
Let
a+
-
IC-
Iakl +ak
2
-
ak
=
lakl-ak
2
so that a: = ak if ak 2 0 and a; = -ak if a k 5 0. Then 0 5 a: 5 lakl,
0 _< a; _< l a k l , and then the convergence of C l a k l implies the convergence
of C a: and C a;. This in turn implies convergence of C ak, by Remark
11.1, since ak = a;Z - a;.
0
D 11.2 (Absolute and Conditional Convergence) If
the series
lakl converges, then C ak is said to be absolutely convergent. A convergent series which does not converge absolutely is said to
be conditionally convergent.
Remark 11.6
The alternative proof of Theorem 11.7 shows t h a t for a
conditionally convergent series
ak with real terms, both of the series a:
and x u ; are divergent.
C
C
Introduction to Mathematics with Maple
294
Example 11.13
(-1)LtJ
The series
Let
Q!
(-
is the series
1
k2
E
Q. The series
> 0 and the series
Example 11.12 for a
Ic2
- converges by Example 11.8.
converges absolutely because
Example 11.14
1
- that
1
( - l ) k + lconverges
~
by
1diverges for a 5 1, by Example
ka
l)k+l
-is only conditionally convergent for 0 < a 5
11.13. Consequently
1.
Ica
Absolutely convergent series have some properties in common with finite
sums which are not shared by conditionally convergent series. One such
property is considered in the next section.
Exercises.
Exercise 11.4.1
Test for the absolute or the conditional convergence
of the following series
1
2
600 - 1100
+ - -3- + . . . -4
1600 2100
2
3
4
5
(2) -- -+---+...*
2 a - 1
3&-1
4&-1
5&-1
@ Exercise 11.4.2
I f u, are terms of a bounded sequence and
ukbk converges absolutely.
verges absolutely, prove that
Exercise 11.4.3
does too.
@ Exercise 11.4.4
If
converges absolutely prove that
con-
k+lak
Ic
Prove that if CUEand chi are convergent then
[Hint: lakbk) 5 1/2(u;
b;)]
C ukbk converges absolutely.
@ Exercise 11.4.5
C bk
+
Prove that the series
1 - -1+ - -1- + 1
---1
+ - -1- + -1- - +1- - .1. . 1
2 2 4 3 8 4 16 5 32
1
6
Series
295
has unbounded partial sums and therefore diverges. Does this contradict
the Leibnitz test? If not, why not?
Exercise 11.4.6
The series
1
l 5 - 1 E!+1
1
---
1
1
+- -++..
*-1
q3+1
that is, the series whose terms are given by
1
a2k-1 =
diverges and
-1
I7G-i-1
a k -+0.
a2k =
IW+l
Prove this, and explain why this example does not
n
1
1
n 2
- ?)
=
contradict the Leibniz test. [Hint:
k=2 f i - l
k = 2 A- 1.l
11.5
x(-
Rearrangements
nk is a bijection of N onto itself, then the series Can,is called a
C a k . The rearranged series contains all the terms of the
original series and no other terms, but contains them in different order.
If k
I-+
rearrangement of
Theorem 11.8 Every rearrangement of an absolutely convergent
series converges absolutely and has the same sum.
Proof. The idea of the proof is simple. If we go far enough, partial sums
of both series will have a lot of terms in common, namely the first N , and the
sum of all other terms in which they differ would be small by the BolzanoCauchy Theorem. The formal proof is as follows: We write E k , ak = S N
00
00
1
and c a k = S . Given E > 0, choose N so that
lakl < - E ; then
C
IS - SNI
< + E . Now there exists an mo such that
(721,.
and then
2
k=N+1
k=l
(121,
. . , n m }2 (1,. . . ,N }
for all m 2 mo
. . . , nm} = (1,. . . , N , u l , .. . ,UK} for some K 2 0 where ui
m
are distinct integers all greater than N . Writing om =
an,, we have
k=l
N
K
k=l
k=l
Introduction to Mathematics with Maple
296
so
and hence
for all m
2 mo, that
Example 11.15
is o m
+S
The series
as m + 00.
x-
(- 1)"1
,fi
0
converges conditionally by Ex-
ample 11.14. We now show that its rear;angement
(11.13)
diverges. We have
Consequently the partial sums of (11.13) are not bounded and the series must
diverge.
Exercises.
@ Exercise 11.5.1
(++I
1
. Show that the series
. k
O0
Let S =
k=l
1 - +1 - - 1- + -1+ - 1- - +1 - +1. . . 1
1 + -1 - - +
3 2 5 7 4 9 11 6 1 3
(11.14)
1 - -1 - -1 + -1 - -1 - -1 + -1 - -1 - - 1 + -1- . . .
2 4 3 6 8 5 10 1 2 7
(11.15)
and
Series
2s
is,
c (-
-)
297
converge and have sums
and
respectively. [Hint: For the series in
(11.14) show that the series S can be written in either of the forms
s=
O 0 1 - 1
2k-1
2k
k=l
k=l
1
4k-3
’
1
4k-2
+--4 k1- 1
a)
’
and from the first of these
1
2
k=l
Now add the last two series. For the series in (11.15) proceed in a similar
fashion. Do not forget to prove the convergence of all the series involved in
whatever procedure you use. ]
11.6
Convergence tests
Since the sum of a series cannot often be found it is important to have
criteria of convergence which will allow us to establish at least that the sum
exists. If we know that a series converges we can than perform operations
which will help in evaluation of the sum. In this section we prove two simple
theorems which are based on comparison of a given series with the geometric
series. In Section 11.8 we consider several finer tests of convergence.
Theorem 11.9 (Ratio or d’Alembert’s Test)
N and a q such that
If there exists an
%+I
1-+4<1
for n
2 N , then the series
(11.16)
a, is absolutely convergent; if however,
(11.17)
1
for n >_ N then the series diverges.
Condition (11.16) or (11.17) is satisfied if lim
n+m/
1
%
an
=
Z < 1 or if
Introduction to Mathematics with Maple
298
=I
> 1, respectively. Hence we have
Corollary 11.9.1 (d’Alembert’s Limit Test)
then
If
C an converges absolutely if 1 < 1and diverges if I > 1.
This corollary follows in a straightforward way from Theorem 11.9, so
here we prove the theorem.
Proof.
We may assume that N E N and obtain successively from (11.16)
By an easy induction, l a ~ + k I 5 ( a ~ l q ~
We
. now have that the series
C l a ~ + k l converges by Theorem 11.4-the comparison test. Hence C a k
converges absolutely. If (11.17) holds, then 0 < l a ~ 5
I laN+ll 5 l a ~ + a l
and it is not true that a, --+ 0. The series diverges by Theorem 11.2.
0
Example 11.16
For the series C n x n we have
By Corollary 11.9.1 this series converges for 1x1 < 1 and diverges for 1x1 > 1.
For 1x1 = 1the series obviously diverges. Perhaps it is worth mentioning that
for 1x1 = 1Theorem 11.9 is still applicable, whereas Corollary 11.9.1 is not.
z,
1
with cy E Q converges for a > 1 and
Example 11.17 The series
diverges for a 5 1(see Examples 11.8 and 11.10).
an+l
lim -= 1 regardless of a.
n-m
a,
This shows that no conclusion can be drawn regarding the convergence or
divergence of the series C a n from
Series
1
l
1
299
Theorem 11.10 (Root Test or Cauchy’s Test)
an N such that
If there exists
(11.18)
for n 2 N ,then C a n converges absolutely; if
m
2
1
for n 2 N , then
(11.19)
Can diverges.
= I , then the series
Corollary 11.10.1
If limn-,m
converges absolutely for I < 1 and diverges for I > 1.
C an
Remark 11.7
As with d’Alembert’s limit test, no conclusion can be drawn
regarding convergence or divergence if I = 1. The series from Example 11.17
illustrates this again.
We now give the proof of Theorem 11.10.
Proof.
Again we may assume that N E N. If (11.18) holds then Ian1 5 qn
00
for n 2 N . Since q
< 1,the series
lakl
converges by the comparison test
k=N
(Theorem 11.3). If (11.19) holds, then Ian1 2 1for n 2 N , and consequently
+ 0 is false and the series diverges by Theorem 11.2.
0
an
Example 11.18
The series
1
1
1
1
1
1+-+-+-+-+-+..3 2 32 22 33
is obviously convergent as a sum of two geometric series. Since
1
?k
i/an is
1
2-5
for n odd and 3-5 for n even, we can apply Theorem 11.10 to
assert convergence. On the other hand, as an+l/an > 1 for an even n and
an+l/an < 1 for an odd n, Theorem 11.9 is not applicable.
Inequalities (10.47) actually show that whenever Corollary 11.9.1 is applicable, so is Corollary 11.10.1. Moreover, in Corollary 11.9.1, the lim can be
replaced by limsup for the convergence and by liminf for divergence test.
Similar comments apply to Corollary 2.
Introduction t o Mathematics with Maple
300
Exercises
Exercise 11.6.1
11.7
Test the following series for convergence:
Power series
We encountered the series C n x n in Example 11.16. Series of the form
C Cnxn
(11.20)
are called power series. A power series represents a function
n=O
The domain of this function is the set of all x for which the series
converges-the so-called domain of convergence. For the power series from
Example 11.16 the domain of convergence was the unit disc in the complex plane. The next theorem says, roughly speaking, that the domain of
convergence of any power series is always a disc in the complex plane.
Power series are, after polynomials, the simplest functions of a complex
variable. In complex analysis many theorems on polynomials are extended
to power series. However this goes far beyond the framework of this book.
Series
Theorem 11.11 (Circle of convergence)
C anxn there are only three possibilities:
(i)
(ii)
(iii)
301
For a power series
it converges only for x = 0;
it converges absolutely for all x E @;
there exists a positive p such that the series converges absolutely for 1x1 < p and diverges for 1x1 > p.
\
Definition 11.3 (Radius of Convergence) The number p defined in Theorem 11.11is called the radius of convergence of the series
C c n x n . It is customary to say that p = 0 in case (i) and that p = 00
in case (iii) .
Proof.
Let
S = {x; there exists K such that IcnxnI 5 K for every n E N).
Obviously 0 E S and if there is no other element in S then the series
diverges by Theorem 11.2 for all x # 0, this is case (i). Let p = s u p s , if S
is bounded. Let now x be arbitrary if S is unbounded and 1x1 < p if S is
bounded. In either case it is possible to choose T E S with 1x1 < r and we
select such r. Then we obtain
The series C cnxn now converges absolutely, by the comparison test. Hence
we established convergence in cases (ii) and (iii). If 1x1 > p the series cannot
converge because the sequence n I-+ Cnxn is unbounded.
0
Remark 11.8
Nothing can be asserted concerning the behaviour of the
power series for
1x1 = p. The series C n x n diverges for all complex x with
1x1 = p
= 1, the series
1x1 = p = 1, the series
c,i
-xn
converges absolutely for all complex
x with
C(-l)n+lconverges conditionally for both x = 1
n
and x = -1 and the series
X2n
converges conditionally for
x
= -1 and
diverges for x = 1. The convergence or divergence of a given power series for
complex x with 1x1 = p is a rather delicate matter which we shall not attempt
t o solve.
Introduction to Mathematics with Maple
302
The series of the form
(11.21)
are also called power series, 20 is the centre of this power series. All statements for series (1) lead immediately to similar statements for series (11.21)
via the substitution y = x-XO. For instance, the analogue of Theorem 11.11
says that the series (11.21) converges for x = xo only, or for all x, or there
is a positive p such that (11.21) converges for 1x - zol < p and diverges for
Iz - xol > p. The number p is then the radius of convergence of (11.21).
Exercises.
Exercise 11.7.1
Find the radius of convergence p of the following power
series, and investigate the convergence for x = f p .
@ Exercise 11.7.2
I f lim J C n / C n + 1 J exists, prove that the radius of
n-co
convergence o f (11.20) is this limit. (Show that this is also true for
lim Icn/cn+l I = 0 or oa). Give an example o f a power series with posn-co
itive (and finite) radius of convergence for which lim ( C n / C n + l ( does not
n+m
exist .
[email protected]
Exercise 11.7.3
Let limsup
n+co
vergence p is given by:
(1) p =
a1
i f O < a , a~ R;
(2) p = O if a! = 00;
(3) p = 00 if a = 0.
= a.
Prove that the radius of con-
Series
303
This theorem was originally proved by A. L. Cauchy and then forgotten
until it was re-discovered by another French mathematician, J. Hadamard.
The theorem became rather important afterwards.
@ Exercise 11.7.4
and
Using the previous result show that the series
kCkXk have the same radius of convergence.
CkXk
@ Exercise 11.7.5
If there exists K E R and m E N such that lcnl 5 Kn"
for all n E N, prove that the radius of convergence of (11.20) is a t least 1.
Prove also that the radius of convergence of the series C nmxn is 1. [Hint:
The radius of convergence of the last series is 1 by Exercise 11.7.2; for the
first part use the the comparison test.]
11.8
Comments and supplements
To find the sum of an infinite series in a simple closed form is often difficult
or impossible. In Sections 11.1 and 11.2 we found sums of only a few series.
It can be shown that
1 + -1+ - +1. . .
22 32
lr2
=-
6
(1 1.22)
and that
1 +1 -1 + ... =log2
1-2 3 4
(11.23)
However, this cannot be done by merely finding the limit of the partial
sums; more theory is needed before we can attempt to establish (11.22)
and (11.23). (Perhaps we should mention that so far we have not even
defined lr or the function x I-+ logx.) We have already pointed out that
in cases where it is difficult or impossible to find the sum of a series it is
important to establish that the series converges. The tests we have so far,
namely the root and ratio tests, are rather crude, and many other tests for
convergence exist. These tests are based on comparison with another series
known to be convergent. The slower the convergence of this latter series,
the finer the test derived. We mention here a few more tests.
11.8.1
M o r e convergence tests.
For series with non-negative and decreasing terms the following theorem is
rather strong but has a simple elementary proof.
Introduction t o Mathematics with Maple
304
Ti
11
Theorem 11.12 (Cauchy's condensation test) If a1
a3 2 - - - 2 0 , then the series C ai converges if and only i f
does.
Proof.
I. Assume
2 a2 2
Pa2i
2ia2i converges. We obviously have
i=O
Consequently the partial sums of
verges.
Cai are bounded
and this series con-
00
11. Assume
C ai converges and put C ai = S. Then
i=l
2 a1 + f
C 2aa2i
i=2
k+l
22
C 2aa2i.
i=O
Consequently the partial sums of
verges.
22a2i are bounded and this series con-
We consider again En-" for a E Q (Examples 11.8,
11.9 and 11.10). Applying Cauchy's condensation test yields the result that
C n-" converges if and only if 2n(1-")does. This latter series converges
Example 11.19
if and only if 2l-" < 1, that is, if and only if a! > 1.
305
Series
Theorem 11.13 (Raabe’s test)
Let an
> 0. If there exists an
r > 1 and N E N such that
n ( 3 - 1 )
>_r
(11.24)
%+I
for all n > N , then the series
exists an N1 E N such that
Cui is convergent.
If, however, there
(11.25)
n(-&l)<l
1 for all n > N1 then Cui is divergent.
Proof.
It follows from (11.24) that
nun
2 (r + n)an+l > ( n + l ) a n + l ,
and
Hence the sequence n H nun is strictly decreasing after its Nth term and
it is also bounded below. Consequently it converges, which by the result of
Exercise 11.1.2 implies the convergence of the series
Since T - 1 > 0 it follows from (11.26) by the Comparison Test that the
series C a k + l also converges.
Turning our attention to divergence, we see that Inequality (11.25) implies that nun 5 (n l)an+l and consequently ( N l ) a ~ + 5
l nun for
n 2 N . It follows that there is a c such that 0 < c < nun for all n. By
comparing x a k with a multiple of the harmonic series we see that the
former series diverges.
0
+
Corollary 11.13.1
C ai
+
If an
> 0 and
converges. If this limit is less than 1 then this series diverges.
~
We know from Example 11.8 that the series
C l / n 2 converges.
The
Introduction to Mathematics with Maple
306
ratio test is not conclusive for this series. On the other hand,
= 2 t -1> 2
n(-&-+n[(+)2-l]
n
and Raabe's test yields convergence. Theorem 11.13can, of course, be used
for testing absolute convergence of a series; if however n(la,l/la,+ll- 1) 5
1, then it follows only that
ai is not absolutely convergent. For instance
converges but n(la,/a,+ll - 1) = ,,GI/(,,GI) 5 1.
We had only one test for non-absolute (conditional) convergence, namely
the Leibniz test. We note that this test requires the terms to alternate in
sign and to decrease in absolute value. There are many more general tests,
and we refer the interested reader to Knopp (1956) for Abel's, Dirichlet's
and Dedekind's tests.
c(-l)"+'/&
11.8.2
c
+
Rearrangements revisited.
We know from Section 11.5 that absolutely convergent series can be rearranged and the sum is not altered. We also had examples showing that a
rearrangement of a conditionally convergent series may diverge or have a
different sum. There is a famous theorem due to the German mathematician B. Riemann which states that a conditionally convergent series with
>g
real terms can be rearranged to have any sum.
Theorem 11.14 (Riemann's
gence) If A,ai E R and the series Cui converges conditionally,
then there exists a rearrangement
a,, such that Czl ant = A.
There
Xi
We shall not prove this theorem, but indicate the main idea of the proof
instead. We add up the positive terms of the series until we first exceed A;
hence we have a,,
anz . . . a,, > A. This can be done because the
series of positive terms diverges to 03. Then we add successively negative
terms until the sum is smaller than A for the first time. Thus
+
an,
+ +
+ anz + . .. + an, > A >
an1
+ anz + .. . + a,, + an,,, + ... + u n s .
Then we add positive terms to exceed A again, and by continuing with this
process of jumping up and down over A we obtain the required rearrangement.
307
Series
For series with complex terms the Riemann Theorem is not valid. This is
clear from the following: If an, bn E R, C an converges absolutely and C bn
converges conditionally then the series C(an zbn) converges conditionally
but by Theorem 11.8 the real part of the rearranged series will have the
same sum as the real part of the original series. On the other hand we have
the following corollary to the Riemann Theorem.
+
Corollary 11.14.1 (Rearrangement of series with complex
terms) I f an, bn E R and the series C ( a n zbn) converges conditionally to S then there is a rearrangement of the series which either
has a sum distinct from S or diverges.
+
~~
~~~
Proof. One of the series C an, C bn must be only conditionally convergent, hence by Riemann’s Theorem can be rearranged to a different sum.
If we apply the same rearrangement to C(an zbn) then the rearranged
series cannot have S as its sum.
+
11.8.3
Multiplication of series.
Infinite series which converge absolutely can be multiplied together. More
precisely, we have the following theorem.
Theorem 11.15 (Multiplication of series) If ai and
bi converge absolutely, (rn,n) H k is a bijection from N x N onto N,and
d k = anbm then the series
d k converges absolutely and
(2%)
n=l
( m=l
F b m )
= Fk =dl k -
(11.27)
Remark 11.9
The content of this theorem is easy to remember: one
multiplies the series C a n and Cb, by multiplying each term of the first
series by every term of the second series and then arranges the series with
terms anbm arbitrarily.
+ + +- - -
Example 11.20
If we multiply the series 1 x x2
with itself and
arrange the resulting series by increasing powers of x we obtain
(1 +x+x2
+ --)(l+x+x2 + - )
l + x + x + x 2 +x2 +x2 + - . = 1+2x+3x2+=
Introduction to Mathematics with Maple
308
for 1x1
< 1.Consequently
1+2x+3x2+--=
~
1
(1 - x)2'
It is often convenient to arrange the product series in the following form
00
+ (azh
ck = albl
k=l
a1b2) 4- (Qbi 4- aab2 4- aib3) 4-
+
*
- .
*
+ +
The series C cj with cj = ajbl aj-lb2
- - - albj is sometimes called
the Cauchy product of the series C a n and C bm. In Example 11.20 the
Cauchy product of C xk-' with itself was C k x k - l .
Example 11.21
itself is
(- 1)"+1
The Cauchy product of the series =
Jn
C cn where
This series diverges, indeed3
consequently lcnl
2
1
2
J a d m 2-n + l
for 0
with
5 k < n - 1, and
1, therefore lim cn does not exist and the series C c n
n+ca
must diverge. This example shows that the Cauchy product of two conditionally
convergent series may diverge. The so called Merten's Theorem asserts that
the Cauchy product of an absolutely convergent series with a conditionally
convergent series is convergent and has the obvious sum. For the proof see
Stromberg (1981) or Knopp (1956).
After these remarks and examples we turn to the proof of Theorem 11.15.
Proof.
Let A =
00
00
n=l
n=l
C Ian] and B = C Ibnl. Consider a sum
ldll
+ Id21 + +
* * *
where each dk above is of the form anbm. Let N be the maximum of these
n and M be the maximum of such m. Then clearly
3By 2ab
5 a2 + b2
Series
309
The partial sums of C ldkl are bounded and hence C d k converges absolutely. To find the sum of C d k it is sufficient to find a sum of a particular
rearrangement. Consider
Letting n -+
00
completes the proof of Equation (11.27).
0
We were led to infinite series by successive addition of terms of a sequence. Successive multiplication leads to infinite products. An example
of an infinite product is in Exercise 11.8.3. Some other examples of successive operations on the terms of a sequence and the taking of limits are in
Exercises 11.8.4 and 11.8.5.
11.8.4
Concluding comments
In conclusion we add a few historical comments. It was already Archimedes
who realized that the sum of a series must be defined and not be left as
(intuitively) obvious. His definition anticipated the modern one given by
Cauchy one and a half millennia later. Archimedes considered only (simple)
series with positive terms but for these his definition was equivalent t o
Definition 11.1. Johann Bernoulli (1667-1748) is often credited with the
divergence of the harmonic series; his proof is close to ours given in Example
11.9. The first who realized that the harmonic series diverges was most
likely Nicole Oresme (1323-1382). He employed the inequality we used in
Example 11.11. Oresme found the sum of
3 - +4. . .
-1 + -2+ - +
(11.29)
2 4 8 1 6
by the following clever trick. He calculated the area T of the infinite tower
in Figure 11.1
in two different ways. The areas of rectangles resting one on another
1
1
and leaning on the y-axis with top right hand corners at4 (1,l),( 2,2), (4’3)
1 1
are 1, - - - . - . Consequently
2’ 4
4The first rectangle is shaded in Figure 11.1
310
Introduction to Mathematics with Maple
Fig. 11.1 Oresme’s tower
On the other hand the areas of the ‘ ~ t e p sabove
’ ~ the intervals [2-”, 2-”+’]
are n2-n; hence the area of T is also
1
2
3
4
T=-+-+-+-+-..
2 4 8 1 6
.
Consequently
1
+ -2 + -3 + -4+
2 4 8 1 6
-
... = 2 .
For us to find the sum of the series in (11.29) is easy, it suffices to put
IC = 1/2 in Example 11.20 (and multiply by 1/2). This example illustrates
two points: the intellectual power of the mathematicians of the distant past
and that the wisdom accumulated in mathematical theories makes problems
easier to solve.
The history of infinite series is interesting, unfortunately we have to
leave it now.
5The second step is shaded differently in Figure 11.1
31 1
Series
Exercises.
Exercise 11.8.1
For which values of a is the series
a(a - l ) ( a- 2). . . (a - k
k!
convergent? The symbol
for a E R, k E
+ 1)
N.
Exercise 11.8.2
Using Raabe’s test prove the convergence of
( 2 n - 1) .-
@ Exercise 11.8.3
Prove that
n-mo
@ Exercise 11.8.4
1
Let
i=l
a1 =
2,
a2 =
2
+ -2’1
a3 =
2
1
+ 7,
- - . Give
2+-
2
a recursive definition of an. Prove that lim an exists and find it. (The
n-+m
sequence n H an is an example of a continued fraction. Continued fractions
play an important part in number theory and numerical analysis.)
Exercise 11.8.5
Let
Give a recursive definition of xn. Prove that lim xn exists and find it.
n-cc
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Chapter 12
Limits and Continuity of Functions
Limits of functions are defined in terms of limits of sequences.
With a function f continuous on an interval we associate the intuitive idea of the graph f being drawn without lifting the pencil
from the drawing paper. Mathematical treatment of continuity
starts with the definition of a function continuous at a point; this
definition is given here in terms of a limit of a function at a point.
In this chapter we shall develop the theory of limits of functions,
study continuous functions, and particularly functions continuous
on closed bounded intervals. At the end of the chapter we touch
upon the concept of limit superior and inferior of a function.
12.1
Limits
Looking at the graph of f : x
X
- x) (Figure 12.1), it is natural to
1x1
say that the function value approaches 1 as x approaches 0 from the right.
Formally we define:
w
-(l
efinition 12.1
A function f , with domf c R, is said to have
a limit 1 at 2 from the right if for every sequence n
xn for which
2, + 2 and x, > 5 it follows that f(xn) + 1. If f has a limit 1 at 2
from the right, we write limf(x) = 1.
Xla:
(
Definition 12.2 If the condition xn > 2 is replaced by xn < 2 one
obtains the definition of the limit of f a t 2 from the left. The limit of
f at 2 from the left is denoted by limf(x).
Remark 12.1
xt2
The symbols x J, 2 and x
313
J
2 can be read as “x decreases
314
Introduction to Mathematics with Maple
to k” and “ x increases t o 2” , respectively.
-1
-1.5
-0.5
-0.5
-
X
Fig. 12.1 Graph of f(z)= -(1
-x)
1x1
Example 12.1 We now prove, according to Definition 12.1, the limit from
the beginning of this section; that is,
X
lim -(I
XlO
If
Xn +
0 and xn
X
>
1x1
Xn
0 then -(1
lxn I
Xn
lim-(1 - x ) = -1 because -(1
x t o 1x1
lxnl
xn - 1 + -1 if xn 4 0.
-x) =
1.
- xn) =
- xn)
= xn
(1 - xn)
-
+
1 for xn
1. Similarly,
<0
and since
Remark 12.2
It was natural to say in Example 12.1 that f has a limit
from the right a t 0 even though f was not defined a t 0. And it still would be
natural to say so if f was defined a t 0 no matter what f(0) was. It should be
remembered that Definition 12.1 (or 12.2) ignores the value of f a t 2 .
Remark 12.3 It is clear from Definition 12.1 (Definition 12.2) t h a t two
functions f and g which differ a t most for x 5 2 ( x 2 5) have the same limit
from the right (from the left) a t 2 or they have no limits a t all.
Limits and Continuity of Functions
315
Example 12.2
The function f : II: H saw(l/z) has no limit from the
right a t 0. (The saw function is defined in Exercise 4.6.8 in Chapter 4.) A
partial graph of f is sketched in Figure 12.2. It consists of triangles of height
1/2 whose lateral sides are slightly curved' and base is formed by the interval
[l/(n l), l/n] of the x-axis. Clearly, if we choose to take as points of the
I), for k E N,then the values of
sequence x2k-1 = l / k and 221, = 2/(2k
saw(l/sn) alternate between 0 and 1/2. Obviously the sequence n H f(zn)
does not converge and the limit limsaw(l/z) does not exist.
+
+
x:To
X
Fig. 12.2 Graph of f(z) = saw(l/z)
Theorem 12.1
A function f has a t most one limit from the right
at 2 (at most one limit from the left at 2).
Proof. If I and L are limits of f at 2 from the right, zn -+ 2 and zn > 2 ,
then by Definition 12.1 f(zn)-+ I and also f(zn)-+ L. Consequently I = L
(by Theorem 10.1).
0
The great advantage of Definitions 12.1 and 12.2 is that they make it
possible to use the already established theory of limits of sequences (see
Chapter 10) for limits of functions. This was clearly exploited in the proof
of Theorem 12.1 and we shall exploit it further soon. However, we state
two more definitions first.
'They are congruent to pieces of the graph of z
I+
1/x
Introduction to Mathematics with Maple
316
Definition 12.3
A function f is said to have a limit I at 2 if for
every sequence n H x, with x, # 2 and x, --+ li. it follows that
The limit of f at 2 is denoted by lim- f(x).
2-5
Remark 12.4 We left the wording of Definition 12.3 intentionally a little
ambiguous. The meaning is changed according to whether x, E R or C. If
nothing is said to the contrary we shall assume that x, E R and if z, E C
we shall explicitly say so. We also may make the distinction verbally by saying
t h a t the limit is in the real or the complex domain.
Example 12.3
lim x2 = 4. The proof is simple: if xn
x-2
--+ 2
f(x,)
then
=
xi converges to 4.
If domf c R and the symbol 2 in D e f i n i t i .
6efinition 12.4
12.3 is replaced by the symbol +oo or -ca (and the inequality x, # 2
omitted) then one obtains the definition for the limit off at +oo or -m
respectively. The limit of f at +m or -oo is denoted by xli~OOf(x)
or lim f(x) respectively.
x+-m
The letter x in any one of the symbols lim- f(x), limf(x), limf(x),
x+x
xtx
xlx
lim f(x) or lim f(x) can be replaced by any other letter without chang-
z--t+Oo
24-00
ing the meaning of that symbol. For example lim t2 = lim h2 = 4.
t-2
h-2
The next theorem can be proved in similar way to Theorem 12.1.
Theorem 12.2
+oo and -00.
Example 12.4
then
1
-
--+
A function f has at most one limit at each o f 2 ,
1
lim - = 0. Again the proof is simple: if xn -++oo
x++m
x
0 by Exercise 10.5.5.
Xn
Example 12.5
We find
d
lim (X
X+-00
+ d x 2 -x).
If
Xn
-+
-a then
G -+ +m and Theorem 10.6 concerning the limit of a sum is not appliX
This
cable. We first write x + d
z= xx2--&x2r+Tx -- x - IXldrn'*
last expression is equal to
1
for x < 0. Now if x,
l+Jm
--+
--oo then
317
Limits and Continuity of Fzlnctions
1/x,
0 and
-+
1
-+
l+d-
1
Consequently lim (x
2'
X--*--oo
+ d z )=
112.
If limAf(z) = 1 and lim- g(x) = k then
Theorem 12.3
x+x
X+X
(i) 5-2
lim lf(4 = 1%
(ii) lim(f(z) f g(x)) = 1 f k ,
2-2
(iii) lim f(x)g(x) = Zk,
2-2
(iv) if k
f(x) = 1
# O then limAg(x)
k'
> 0, r E Q then x+x
lim [f(z)]' = Z',
2-2
(v) if Z
(vi) limAMax(f(x), g(x)) = Max(Z, k),
5-2
lim Min(f(x), g(x)) = Min(l, k ) .
x-x
Proof. All assertions (i)-(vi) follow directly from Definition 12.3 and
corresponding theorems for limits of sequences. As a sample we prove
(iii). Let x, --+ 2 and z, # 2 , then f(xa) -+ 1 and g(xn) -+k by Definition 12.3. Using Theorem 10.6 for the limit of a product of sequences
we have f(xn)g(zn) -+ Zk which in turn implies by Definition 12.4 that
x+x
limAf(x)g(x)
= Zk.
0
Remark 12.5
An important case of assertion (iii) is g(z) = k for all
x. Then lim kf(x) = k lim f(x). For k = -1 we have limA[-f(x)] =
x+x
- lim- f (z).
X+2
2-2
2-2
Remark 12.6
All assertions in Theorem 12.3 remain valid if
(a) 2 is replaced everywhere either by +oo or by -m;
-+ 2 is replaced everywhere by either x I 2 or by x
(b) x
Example 12.6
We wish to find lim
x+-2
x2
3x
x2-4
+
+
2 . We cannot use (iv) of
Theorem 12.3 directly since lim (x2 - 4) = 0. Since
x+-2
for x
#
-2 we have lim
x+-2
T 2.
x2+3x+2 - x + l
22 - 4
x-2
1
x2+3xt2
x+l
= lim -22 -1
x+-2x-2
4'
Remark 12.7 In the previous example we used an analogue of Remark 12.3:
if two functions differ a t most when x = 2 and one has a limit 1 a t 2 then so
318
Introduction to Mathematics with Maple
does the other. More generally, if there exists A > 0 such that f(x) = g(x)
for 0 < 1x - 21 < A then lim f(x) = 1 implies that limAg(x) = 1. This is clear
x+x
2-2
from the definition of a limit.
The Squeeze Principle (see Theorem 10.3) is also valid for limits of
functions.
If there exists S > 0 such that
Theorem 12.4
g(z> L f(x) 5
for O
(12.1)
< Ix - ?I < S and lim g(z) = lim h ( z ) = 1 then f has a limit at
2-2
2 and lim f(z)= 1.
x+x
X-Kt
Since the formulation of the theorem involves inequalities, the functions
g, f, h are automatically assumed to be real valued.
Proof. The proof follows the same pattern as the proof of Theorem 12.3.
If x, -+ 2 and x, # 2 then there exists N such that 0 < Ix, - 21 < 6 for
n > N . Consequently
g(xn) 5
for n
f (x,)
f(%) L h(xn)
> N and it follows from the Squeeze Principle for sequences that
--f
1. That in turn implies that lim f (x) = 1.
242
Example 12.7
We wish t o calculate lim
y
Y
W
-1
x+o
22
0
. In order t o apply
the theorem we note that, by Exercises 5.8.5 and 5.8.6 with x replaced by 3x2
and n = 10,
ConsequentIy
3
10y/-
1qiT-32-l < -3
22
- 10'
Now simple application of Theorem 12.4 shows that the limit is 3/10.
Remark 12.8
Theorem 12.4 remains valid if
+
(a) the inequality 0 < lx - 5
1 < S is replaced by 5 < x < x S (x - b <
x < 2), and limit is replaced by limit from the right (limit from the
left) ;
319
Limits and Continuity of Fzlnctions
(b) one assumes the existence of K (k)such that inequality (12.1) is valid
for x > K (x < k) and limits are replaced everywhere by limits at +oo
(-4.
There is a simple relation between the limit at +oo and the limit from
the right at 0.
Theorem 12.5
We have
lim f(x) = 1 if and only if
l i m f ( l / y ) = 1.
YlO
z++m
Proof.
I. Assume that lim f(x) = I and let yn > 0 and yn
z++m
+oo by Exercise 10.5.5 and therefore f(l/y,)
--+
-+
0. Then l/y, --+
I ; that is, limf(l/y) = 1.
Y10
11. Assume limf(l/y) = 2 and let x n
4
+oo. Then there exists n such
Y10
that x, > 0 for n > N . For n > N set yn = l/xn. Then yn -+ 0 again by
Exercise 10.5.5 and consequently f(l/y,) -+ 1. However f(l/y,) = f(xn)
and hence f(xn) -+ 2 which proves z lim
4 t m f(s)= 1.
0
12.1.1
Limits of functions in Maple
Using Maple for evaluating limits is easy. The form of the command is
limit (fvalue,x=relevantpoint ,direction) ;
fvalue stands for the value of the function for which the limit is sought,
relevantpoint can be infinity or -infinity as well as some numerical
value, direction can be left, right or complex. If the limit is in the
real domain in the sense of Definition 12.3, two-sided so to speak, then
direction should be omitted. The following examples illustrate some possibilities.
>
limit(2*x+floor (-x) ,x=3,right) ;
2
>
limit(x/abs(x) ,x=O,left);
-1
Introduction to Mathematics with Maple
320
1
>
l i m i t ( x + s q r t (xn2-3*x) ,x=-inf i n i t y ) ;
3
2
Sometimes Maple needs a little help
>
l i m i t ( l / f loor (x) ,x=inf i n i t y ) ;
lim
X+OO
1
floor(x)
0.
Now there is an obvious question: is this limit exactly O? Since
o < - - <1 -
1
1x1 - x - 1
for x > 1, it follows from the Squeeze Principle that, indeed, the limit is
exactly 0.
Exercises
Exercise 12.1.1
and by Maple.
Find the following limits by paper and pencil method
+
+
2x2 - 32 1 .
x2-4
’
2x2 - 32 1 .
(2) lim
x-2
x2-4
’
2x3 - 2x2 ix - 1.
(3) lim
x+l x 3 - X Q - 3 x - 3 ’
(1) lim
z-1
(4) lim
x-1
X2-&
&-1’
321
Limits and Continuity of Functions
(5)
(x
lim
(6) lim
lim
x+o
(8) lim
7
Ye2-1.
7
22
ym-qi=%*
x
2-0
ª9)
,
7
JiT7-1.
240
(7)
+ l)d2 -x
22-1
x+-1
+2 2
7
+ + (x +
1)5
lim
2)5
+
+ . . . + ( x + 1000)5
-
x5 10020
(10) lim x (&?Ti
- x);
X-++oO
7
x++oO
Exercise 12.1.2
Use the Squeeze principle to prove:
(1) lim Ll/xjx = 1;
2-0
(2)
+
x2 1
lim -= -1;
x+-m
(3) lim
IzllxJ
lVE2-1.
x+o
7
X
(4) lirn x Iq(x),
x+o
[Hint: For (3) use Exercise 5.8.5; for (4) use I z l ~ ( z ) 5
I
The following exercises are stated as theorems-the
them.
3
Exercise 12.1.3
If a
# 0 and ij
= a2
Izl.]
task is to prove
+ b then x+x
lim f ( a z + b) = 1 if and
only if limAf ( y ) = 1. Is this statement true if limits are replaced everywhere
Y+Y
by limits from the right? From the left? [Hint: No, but remains true if
a > 0.1
3
Exercise 12.1.4
What happens if a
9 Exercise 12.1.5
If a > 0 then
< O?
lim f ( a x
X++m
Exercise 12.1.6
f ( y ) = 1.
lim f(x) = I @ lirn f(-y) = 1.
y--’-m
2++oO
@
+ b) = 1 @ y++m
lim
lim f(z)= I
x+x
@
lirn f(2
h+O
+ h ) = I.
State similar
theorems for the limit from the right and for the limit from the left.
Introduction t o Mathematics with Maple
322
@ Exercise 12.1.7
limf(x) = lim f ( 2
zlx
t++m
+ $.1
@ Exercise 12.1.8 If lim f(x) = 0 and there are positive 6 and M such
x+c
that 1g(x)l 5 M for O < Ix - cI < 6 then lim f(x)g(x) = 0.
x-c
12.2
The Cauchy definition
The great advantage of Definition 12.3 is that the theory of limits of sequences carries over easily to limits of functions. Sometimes it is a little
inconvenient to prove that f(z) -+ l for every sequence n I--+ xn with x, -+2
and xn # 2. This inconvenience is often overcome by using Theorem 12.6
below, which is similar to the E-N definition of a limit of a sequence.
If 2,1 E R and E , S are positive numbers then we call the set
an epsilon-delta box centred at ( 2 , l ) (or briefly, an E-6 box: see Figure 12.3).
This €4box is the interior of a rectangle with vertices at (2 f 6,l f E ) .
Theorem 12.6 below states that f has a limit 1 at 2 if for every E > 0 there
exists 6 > 0 such that the graph of f near the point ( 2 , l ) with the possible
exception of ( 2 ,f(2))lies in the €4box centred on ( 2 , l ) (see Figure 12.3).
The graph of f enters the E-S box at the left vertical side and leaves it at
the right vertical sight of the box. This happens for every positive & no
matter how small E is.
The function f has a limit 1 at 2 if and only if for
Theorem 12.6
every E > 0 there exists 6 > 0 such that
0 < 132 - 21
< 6 * If(.)
- lI < E .
(12.2)
Proof.
I. Let E > 0 and assume there exists 6 > 0 such that the implication
(12.2) holds. Let z n + 2 and x n # k , then there exists N such that
0 < n1. - 21 < S, and consequently
- 11 < E for n > N . This proves
f(xn) --+1 and hence limAf(x) = 1.
x-x
11. Assume f has limit I but that it is not true that for every E > 0 there
exists 6 such that the implication (12.2) holds. This means there exists
EO > 0 for which there is no suitable 6, and in particular 6 = 1/n for n E N
is not suitable. In other words, for every n E N there exists a number Z n
If(?)
Limits and Continuity of Functions
323
such that 0 < lxn - 21 < 1/n but I f ( x n ) - I1 2 EO. Now we have a sequence
n H xn such that xn # ? and xn + ? by the Squeeze principle,2 but the
sequence n H f(zn)does not converge to I . Hence I is not the limit of f
at 2 , a contradiction, and so there must exist a 6 for every E such that the
implication (12.2) holds.
0
Fig. 12.3 Interpretation of Theorem 12.6
Remark 12.9 The interpretation of Theorem 12.6 with the E-S box makes
sense only if domf c R and I E R. However the theorem itself is valid in the
complex domain as well.
Remark 12.10
Theorem 12.6 remains valid for the limit from the right
and the limit from the left if the inequality 0 < Ix - 21 < S is replaced by the
inequality 2 < x < 2 S and 2 - 6 < x < 2, respectively.
+
Remark 12.11 The modification of Theorem 12.6 for limits a t +oo or
-oo reads: f has a limit I a t +oo or --oo if and only if for every E > 0 there
exists K or Ic such that
respectively.
More precisely by Corollary 10.3.1.
Introduction t o Mathematics with Maple
324
As an illustration of Theorem 12.6 we prove t h a t lim z3=
Example 12.8
x-+-1
+
-1. In this case f(z)- I = x3 1 and we rewrite this expression in a more
convenient3form: (x+l- 1)3 1 = (x+ 1)3-3(x+ 1)2+3(x+1). Consequently
1z3 11 5 I
z 11(1z 112 312 11 3). Therefore, if Iz 11 < 1 then
1z3 11 5 712 11. For E > 0 we can choose S = Min(~/7,1)and we have
1 < 6 +-1.3 115 7s < &.
+
+
+
+
+
+
+
+ + +
+
+
Some authors use the implication (12.2) as the definition of a limit of
f at 2. More precisely they say: ‘f has a limit 1 by definition if for every
positive E there exists a positive S such that implication (12.2) holds.’ Such
a definition is often referred to as Cauchy’s definition of the limit. Often it
is also called the ~ - 6definition of the limit. Definition 12.3 is named after
the German mathematician Heine.
Theorem 12.6, in conjunction with Remark 12.10, makes the following
theorem fairly obvious.
I( Theorem 12.7
Let I be an open interval and f : I t-+ R has a limit
at 2 if and only if lim f(z)= lim f(z)and then lim f(z)= lim f(z)=
XcTX
xt?
XlX
1
xi?
lim f(z).
2-2
Proof.
I. If lim f(x) = I then for every E > 0 there exists S > 0 such that
5-2
that is,
and consequently limf(z) = 1. The proof that limf(x) = I is similar.
xtx
XlX
11. If limf(z) = limf(x) = 1 then for every E > 0 there exists 61 > 0 and
Xt?
62
[email protected]
> 0 such that
i - s1 < x < 2
* If(.)
- 11 < &,
(12.3)
- 11 < E ,
(12.4)
and
2 <z <2
+ 62 * If(.)
3This really amounts to the use of the Taylor polynomial (see Section 6.5) but the
situation is simple enough for some elementary algebra.
325
L i m i t s and C o n t i n u i t y of Functions
respectively. Let S = Min(&,&). Then we have: if 0 < Ix - 21 < S then
either 2 - 61 < x < 2 or 2 < x < 2 62, and in either case we have
+
If(.)
-
ZI < E
0
by (12.3) or (12.4).
As another application of Theorem 12.6 we prove:
Theorem 12.8
Let r g f
lim- g(x) = k and Z
< k then
limAf(x)
c R. If 2-2
there exists A > 0 such that
cR
and rgg
= 1,
2-2
f (4 < g ( 4
when 0
< Iz - 21 < A.
Proof. The proof is analogous to the proof of Theorem 10.7. We choose
k-Z
&=-
0
. There exist 61 > 0 and 62 > 0 such that
L
l+k
O<~x-~~<sl~f(x)<z+&=-
(12.5)
< Ix - 21 < 6 2 * -= k - & < g(x).
(12.6)
2
and
0
I+k
2
Let A = Min(61, Sz), then if 0 < Ix - 21 < A then also 0
0 < Iz - 21 < 62, and then by (12.5) and (12.6)
< Iz - 21 < 61 and
follows.
0
If lim f(z)= I < k then there exists A > 0 such
x+x
that f(x) < k when 0 < 1x - 21 < A. Also, if lim- g(x) = k > 1 then
2-5
there exists A > 0 such that g(x) > I when 0 < IJ: - 21 < A.
Corollary 12.8.1
Particularly important cases of this corollary are I = 0 or k = 0; if the
limit of f is positive at 2 then f is positive near 2 too (but not necessarily
at 2 itself).
326
Introduction to Mathematics with Maple
Theorem 12.9 If lim f(x) = I and limg(z) = k andifthere exists
X+2
x+x
a positive u such that
f (4 I
9(4
(12.7)
for O < Iz - 21 < u,then I 5 k.
Proof. Assume contrary to what we want to prove that I > k. Then
by Theorem 12.8 (with the roles of f and g interchanged) f(z)> g(z)
for 0 < Iz - 21 < A for some A > 0. Hence (12.7) does not hold if
0
0 < Iz - 21 < Min(a, A), and this is a clear contradiction.
Analogues of Theorems 12.8 and 12.9 as well as Corollary 12.8.1 are
valid for limits from the right, from the left, at +oo and at -00. Proofs of
these are left for the exercises.
Bounded monotonic sequences always have limits. A similar theorem
is valid for functions. The Cauchy definition is a convenient tool for the
proof.
Theorem 12.10 (Limits of monotonic functions)
I f f :]a,b[~+R is monotonic and bounded then
exist.
Proof. We shall assume that f is increasing: if it is not, the theorem for
an increasing function can be applied to -f. We prove that the first limit
is equal to
I = inf {f(x);x € ] a ,b [ } .
The proof that
lim f(z)= sup {f(x);z € ] a ,b [ }
xtb
is very similar. Let E > 0. By the definition of the greatest lower bound
there exists 2 € ] a ,b[ such that
327
Limits and Continuity of Functions
For g = a
+ 6 we now have, by monotonicity of f,
1 I f(z)I f(z)< I +
for all 11: with a
E,
< J: < a + S.
0
A monotonic function, although having limits from right and left, need
not have a limit. For example limsgn(z) = -1 and limsgn(z) = 1. Conse-
xto
210
quently there is no limit at 0.
There is also an analogue to Theorem 12.6 for limits at
The function f has a limit I at
Theorem 12.11
+00
or
-00:
00,
lim f(x) = 1,
x++OO
if and only if for every
> 0 there exists K such that x > K
E
+
lf(4- 4 < E .
Proof.
By Theorem 12.5 lim
f(11:)
= 1 if and only if limf(l/y) =
1.
Y10
X-++OO
This latter equation holds if and only if for every E > 0 there exists 6 > 0
such that
0<y
< 6 + Jf(l/y) - 11 < E .
We now set K = 1/S and x = l / y and see that the above implication is
0
equivalent to 11: > K +
- 11 < E .
).(fI
Remark 12.12
if for every
E
For the limit a t
-00
we have
lim f(z)= I if and only
X+--oo
> 0 there exists k
such that
x <k
+ If(.)
- I1
< E.
Exercises
Exercise 12.2.1
(1) h ( 2 s
5-2
Using Theorem 12.6 prove that
+ 3) = 2%+ 3,
(2) ai_m,x2 = u2,
(3) lim 1/x = 1/c provided c # 0.
x4c
[Hint: For (2) use the same trick as in Example 12.7; for (3) use the inequality 11/11:- l/cl
1x - cI/lxcl 2111: - cl/c2 for 1x1 > Ic/al.]
<
Exercise 12.2.2
<
Using Theorem 12.6 prove that
Introduction to Mathematics with Maple
328
(1) limsgnz = -1; (see Exercise 3.2.3 for the definition of sgnz)
zto
(2) limsgn z = 1;
210
(3) limlxj = n
and 1imLzJ = n - 1
(4) liml-zJ = -n - 1
xln
( 5 ) lim(1zJ
x-+7
for n E
ztn
xln
and liml-zJ = -n
+ L-xJ) = -1;
Z;
for n E Z;
xtn
[Hint: For ( 5 ) you can use the results of (3) and (4) together with Theorem 12.7, but a direct proof is also easy.]
The following exercises are stated as theorems. The task is to prove
them.
@ Exercise 12.2.3 If limf(x)
Xla:
exists S > 0 such that
= 1,
limg(z) = k and I
212
< k , then there
State and prove an analogous theorem for the limit from the left.
@ Exercise 12.2.4
If lim f(z)= 1, lim g(x) = k and 1 < k then there
2++00
z++m
exists K such that
x >K
+ f(z)< g(x).
State and prove an analogous theorem for the limit at -w.
@ Exercise 12.2.5
If lim f(x) = I then there exists 6
x+x
> 0 such that f is
+
bounded on ( 2 - S,2 6). State and prove similar theorems for the limits
from the right, from the left and for limits a t +w or -GO.
@ Exercise 12.2.6
Theorem 12.9 remains valid if x --+ k is replaced by
x 2 or by x 2 and the inequalities 0 < Ix - 21 < o by 2 - o < x < 2 or
by 2 < x < 2 CT respectively.
+
@ Exercise 12.2.7
If lim f(z)= I ,
z++m
lim g(x) = k and if there exists
X++W
K such that
then 1
5 k.
0 Exercise 12.2.8
If x E Q let x = p / q with p E Z and q E N and p
and q relatively prime (the only positive integer dividing both is 1). Define
Limits and Continuity of Functions
329
f as follows: f(x) = I/q if x E Q and f(x) = O if x
lim f(x) = 0 for 2 E Q.
#
Q. Prove that
x+x
12.3
Infinite limits
Functions, like sequences, may have infinite limits. The following definition
covers the possibilities of a function having +oo or -00 as a limit.
1
/Definition 12.5
If in the definitions of a limit from the right at x,
limit from the left at 2, limit at 2, limit at +00 or limit at -00, the
1etter.Z denoting a real number is replaced by the symbol +00 or --oo
one obtains the definition of an infinite limit. Iff has a limit +00 from
the right at 2 , from the left at 2 , at 2 , at $00 or at -00, we write
limf(x) = +00, limf(x) = +00, lim f(x) = +00, lim f(x) = +00
512
xT2
x++OO
245
or lim f(x) = +00, respectively. A similar notation is used for the
X+--oo
.infinite limit
-00.
Typical behaviour of functions which have infinite limits at 2 is sketched
in Figure 12.4. For the function f shown
lirn f(x) = +00,
5-+-OO
lim f(x) = -00,
2-00
lim f(x) = 00,
x+o
while
Limits from Definitions 12.1-12.4 are sometimes called finite limits, particularly if one wants to emphasize that the limit in question is not an
infinite limit.
Example 12.9
Clearly lirn x = +00,
x++00
1
1
lim- = +oo, lim- = -00,
210 x
xto x
or infinite.
lim x =
-00,
lim x2 = +00,
2+-m
X+--oo
1
1
lim - = +00 but lim - does not exist, finite
2-0 x
x+o 2 2
Many theorems concerning finite limits can be extended to infinite limits, but not all can and care is needed. For instance, if limAf(z) = +00
then lim af(z) = +oo if a
X+?
> 0 and lim- a f ( x ) =
x+x
-00
if a
< 0 and ob-
2-2
viously lim*uf(z) = 0 if a = 0. Some extensions of Theorem 12.3 are
x+x
discussed in Exercises 12.3.3, 12.3.5 and 12.3.7. Generally speaking, no
Introduction to Mathematics with Maple
330
- 8 - 6 4 - 2
-
2
4
6
8 1 0 1 2
Fig. 12.4 Behaviour of functions with infinite limits
+
conclusion can be made concerning limA(f(x) g(z)) if lim f(z)= +oo
2-2
2-2
and limAg ( x ) = -m. For questions of this kind see Exercises 12.3.4, 12.3.6
2-2
and 12.3.7.
An analogue of the Cauchy definition for infinite limits is the following:
Theorem 12.12 Let rg f c R. Then limAf(x) = +m if and only
2-2
if for any K there exists 6 > 0 such that
0
<
- 21
< 6 + f(x) > K.
(12.8)
Proof.
I. Take any K and assume that there exists 6 > 0 such that (12.8) holds.
Let x, # 2 and x, -+ 2, then there exists N such that 0 < Iz, - 21 < 6 for
n > N . By (12.8), f(xn) > K for n > N . This proves that lim f(z)= +oo.
2-2
11. Assume lim- f(x) = +oo but that for some KOthere does not exist 6 > 0
2-2
such that (12.8) holds. Then for every n E N there exists a number x, such
that 0 < lx, - 21 < l / n but f(xn) 5 KO. Then x, # 2 and x, -+ ? but
Limits and Continuity of Functions
331
f(xn) -+ +oo is false. Consequently lim- f(x) = +oo is false, which is a
x+x
contradict ion.
0
Remark 12.13
Theorem 12.12 remains valid for the limit from the right
and for the limit from the left if the inequality 0 < Ix - 51 < S is replaced by
2 - 6 < x < 5 and by 2 < x < 5 S respectively.
+
Remark 12.14
f 5g
It is an immediate consequence of Theorem 12.12 that if
and limAf(z)= +oo then lim-g(x) = +oo. This remains valid for
x+x
x+x
limits from the right, from the left, and limits a t +oo and
We have lim (22 -
Example 12.10
x++oO
1.J)
=
+GO.
-00.
It follows from the
definition of the floor function that x 2 1x1 and therefore 22 - 1.J
result then follows from Example 12.9 and Remark 12.14.
Exercises
Exercise 12.3.1
(1) lim
-
Find the following limits.
1
x 1 ~ x2 2’
(2) lim
1
- a
X T d w 2
- 2’
(3)
2x+1
st-1 23 + 1’
(4) lim
(5)
lim xn,
n E N;
X+-oO
1
(6) lim -
lim - n E N ;
210 x n ’
x3 1
(7) lim
x++oO x2
52 - 7’
x3 1
(8) lim
x+-m 2 2 + 52 - 7 ’
ZTO 2 n ’
+
(9)
+
+
2 x.
The
Introduction to Mathematics with Maple
332
1
@ Exercise 12.3.2
Prove: lim -= 0
5-2
f (2)
* x+x
lim lf(z)I = +oo. Also prove
this for lim and lim. [Hint: Use Exercise 10.5.5.1
xtx
xlx
@ Exercise 12.3.3
Prove: If lim f (x) = +oo and g is bounded below on
xtx
some interval ( 2 - 6, 2 ) then
lim(f (x) g(x)) = +a.
Deduce that lim(f(z) g(x)) = +oo if lim f(z)=
+
+
xtz
XTX
ztx
+oo and limg(z) = I or +oo.
xf2
@ Exercise 12.3.4
Let F be an arbitrary function with dom F = (-00,2).
Find functions f and g such that limf(z) = +oo, limg(z) = --oo and
f
+ g = F.
-00,
XT2
XTX
+ g(z)) can be any number, +oo, or
xtx
1
or need not exist at all. [Hint: 2f(x) = IF(x)I + F ( z ) + -g(x) =
x-x
Deduce that lim(f(x)
F ( 4 - f(4.1
@ Exercise 12.3.5
Prove: If lim f(z)= +oo and there are positive m and
XTX
6 such that 2
-6
< x < 2 + g(x) 2 m then limj(x)g(x) = +oo.
xt2
@ Exercise 12.3.6
Let F be an arbitrary function with dom F = (Loo, 2 ) .
Find f and g such that lim f(x) = +oo, limg(x) = 0 and f g = F. What
XTa:
XTj;.
can you say about limf(z)g(z)? [Hint: f(z)= IF(4I + 1 9 ( 4 = F(4
X t2
2 - X
fo'
The limit need not exist but if it does, it can equal anything.]
@ Exercise 12.3.7
Prove: I f f is bounded on some interval 2 - 6 < x
and limg(x) = +oo then lim-f (4 = 0, and if moreover f(x)
xtx
XTX 9 ( 4
g(x)
2 - 6 < x < 2 then lim= +m.
XTX
>0
<2
for
f (4
@ Exercise 12.3.8
State analogues of Exercises 12.3.3-12.3.7 for limits,
limits from the right, and limits at +-oo and -GO.
12.4
Continuity at a point
A function f is continuous at a point 2 if it is possible to find an approximate
value of f(5) from an approximate value of 2. Formally, we make the
following definition:
Limits and Continuity of Functions
333
Definition 12.6 (Continuity) A function f is said to be continuous
at 2, continuous from the right at 2 or continuous from the left at 2 if
(respectively.
Example 12.11
By Example 12.8 the function x H x3 is continuous a t
3, the floor function is obviously continuous from the right a t n E N since
liml(z)J = n = LnJ and similarly the function ceil is continuous from the
xln
left a t n E N. More generally, every polynomial is continuous a t every 2 , the
greatest integer function is continuous from the right a t every point and the
ceil function is continuous from the left a t every point.
Usually, we say discontinuous instead of not continuous. For example
the floor function is discontinuous from the left at any integer and the
function ceil is discontinuous from the right at any n E Z. In mathematics
it is sometimes convenient to consider functions which have values 00 or
-00.
We do not do this in this book and, in particular, in Definition 12.6
the limit is understood as a finite limit. However, even in situations where
infinite values are allowed, the function is considered to be discontinuous
where lim f(x) = f(2)= f o o .
2-2
In the past, mathematicians believed that for a function to be continuous everywhere was normal and to be discontinuous was an exception.
To a certain extent this is somewhat tenable even today in the sense that
most functions encountered in applications are continuous apart from a few
points. However, today some functions discontinuous at many or even all
points of the domain of definition play an important part in mathematics
and in applications. Some examples of functions with discontinuities are
discussed in Exercises 12.4.4-12.4.6. Obviously there are many functions
continuous everywhere, for instance polynomials. The exercises contain examples of functions continuous at one point only, functions discontinuous
everywhere and functions continuous a t all irrational points and discontinuous at all rational points. It is an interesting fact that there is no function
continuous at all rational points and discontinuous at all irrational points.
The proof of this is beyond our reach at this stage.
If f is discontinuous at 2 and limAf(z)exists, the discontinuity at 2 is
2-2
called removable. This is because the definition of f can be amended by
declaring f(2) = lim- f(x) and thus making the function continuous at 2.
x-x
334
Introduction to Mathematics with Maple
The changed function is, strictly speaking, a new function but it is customary to keep denoting it as f and identify it with the old f . Understanding of
this point sometimes helps in dealing with some computer system algebra.
For instance in Maple substituting x = 2 is, quite naturally, not allowed
in h(x) = (x - 2 ) 2 / ( x 2 - 5x 6) but the Maple solution of h(x) = 0 is
x = 2. Maple automatically removes the singularity at x = 2 by setting
h(2) = lim h(x) and solves the equation after that.
x+2
Almost all statements about limits of functions translate into statements about continuity. Here we illustrate this by several corollaries to
theorems or definitions which we need for future reference. One corollary
to Theorem 12.7 is:
+
Corollary 12.7.1 (to Theorem 12.7) A function f is continuous
at 2 i f and only i f it is continuous both from the left and from the
right at 2.
The Cauchy definition of a limit translates into the Cauchy definition
of continuity as follows.
Corollary 12.6.1 (to Theorem 12.6) f is continuous at 2 if and
only if for every E > 0 there exists S > 0 such that
Ix - 21 < S 3 If(x) - f(?)I
< E.
(12.9)
It is a detail but an important one: the inequality 0 < Ix - 21 from
Theorem 12.6 is now omitted.
The Corollary remains valid if the word continuity is replaced by continuity
from the left or continuity from the right and the inequality Ix - 21 < 6 by
2 - 6 < x 5 2 or by 2 5 x 5 2 6, respectively.
+
f is continuous
or continuous from the right or continuous from the left at ? i f and
only i f for every xn + 2 or xn + 2, xn 2 2 or 2, + 2, 2, I ?,
respectively, it is true that f(2n) + f(2).
Corollary 12.2.1 (to Definitions 12.1 and 12.2)
The only if part is not entirely obvious since now 2, = 2 for some n
is possible. However the above Corollary can be proved using the Cauchy
definition of continuity and the method of part I1 of the proof of Theorem 12.6. The next Corollary which is a counterpart of Theorem 12.3 is
very useful for an instant recognition of whether or not a given function
Limits and Continuity of Functions
335
is continuous at some point. This helps in graphing functions in Maple.
To obtain a correct graph of a function with a discontinuity use the option
discont=true. The following Maple session illustrates this.
>
>
plot(signum(x-1) ,x=-1. .3);
plot(signum(x-1) ,x=-1. .3,discont=true);
I
Fig. 12.5 Muple plotting discontinuities
Corollary 12.3.1 (to Theorem 12.3)
Iff and g are continuous (continuous from the right, continuous from
the left) at 5 then so are the functions
336
Introduction to Mathematics with Maple
Corollary 12.8.2 (to Theorem 12.8) Let rg f c R and rg g c R.
Iff and g are continuous at 3 and f(2) < g(3) then there exists a
positive A such that
f (4< d-4
when Iz - 31 < A.
Remark 12.15
Important consequences of this Corollary are the cases
g are constant. For instance, if f(2) < A4 or g(2) > 0 then
f(x) < M or g(z) > 0, respectively, for Iz - 31 < A. Similar statements hold
for continuity from the right and from the left, for instance if f is continuous
from the right a t 3 and f(2)< M then there is a A > 0 such that f(x) < A4
for 2 5 x < ? + A .
when f or
A very important tool for establishing continuity is the next theorem
on the continuity of composite functions. It says that a composition of two
continuous functions is continuous. More precisely, we have
If g is continuous at 2 and f is continuous at
Theorem 12.13
ij = g(2) then f o g is continuous at 3.
Proof. Let x, --+ 3. By Corollary 12.2.1 and continuity of g at 3 we
have that y, = g(x,) --+ g(3), by the same Corollary and continuity of
f at ij we also have that y, --+ f ( i j ) . In other words if 3, -+ 3 then
0
f o g(x,) -+ f o g(3). This, by Corollary 12.2.1, proves the theorem.
A polynomial P is continuous everywhere and the square
root function is continuous a t every ij > 0. Hence the function x :H
is continuous a t every 3 for which P ( 2 ) > 0.
Example 12.12
Remark 12.16
,/m
Theorem 12.13 is important and has a simple wording
and a simple proof. In contrast t o theorems about continuity considered so
far it is NOT valid if continuity is replaced by continuity from the right or
left throughout: see Exercise 12.4.7. Also, in contrast t o previously stated
theorems and corollaries it does not translate directly to limits: see Exercise
12.4.8.
Limits and Continuity of Functions
337
Exercises
@ Exercise 12.4.1
I t follows from Theorem 12.3 that a polynomial is
continuous at every point y E R. Give a direct E - &proof. [Hint: P ( x ) P ( y ) = q(x,y)(x - y) with q(z,y) bounded for 1x - yI < 1, say by K . Then,
given E > 0, choose S = e/K.]
@ Exercise 12.4.2
Why are the functions f1, f2 and f3 with f i ( x ) =
x3/1x1, f2(x) = x3/1x1 and f i ( 0 ) = 1, f3(x) = ~1x1all distinct? Show that
if removable singularities are cleared off then all three become equal. [Hint:
All differ at 0 and two functions are equal if and only if they have the same
domain of definition and have the same values for the same x. All three
are equal for x # 0 and f3 is continuous at 0.1
Exercise 12.4.3
Decide which of the followingfunctions have removable
singularities at 0.
Prove the statements in the next three Exercises.
@ Exercise 12.4.4
The function x H xlq(x) is continuous only at 0.
@ Exercise 12.4.5
The function 1~ is discontinuous everywhere. (It is
called the Dirichlet 's function.)
Exercise 12.4.6
Let p , q denote relatively prime positive integers, with
p < q. For x = p / q let f(x) = l/q, f(0) = f(1) = 1 and f(x) = 0 for
all other x E [0, I]. This function is continuous at all irrational points and
discontinuous at all rational points of [0,1]. [Hint: If 2 is rational then by
Exercise 4.6.6 there is a sequence n H xn # Q with xn 4 2 and f ( x n ) = 0.
On the other hand, if 2 is irrational, for any E > 0 there are only finitely
many rational points p / q with l / q 2 E . Consequently, there is a S > 0
such that the set { 2 ;Ix - 21 < S} does not contain any of these points. For
Ix - 21 < 6 either f(x) = 0 or 0 < f(x) = f ( p / q ) < l/q < E.]
Introduction to Mathematics with Maple
338
@ Exercise 12.4.7
Both of the ffoorfunction and g : x H - x 2 are continuous from the right at 0 and a t g(0) = 0 but f o g is not continuous from the
right at 0. Show this. Give an example of F, G continuous from the left a t
G(l) and 1, respectively, such that F o G is not continuous from the left at
1. [Hint: f o g is -1 on [0,1[ and f o g(0) = 0. F ( x ) = 1x1,G ( x ) = 2 - x.]
[email protected]
The direct translation of Theorem 12.13 into limits is
Exercise 12.4.8
false. Show that if lim g(x) = 6 and lim- f (y) = I then lim- f o g(z) need
Y +Y
1
’
2
2
’
2
not be 1. [Hint: f (x)= [xl- 1x1,g(x) = 0, lim f o g ( x ) = 0, lim f (y) = 1.1
x+2
12.5
Y-+O
Continuity
Definition 12.7 A function
interval [a,b] if it is continuous at every point c such that a < c < b,
continuous
This definition can be rephrased by saying that f is continuous on [a,b]
if it is continuous from the left at every point c E ] U , b] and continuous from
the right at every point c E [a,b[.
When drawing a graph of a function f continuous on [a,b] with f ( a ) < 0
and f ( b ) > 0 one has to cross the x-axis or lift the pencil from the paper.
This leads to the following theorem.
-
Theorem 12.14 I f f : [a,b] R is continuous with f ( a ) < 0 and
f ( b ) > 0 then there exists a point c € ] a ,b[ such that f (c) = 0.
Proof.
and
We denote [a,b] by [ao,bo] and consider the intervals [a,
[
a + bT
, b]. I f f
(T)
=
$1
0 the proof ends, otherwise one of the
intervals will have the property that f is negative at the left end point
and positive at the right end point. We denote this interval [ a l ,bl] and
continue the process. Either we find a point c with f(c) = 0 in a finite
number of steps, or we have constructed a sequence of intervals [a,, b,]
b-a
such that [a,, bn] 3 [a,+l, &+I], and b, - a, = -+. 0. By the Nested
2,
Intervals Lemma (Theorem 10.17) there exists a number c such that a, 1 c
Limits and Continuity of Functions
and
bn
339
1c. By construction
and
f (bn) > 0.
(12.11)
By taking limits as n 4 00 in (12.11) and (12.10) we have f(c) 5 0 and
0
f(c) 2 0,and consequently f(c) = 0.
Remark 12.17
Despite its plausibility, Theorem 12.14 does require a proof.
We have t o realize that intuition and the concept of continuity do not fully
agree; for example there are continuous functions which behave so badly they
cannot be graphed. Moreover, Theorem 12.14 has to be proved as a matter of
principle: we are obliged t o prove it from the definition of continuity (and its
established consequences) and not rely on drawings or our feelings.
The idea of the proof can be used to find the point c approximately. If
we want to find c with an error not exceeding E , we find n so large that
bn - an < E and then the approximate value ?i = (an bn)/2 differs from
c by less than E . This can be easily programmed on a computer, and the
method has the advantage that we have good control over the error. See
Example A.3 in the Appendix.
+
+ +
Example 12.13
For f : x H x5
x 1 we have f(-1) = -1 and
f(0) = 1. Since f is continuous by Example 12.8, the equation x5 x 1 has
a solution in the interval [-1,0]. Since f is obviously strictly increasing this is
the only solution.
Example 12.14
Every cubic equation
f(x) = x3 + ax2 + bx + c = o
with a,b,c E R has a t least one real solution. Since
lim ( I + -x
a+ > + x
-
2+--00
there exists k
for
2C)=l,
3
< 0 such that
x 5 k , and consequently
f ( k ) < -k3
1 < 0.
2
+ +
Introduction to Mathematics with Maple
340
Similarly there exists K
> 0 such that
1
f ( K ) > 5K3 > 0.
By Theorem 12.14 there exists c E [k,K ] such that f(c) = 0.
The following generalization of Theorem 12.14 is often used.
Theorem 12.15 (The Intermediate Value Theorem)
Iff : [a,b]I+ R is continuous and a lies between f(a) and f ( b ) then
there exists a point c E [a,b] such that f ( c ) = a.
Proof. If f ( a ) = f ( b ) then a = f ( a ) and the theorem is obvious. If
f ( a ) < f ( b ) we define F : x H f(x) - a. Clearly F is continuous, F ( a ) < 0
and F(b) > 0. Therefore by Theorem 12.14 there exists a number c such
that F(c) = 0; that is, f(c) - a = 0. If f ( a ) > f ( b ) one can apply the
0
already established result on -f.
Example 12.15
The equation
has a solution in [1,2]. Indeed f(1) < 0 and f(2)
value theorem applies with a = 0.
> 0 and the Intermediate
The intermediate value theorem plays an important role in our next
theorem on continuity of the inverse function. We have:
Theorem 12.16
interval I then
Iff is strictly increasing and continuous on an
(i) the inverse f-1 is strictly increasing on f(1);
(ii) f(1)is an interval;
(iii) f-1 is continuous on f(1).
Remark 12.18
The theorem remains valid if the phrase strictly increasing
is replaced everywhere by strictly decreasing.
Y1 = f(f-l(Y1))
2 y2 = f ( f - l ( Y 2 ) ) ,
which is a contradiction. This proves (i).
Limits and Continuity of Functions
34 1
For every n E N let bn = b if b, the right-hand end-point of I , belongs
to I . Otherwise let bn t b if I is bounded from above and bn + 00 if
I is not bounded above. Define a, similarly at the left of I . Then by
the intermediate value theorem each [f(an, f ( b n ) ] is a part of f ( I ) and
moreover
is an interval.
V
/I
I
I
I
I
I
I
I
I
I
I
I
I
I
0
U = X - &
I
X
Fig. 12.6 Continuity of the inverse from the left.
To prove (iii) we denote by ij a point of f ( I ) which is not a left-hand
end point of f ( I ) and ij = f(2), that is 2 = f-l(ij) . For a positive E let
6 = f(2 - e ) - f(2). (See Figure 12.6, which is provided for illustration,
not as a proof.) For @ - 6 < y 5 ij we have, by the monotonicity of f-1,
that
The continuity from the right can be proved similarly.
0
Introduction to Mathematics with Maple
342
By Exercise 5.5.6 the function x I+ x n ,for an odd n E N,
is strictly increasing on R. Obviously it is continuous everywhere. Hence the
inverse function x H x; is continuous everywhere where it is defined, and
that is on R. An alternative notation commonly used is x i = f i . In Maple
it is necessary to use surd(x) , but the other two symbols can have different
meanings in Maple.
Example 12.16
The theorem asserts that f ( I ) is an interval and if I is bounded and
closed then so is f ( 1 ) . However, if I is bounded but not closed, the set
f ( I ) need not be bounded. The function x H 1/x2, x ~ ] 0 , 1has
] as the
inverse y H l/a,
y E [l,m[. The original function had bounded domain
but the domain of the inverse was unbounded. Exercise 12.5.5 shows the
importance of the assumption that the domain o f f was an interval.
The next few theorems extend local properties of functions to global
properties. Typical of these theorems is Theorem 12.18, proof of which
establishes boundedness of the function, a global property, from continuity
of the function at every point, a local property. An important tool for the
proofs is the Cousin Lemma below.
A finite sequence of points t k , k = 0 , 1 , . . . ,n forms a division T of an
interval [a,b] if
u = to
The intervals
[ t k , t1,+1]
< ti < t 2 < - . . < t ,
= b.
(12.14)
are called subintervals of the division. If there is an
[ t k - l , t ~ ,for
] k = 1,. . . ,n then we call
X I , in every subinterval
a tagged division of [a,b]. The points XI, are called tags, and X I , tags the
interval [tk-l ,t k ] . We shall denote the tagged division 12.15 with dividing
points T = { t o , t l , . . . , t n } and tags X = { X l , X 2,..., X,} by T X . A
function is called a gauge if it is positive everywhere where it is defined. If
6 is a gauge on [a,b] then the tagged division T X is called &fine if
X I , - XI,) < tlc-1 < t k < Xk
+6(Xk)
for all
k = 1 , 2 , . . . ,n. (12.16)
In proofs, a gauge is often used to control the size of subintervals of a tagged
division. Of course, any other letter can be used in place of 6 but use of this
letter is traditional and a gauge usually enters a proof from some related
E-6 definition. We are now ready for4
4Lemma means an auxiliary theorem. History of mathematics knows a number of
examples where a lemma becomes so important that it gains a status of a Theorem.
343
Limits and Continuity of Functions
Theorem 12.17 (The Cousin Lemma) For every gauge 6 there
is a 6-fine tagged division of a closed bounded interval [a,b].
Proof. We proceed indirectly. If there is no 6-fine tagged division of [a,b]
then there is no 6-fine tagged division of one of the intervals [a,( a b)/2],
[(a b)/2, b]. If it were then merging these two tagged divisions together
would define a 6-fine tagged division of [a, b]. Denote this interval [al,bl]
and continue the process indefinitely. We obtain a sequence of nested closed
bounded intervals [a,,b,] and none has a &-fine tagged division. Since
b, - a, = (b - a)/2-, --+ 0 there is a point c E [a,, b,] for every N. For
E = S(c) > 0 there is rn E N such that b, - a, < S(c). Consequently
+
+
c - S(C) < a,
< b, < c + 6 ( ~ ) .
This means that a , 5 c 5 b, is a &fine tagged division of [a,, b,],
0
contradicting the definition of [a,, b,] .
Cousin’s Lemma looks obvious, but see Exercise 12.5.6. The first application of Cousin’s Lemma is:
Theorem 12.18 (Continuous functions are bounded) I f f is
continuous on a closed and bounded interval [a,b] then the function f
is bounded on [a,b].
If1
is continuous on [a,b]. By
Remark 12.15 there is a positive 6 such that lf(x)I < If(X)l 1 for
Ix - XI < 6. Let T X be a &fine tagged division 12.15 and A4 =
1 Max(f(X1), f(X2), . . . f(X,)). If x E [a,b] then for some natural k
the point x lies in the subinterval [ t k - l , t k ] and consequently Ix - X k I < 6.
It follows that
Proof. By Corollary 12.3.1 the function
+
+
We emphasized that [a,b]was bounded and closed. The importance of
this assumption is illuminated in Exercise 12.5.7. The next theorem says
that a continuous f attains its maximum and minimum on [a,b].
This is so here.
Introduction to Mathematics with Maple
344
Theorem 12.19 (Weierstrass on extreme values) I f f is continuous on [a,b] then there exist c, d in this interval such that
f(c) I
f(x) L f ( 4
for all x in [a,b].
Proof. We denote by m = inf {f(x);x E [a,b ] ) . For every natural n there
is a point x, such that f(xn) < m l/n. By the Bolzano-Weierstrass
Theorem there is a convergent subsequence, say xn, + c as k + 00.
By Corollary 12.2.1 we obtain f(xnk) + f(c) and by Theorem 12.4 that
f(x,,) --+ m. Hence f(c) = m, and by Definition of the greatest lower
bound f(x) 2 m = f(c). The proof for d is similar or one can apply the
already established part of the theorem to -f.
0
+
This Weierstrass Theorem is theoretical in its character. However we
shall see in the next chapter that the mere knowledge of the existence of,
say, a maximum value, enables to find this extremum.
The next theorem shows that continuous functions can be well approximated by some simple functions. We shall call a function Z piecewise linear
on [a,b] if there is a division D such that 1 is linear on all intervals [xk-l,xk]
for k = 1 , 2 , . . . x,. 5
Theorem 12.20 I f f : [a, b] H R is continuous on a bounded and
closed interval [a,b] then for every positive E there exists a piecewise
linear function I such that, for all x E [a,b],
If(.)
Proof.
).(fI
- l(x)I
< E.
By continuity, for every X E [a,b] we find S = S ( X ) such that
< ~ / for
2 Ix -XI < S ( X ) . Let
- f(X)l
be a 6-fine tagged division of [a, b] and I the piecewise linear function which
agrees with f at all the points xk (see Figure 12.7). If x E [xz-l,xk]then
f(x) lies in the interval J k = ] f ( X k )- ~ / 2 f(Xk)
,
~ / 2 and
[
so does Z(x)
because it lies between f(xk) and f(xk+l). Consequently If(.)
- Z(x)I is
0
less then the length of J k , which is E .
+
51t is a consequence of the definition that a piecewise linear function 2 is continuous
on [a,b].
345
Limits and Continuity of Functions
Y
If
I
(xk-1)
i
I
I
I
f (xk+l)
I
I
I
I
0
xk-1
xk
xk+l
2
Fig. 12.7 A detail of a piecewise linear approximation.
A graph of a linear approximation to a function is depicted in Figure
12.8. It was made with Maple: for making such graphs see Exercise 12.5.11.
Fig. 12.8 Piecewise linear approximation
The theorem has a practical application in Maple. When making a plot
of a continuous function, the picture on the screen is a graph of a linear
approximation to the function. It has a large number of subintervals, so
large that the eye sees a continuous curve rather than a polygonal line.
If f is continuous at a point y E [a,b] then for every E > 0 there exists
Introduction to Mathematics with Maple
346
a
S > 0 such that
This S depends generally speaking on y (and also E). For instance for
the function f : x H x2 a suitable 6 satisfying the implication 12.17 is
60 =
- Iyl. This is easy to prove.
d w
The implication 12.17 does not determine 6 uniquely: any positive number
smaller than 60 can be used as a S in 12.17. In particular
E
S1 = 2 d W
&
&qF+
= 60
Iyl
Now if y is restricted to the interval [0,1] then
61 L E / ( 2 d X ) = 6
and so this 6 can be used in implication (12.17). We have found something
important: 6 is the same for all y. As a consequence, with S = 6 the
implication 12.17 holds for all y in [0, 1].6
12.8 We shall say that a function f is
a set S if for every positive E there exists a positive S such
12.17 holds for all x , y E S.
T h e S in Definition 12.8 may (and usually does) depend
Remark 12.19
on E , but does not depend on x (or y).
Theorem 12.21 (Uniform continuity) A function continuous on
a closed bounded interval K is uniformly continuous on K .
Proof.
By continuity of f at X for every q there exists &(X)such that
It - XI < 6. Then
If(t)- f ( X ) l < q/2 for
(lu - XI
< S1 and Iw - X I < 6,)
+ If(u) - f(w)I
< q.
Let 62 be chosen so that the above implication holds for 61 and q replaced
by 6 2 and &/2, respectively. By Cousin’s Lemma there is a tagged division
“Not
merely for one particular y as before.
Limits and Continuity of Functions
347
T X which is &-fine. Define
1
6 = -Min(&(Xk); k = 1,.. .n)
2
If Iu-wI
of 6
< 6 and u, w belongs to the same subinterval of T then by definition
If(.)
- f(4I <
&
5 < E-
If u, w do not belong to the same subinterval of T then the smaller one, say
u, belongs to [ t k - l , t k ] for some k and w E [ t k , t k + l ] and then
Conceptually and computationally, polynomials are the simplest functions. The next theorem, very important from a theoretical point of view
but also with some practical applications, says that continuous functions
are close to polynomials.
Theorem 12.22 (Weierstrass theorem on polynomial approximation) I f f : [a, b] H R is continuous and E > 0 then there exists
a polynomial P such that
If(.>
-
W I < ?5
(12.18)
for all x E [a, b].
The main idea of the proof consists in showing that if f can be approximated by a polynomial on some interval that it can also be approximated
by a polynomial on a slightly larger interval. For the proof we also need
a polynomial approximation U of a function which is 1 on a part of the
interval and 0 on another part of [a, b]. We state this as a lemma.
Lemma 12.1 I f a < c - Ic
a polynomial U such that
< c 5 b then for every q~ > 0 there exists
l - ~ < U ( x ) < l for a L x < c - k
(12.19)
O L U ( x ) < l for c - k < x < c
(12.20)
5 U ( z ) < v for c 5 x 5 b
(12.21)
0
Proof. We denote I = b - a and d = c - k/2. First we find a polynomial
p which is between 0 and 1/2 on [a, d] and between 1/2 and 1 on [d, b].
Introduction to Mathematics with Maple
348
This is easy
p(x) =
51 + z1( x - d).
Then we define
U ( 4 = (1- [P(41n)2n
with some n E N which we choose suitably later. Obviously
(12.22)
0 5 U ( x )5 1
for a I x I b. Employing the Bernoulli inequality, from Equation (5.36),
where n is replaced by 2n, gives
U(J:)
for a 5 x
1 - [2p(z)ln L 1 - [2P(C - k)ln
c - k. On the other hand we have for c 5 x
(12.23)
Ib
Since both 2p(c-k) < 1 and 2p(c) > 1 it follows that both [2p(c-k)ln
and [ 2 p ( ~ ) ] ---+
~ 0. We can therefore find n E N such that
+=
0
1
Equations (12.19), (12.20) and (12.21) now follow from Equations (12.22),
(12.23) and (12.24), respectively.
0
We now proceed with the proof proper of the Weierstrass theorem.
For a given E > 0 let S, be the set of all t
exists a polynomial P, with the property that
Proof.
5 b such that there
for a 5 J: 5 t. By continuity of f at a there exists t o such that
for a 5 x 5 to. Consequently f can be approximated by a constant f ( a )
on [a,to] and S, # 8. Let s = sup S,. Clearly a < s 5 b. By continuity of
349
Limits and Continuity of Functions
f at s there is S > 0 such that
(12.26)
+
for s - S 5 x 5 s 6 and x 5 b. By the definition of the least upper bound
there is a c with s - 6 < c 5 s and s E SE.This means there is a polynomial
P, satisfying Equation (12.25) for a 5 z 5 c. Let
m = Max{lf(z) - PE(x)(;a
5z
5 c}
and M so large that
for all x E [a, b]. We apply the lemma for c - k = s - 6 to find the function
U with 0 < 7 < 1so small that
(12.27)
2E
Mq<-
3
(12.28)
This is possible since m < E . Now we define
and show that it satisfies (12.18) on [a,b ] . First we have
It follows from (12.19) and (12.27) that on the interval [a,s - S]
On the interval [ s - S, c] clearly
).(fI
- P(.)I
L E U ( Z ) + 3 (1- U ( X ) ) < E .
&
Finally, using (12.21), (12.26) and (12.28) we have
+
on [c,s 61 n [c,b]. This proves that s = b, otherwise the inequality (12.18)
would hold on [a,s a], contrary to the definition of s. Hence Equation (12.18) holds on [a, s] = [a, b] and the proof is complete.
0
+
Introduction to Mathematics with Maple
350
A function f is said to be Lipschitz on the interval [a,b] if there exists
a constant L such that, for all x , y E [u,b],
A Lipschitz function is continuous but the property of being Lipschitz is
stronger: Lipschitz functions behave even better than continuous functions.
The geometrical meaning is illustrated in Figure 12.29: the graph of f lies
within the angle formed by lines of slopes L and -L with vertex at (u,
f(u)).
It is also important to realize that this happens for every u E [a,b].
Y
-L
0
X
Fig. 12.9 Geometrical illustration of a Lipschitz function
For instance, if f(z) = ,/Z then
(12.30)
Consequently, for y 2 a, this function is Lipschitz with L = l/@. We
leave it as Exercise 12.5.12 to show that this function is not Lipschitz on
[O, 13.
Every polynomial is Lipschitz on any bounded interval. Indeed
The expression for q ( ~ , y is
) obtained by the long division algorithm from
Limits and Continuity of finctions
351
the coefficients of P and from x , y by addition and multiplication, so if x
and y are restricted to a bounded interval q is bounded, say by L, and then
As an application of the Weierstrass Theorem we now give a second
proof of the theorem on uniform continuity.
Proof. For given positive E we find, by the Weierstrass approximation
theorem a polynomial P such that for all x E K
If(.>- P(4I <
5.
Using Inequality (12.32) we have
&
Fkom this inequality it is clear that implication 12.17 is satisfied for S = 3L
0
and all x , y E K .
Exercises
Exercise 12.5.1
Show that the equation
199
x17
+
1
+ 2 4 + saw(x) = 200
has a solution.
@ Exercise 12.5.2
Prove that a polynomial with real coefficients and of
odd degree has a real root.
+
+ +
Let P ( x ) = xn u1zn-' - - - an. If the coefficients
of P are real and an < 0 then P has a positive root. Prove it.
@ Exercise 12.5.3
@ Exercise 12.5.4
Prove the following. I f f is a continuous bijection of
[a,b] onto [c,d] then f is either strictly increasing or strictly decreasing
on [a,b]. Therefore the assumption that f is strictly increasing in Theorem 12.16 can be replaced by a more general assumption that f is oneto-one. [Hint: Use an indirect proof. If q < 2 2 with f(q)
< f(x2)
and x3 < x4 with f(x3) > f ( ~ 4 ) use the auxiliary function F : t H
f(x1
t(x3 - 21)) - f(x2 t(z4 - x z ) ) , t E [0, I], and Theorem 12.15.1
+
+
Introduction to Mathematics with Maple
352
@ @ Exercise 12.5.5
Prove the following. The assumption that dom f is
an interval in Theorem 12.16 is innocuous but essential. Let f(x) = x for
x < 0 and f(x) = x - 1 for x 2 1 then the inverse f-1 is discontinuous at
0. [Hint:f-l(y) = y for y < 0 and f-l(y) = y 1 for y 2 0.1
+
@ @ Exercise 12.5.6
Prove the following. For d ( x ) = x there is no d-fine
tagged division of [0, 11. [Hint: If there was then X I -0 < d(X1) = XI L x1 .]
@ Exercise 12.5.7
The function x H 112,x E]O,1] is not bounded. The
id function is continuous on the closed interval R and is not bounded.
Reconcile this with Theorem 12.18.
@ Exercise 12.5.8
Give an example of a function continuous on all of R
which does not have a maximum or minimum value. [Hint: id.]
@ Exercise 12.5.9
If P is a polynomial show that the function I
PI attains
its minimum on R. [Hint: Since I
PI -+ 00 for x + zkco there is a finite
closed interval I = [-a,a] such that IP(x)I > IP(0)I for x # I. The
minimum on I is the minimum on R.]
@ Exercise 12.5.10
lim f(x) =
1
’
0
0
Suppose that f(a) > 0 for some a E R and
lim f(x) = 0. Prove that f attains its maximum on R.
x4-00
[Hint: There is a closed bounded interval I such that f(x) < f ( a ) outside
I . The maximum on I is the maximum on R.]
@ Exercise 12.5.11 Given two lists X = [x~,x2,...,xn]
and Y =
[yl, y2, . . . ,yn] with xk strictly increasing, the piecewise linear function
which takes the value yk at xk is called a h e a r spline. It can be
made by the Maple command s p l i n e (X,Y, t , l i n e a r ) . Here, t indicates the variable, and the option l i n e a r is needed, as there are other
splines of higher degree. We shall encounter some in the next chapter.
Produce a graph of sin together with a linear spline approximation for
X := [0,Pi/&2 * Pi/3,Pi,3 * Pi/2,2* Pi].
Show that the function x H fi is not Lipschitz on
the interval [0,1]. [Hint: Indirect proof. If it were then fi 5 L x for
0 5 x 5 1, a contradiction.]
@ Exercise 12.5.12
Exercise 12.5.13 Show directly that x H 21x1 is uniformly continuous
on R with 6 from implication 12.17 equal to ~ / 2 .
Exercise 12.5.14
Show that x H 1/x and x H x2 are not uniformly
continuous on the intervals]O,l[ and [O,co[,respectively. [Hint: The proofs
Limits and Continuity of Functions
are indirect. For
respectively.]
12.6
E
= 1 let x = 6, y = 6/2 and x = 1/6, y = x
353
+ 6/2,
Comments and supplements
Similarly to sequences, there are lim sup and lim inf for functions. There are
six of these ‘limits’: from the left, from the right and two-sided. We state
the definition for the limit superior from the left and leave it to our readers
to formulate by analogy the remaining five. Assume that f : ] u , b [R~is
bounded.
The function M is decreasing and bounded, since f is bounded. Consequently it has a limit from the right at 0 and this limit is, by definition, the
limit superior from the left of f at b. In symbols,
lirn sup f (z) = lim M (6).
zTb
610
The notation for the other five limits is (with c ~ ] a , b [ ) :
lirn sup f(z),
xla
lim sup f (z),
x+c
lim inf f (x),
x+c
liminf f (x),
XTb
lirn inf f (x).
xla
To illustrate these limits consider the function
1
f(z) : z H 2(sgn(z) + -) saw(l/z).
2
Introduction to Mathematics with Maple
354
It plausible that
3
limsup f(x) = XlO
2’
lim sup f (x)= 0,
xcto
1
lim inf f (z) = - XTO
2’
liminf f(x) = 0.
210
Similarly as with sequences, the limit exists if and only if the limit
superior and inferior are equal and that is so also when the limit is taken
from the right or left. It is not difficult to prove that
,
limsupf(x) = Max
x+c
liminf f(x) = Min
x+c
f(z),liminf f(z)
xtc
Limit superior and limit inferior make sense only for real valued functions. In contrast, the Bolzano-Cauchy theorem applies to complex valued
functions and also for limits in the complex domain.
Theorem 12.23 (Bolzano-Cauchy) The function f has a limit
from the left at b if and only if for every E > 0 there is a positive 6
such that
whenever b - S < u
< b and b - S < v < b.
Proof. If f has a limit from the left, say I , then for every positive E there
is a positive 6 such that, for all x with b - S < x < b,
Taking x to be u and also v in the above inequality we have
E
E
If(.>- f(4I L If(4 - ZI + If(4 - 4 < 5 + 5’
whenever b- S < u < b and b-6 < v < b. Turning to the second part of the
proof, let xn 3 b and xn < b. There is a N such that b - S < xn for n > N .
This, together with Inequality (12.34), implies that If(xn) - f(xm)l < E ,
Limits and Continuity of Functions
355
for n,m > N . By the Bolzano-Cauchy theorem for sequences 10.16, this
implies that the sequence n I-+ f ( z n ) is convergent, say to 1. It remains to
be shown that if yn -+ b and yn < b then also f(y,) --+ 1. By what we have
already proved the sequence with terms
also converges. Its limit is I since for the subsequence f ( z n ) --+ 1. Every
0
subsequence has the same limit, hence f(yn) -+ 1.
Remark 12.20
Similarly as with sequences, the Bolzano-Cauchy theorem
makes it possible t o prove that a function has a limit without the actual knowledge of what the value of this limit is. The Bolzano-Cauchy theorem holds
also for the limit from the right, limit and limit in the complex domain. The
inequalities b-6 < u < b and b-6 < w < b must be replaced by a < u < a+6
and a < w < a 6, or 0 < Ic - uI < 6 and 0 < Ic - wl < 6, respectively.
For the limit in the complex domain the last inequalities are then required for
complex u, w. For limit of f a t 00, the Bolzano-Cauchy theorem reads: a finite
limit of f a t 00 exists if and only if for every positive E there exists K such
that u,w > K imply If(u)- f(w)I < E .
+
The fact that monotonic functions have limits from the left and from
the right has an interesting consequence, namely, a monotonic function
can be discontinuous only at countably many points. For an increasing
and bounded function f we denote, for brevity, by f(c-) and f(c+) the
limits from the left and from the right, respectively. If f is discontinuous
at c then f(c-) < f(c+). If c,d are points of discontinuity of f then the
open intervals ] f ( c - ) , f ( c + ) [ and ]f(d-), f(d+)[ are disjoint. Indeed, if,
for instance, c < d, then f(c+) 5 f(d-). Now we can associate with every
point c of discontinuity a rational point r(c) ~ ] f ( c - ) f(c+)[.
,
This mapping
is one to one: for distinct values of c, the corresponding values of r(c) are
also distinct, because they lie in distinct intervals. So r is a bijection of the
set of discontinuities onto a subset of rationals and hence the assertion.
The function f is said to have the intermediate value property, or the
Darboux property, on the interval [a,@]
if it attains every value between
f(a)and f(P) somewhere in the interval. We have proved in Theorem 12.15
that a continuous function has the intermediate value property. The Darboux property was in the past confused with continuity but it does not
characterize continuity. The function f equal to saw(l/z) for z # 0 and
f ( 0 ) = 0 has the intermediate value property on every subinterval of [-1, I]
but is discontinuous at 0. There is a more dramatic example in Boas (1972,
Introduction to Mathematics with Maple
356
p. 71) of a function which has the intermediate value property on every
subinterval of [0,1] but is discontinuous at every point of [O,11.
Mathematicians of the 17th and 18th centuries had an idea of continuity but were unable to formulate it precisely. This was done by Bolzano in
1817 in a paper whose main purpose was to prove the intermediate value
property for continuous functions. The Bolzano definition was what we
called the Cauchy definition, following custom rather than historical accuracy. Bolzano’s paper is remarkable. Firstly he set out his goal very
clearly: he wanted to prove the theorem, as he said, scientifically. By that
he meant a proof, which used only the definition and logical means, but
did not contain any reference to intuition, geometry or the concept of motion. Secondly, in the course of his proof, Bolzano established the greatest
lower bound theorem and what we called the Bolzano-Cauchy theorem for
sequences. Bolzano was not only a mathematician but a moral philosopher
and was politically persecuted. He was prevented from publishing some important results which were discovered much later by other mathematicians,
in particular by Weierstrass.
Exercises
[email protected]
This exercise outlines the idea of Bolzano’sproof of the
Exercise 12.6.1
intermediate value theorem. Let f : [a,b] H R with f(a) < 0 and f ( b ) > 0.
Denote S the set of all x E [a,b] for which f(x) > 0. Show that
1. t = inf S exists and is in ]a,b [ .
2. f(<) cannot be positive.
3. f([)cannot be negative.
Chapter 13
Derivatives
Derivative can be described informally as a rate of change and as
such it is extremely important in Science and applications. In this
chapter we introduce derivatives as limits, establish their properties and use them in studying deeper properties of functions and
their graphs. We also extend the Taylor Theorem from polynomials to power series and explore it for applications (within mathematics).
13.1 Introduction
Let us think of a body moving along a straight line. Let the distance of
this body from a fixed point be a known function of time, say f . During
the time interval [t,t h] the body travels the distance f ( t h ) - f ( t )and
the average velocity will be
+
+
f(t + h ) - f(t)
h
The velocity shown on the speedometer of a car or a plane is the instantaneous velocity and for our moving body this is the limit of the above
expression as h -+ 0.
Consider another example, this time from geometry; see Figure 13.1.
The secant line joining the points (z, f(z))and (z h, f(z h ) ) has the
slope
+
+
+
As h -+ 0 the points (z, f(z))and (z h, f(z+ h ) )coalesce and the secant
becomes a tangent. This corresponds to moving the ruler aligned along
the secant line carefully so that the point (z h, f ( z h ) ) moves towards
+
357
+
Introduction to Mathematics with Maple
358
(x,f(x)) until it reaches its limit position, and then the secant becomes a
tangent.
0
x+h
Fig. 13.1 Secant becoming a tangent
Y
(Definition
13.1
The number
(13.1)
(is called the derivative of f at x. It is denoted by f'(x) or
Df(x).
Geometrically, if the tangent forms an angle $ with the x-axis then
f'(x) = tan$.
Example 13.1 The derivative of a constant function is 0 at any point,
(c)' = 0. Also Dx = (2)' = 1. For the quadratic function we have Ox2=
lim(2x+ h) = 22. Using the binomial theorem it is easy to show that Dxn =
h+O
nxnV1for n E N. Indeed,
359
Derivatives
As h + 0 the second term on the right hand side tends to 0.
For x # 0 we have
Remark 13.1 The notation f’(x) is more common, and Maple also uses
D ( f ) ( x )rather than D f ( x ) . If f ( x ) is abbreviated as y or u then f ’ ( x ) is
denoted by y’ or u’.The derivative Of(.) is sometimes calculated according
to Equation (13.1) as
D f ( a ) = lim
f (4- f (4
x-a
x+a
The phrases ‘f has a derivative a t x’ and ‘f is differentiable a t x’ are used
interchangeably. The limit in Equation (13.1) can be understood as a limit in
the complex domain or in the real domain. These two concepts are distinct!
Usually it is clear from the context which derivative is meant. In the previous
example, however, all the formulae are valid for the derivative in the complex
domain and then of course also for the derivative in the real domain. If the
limit in Equation (13.1) is replaced by the limit from the left or right then
Definition 13.1 becomes definition of the derivative of f from the left or right,
and fL(x).
respectively. The derivative from the right is denoted by f:(x),
denotes the derivative from the left. Both one-sided derivatives, the derivative
from the left and the derivative from the right, make sense only as derivatives
in the real domain.
Example 13.2 The function f : x
right 1, and from the left -1. Indeed
I-+
1x1 has a t zero derivative from the
lim-lhl -0 = lim(-1) = -1.
hTo
h
hTO
This example shows that a continuous function need not have a derivative.
Geometric intuition might suggest that a function continuous on some interval
would have derivative a t many points of the interval. This is not true: Bolzano
was aware of this and later Weierstrass published an example of a function
continuous everywhere and differentiable nowhere. We shall comment on this
in the last section of this chapter.
Remark 13.2 The limit in Equation (13.1) can be infinite, and the derivative is then called an infinite derivative. In this book the word derivative means
Introduction to Mathematics with Maple
360
a finite derivative and if we allow the derivative to become infinite, we shall explicitly say so. Moreover, we shall consider infinite derivatives only for functions
for which the domain of definition is a part of R.
Example 13.3
Let f ( x ) = &, g ( x ) =
V G .
Then
We introduce yet another notation for the derivative: we denote f ’ ( x ) by
d
d f ( x ) . The original notation is shorter and on most occasions
- f ( x ) or by dx
dx
unambiguous, but this latest notation has the advantage of indicating with
respect to which variable the derivative is taken. For instance, it is clear
from the notation d ( x 2 txc>that t should be regarded as a constant and
dx
x as a variable, that is,
+
+
d(x2 t x )
(x
= lim
h+O
dx
+ h)2+ t ( x + h)
h
- x2
-tx
=2x+t.
dY
Naturally if f ( x ) is denoted by y then f ’ ( x ) = y’ = - Each notation has
dx ’
its advantages and disadvantages. The symbol y‘ hides the x, so it cannot
be used for denoting, for example, f’(1). Although dY does not hide x , it is
dx
also unsuitable for denoting the derivative at a particular point, say II: = 1.
We now consider some differentiation with Maple.
>
restart;
f :=x+x2+1
22- 22
1
1
X
Derivatives
36 1
The command diff can also be used to calculate the derivative of a
function with values f(x).
1
22- 22
>
g: =x->x*abs (x>;
Error, (in simpl/abs) abs is not differentiable at 0
It is true that x H 1x1 is not differentiable at 0 nevertheless g is differentiable at zero. Maple is unfortunately unable to calculate Dg(0). However
Maple can find the derivative as a limit
0
The main advantage of using Maple for differentiation lies in the fact
that Muple easily calculates derivatives which otherwise would be laborious
to do, as we shall see later. Here is an example which would not be too
difficult without Maple, but using Maple still has an advantage.
>
d i f f ( ( l+x-xA2)/ ( 1-x+xn2) ,x) ;
1-22
1-x+x2
>
+
- (1 x
+
x2) (-1 2 2)
(1 - x x2)2
-
+
simplify(%);
-2
+
+
-1 2 2
(1 - x x2)2
Exercises
Exercise 13.1.1
derivative I/(Z,/Z).
Prove that the function x
w
& has, for all x > 0, the
362
Introduction to Mathematics with Maple
Exercise 13.1.2
1
Show that D-Xn = -n- p z + l for z
+ o and n f N.
[Hint: Use equation (13.1) and the result from Example 13.1, namely that
Dxn = nxn-l.]
Use Maple to find D(1+ &)/(l - &).
Exercise 13.1.3
13.2
Basic theorems on derivatives
If f is differentiable at z then the function
h H D(h) = f ( x + h) - f ( 4
h
(13.3)
has a removable discontinuity at 0 and by defining D(0) = f’(z)becomes
continuous. This leads to the next theorem, which is theoretical in character
but needed at many practical occasions.
Theorem 13.1
The function f is differentiable at z if and only if
there exists a function D continuous at zero such that Equation (13.3)
holds. If so then D(0) = f’(x).
Remark 13.3
domain.
This theorem as well the next two are valid in the complex
From this theorem it follows immediately:
Theorem 13.2 If a function has a finite derivative at a point then
the function is continuous at this point.
Proof. As h -+ 0, Equation (13.3) yields f(z
so f is continuous at x.
/
Theorem 13.3
+ h ) - f(z)-+
D(0)O = 0
0
If f’(x) and g’(z) exist and c is a constant then
[cf(z)l’ = C f ’ b ) ,
[ f ( 4 + g(z)l’ = f’(.)
ig’W7
I/
(13.4)
(13.5)
Derivatives
363
These formulae are more easily remembered as
(cu)’ = CU‘,
+
+ v’,
(uv)’ = u’v + UV’.
(u v)‘ = u’
Proof. The Formulae (13.4) and (13.5) are immediate consequences of
theorems on limits and Definition 13.1. It is important to note that
[ - f ( x ) ] ’ = -f’(3;) follows from Equation (13.4) and then
by Equation (13.5). Let us now turn to Equation (13.6). By Theorem 13.1
+
+
f(z h) = f(z) Dl(h)h,
g ( x h) = g(x) D2(h)h.
+
(13.8)
+
(13.9)
= (f(.)az(h)
+ 9(4Dl(h) + w w 2 ( h > h >h-
Consequently
+
+
f(z h)g(z h) - f(.>g(.>
(13.10)
Dividing by h and then passing to the limit as h --+ 0 leads to 13.6, since
Dl(0) = f’(x) and D2(0) = g’(x).
0
This Theorem together with the formula Dxn = nxn-l
makes i t clear that the definition of the derivative of a polynomial as given in
Equation (6.25) and the definition of a polynomial according to Definition 13.1
do agree.
Remark 13.4
Exercises
@ Exercise 13.2.1
@
Prove by induction:
+
Exercise 13.2.2
Show that f g and f g might have derivatives even
though neither f nor g has. (Hint: f(z) = 1x1,g(x) =1. -1
for x = 0.1
@ Exercise 13.2.3
The function x
sgn(z) has an infinite derivative +oo
at 0 but is discontinuous at 0. Does this contradict Theorem 13.2 and if
not why not.
364
Introduction to Mathematics with Maple
The chain rule
The next theorem concerning the derivative of a composite function is one
of the most important rules for differentiation.
Theorem 13.4 (The chain rule) If g is differentiable at x and f
differentiable at g(z) then f o g is differentiable at x and
(f (9(4)>‘
= f ’ b(4>9’(4
( 13.11)
The equation is often rewritten in the following way with y = f(z)and
u = g(x>
(13.13)
dy
du
The symbols - and - are indivisible but still Equation (13.13) is easily
du
dx
remembered as fractions which cancel. Before we prove the theorem we
illustrate it by two examples.
+
We wish to calculate D ( l x2)20. We can expand the
expression by the binomial theorem and then take the derivative. Far more
convenient is t o use Theorem 13.4, set u = 1 x 2 and y = f(u) = u2’. Then
by Equation (13.13),
Example 13.4
+
-du_ - 22,
dx
+
dY = 2 0 ~ =~4oz(i
~ 2 x2)19.
~
dx
In Example 13.1 we established the formula (xn)’ = nxn-l for a positive
integer n. We now extend the formula for negative integers.
Example 13.5
Set m = -n then m is positive and
Clearly this is valid for x
# 0.
365
Derivatives
We now take up the proof of Theorem 13.4.
Proof.
By Theorem 13.1
(13.14)
(13.15)
with D1 and D2 continuous at 0, Dl(0)= f'(u)and D2(0) = g'(x). Setting
u = g(x) and k = g(x h) - g(x) leads to
+
(13.16)
Since k + 0 as h -+ 0, passing to the limit in Equation (13.16) as h
gives Equation 13.11.
+0
0
Example 13.6
Sometimes the chain rule needs t o be applied t o the inner
function. This happens when the function is multiply composed. We wish
to find
(x+
d m ) 5 . Let
us recall from Exercise 13.1.1 that
(&)I
=
1/(2&).
Firstly, by Theorem 13.4
D&TF =
1
21/iT72x
and then
If f(u)= 1/u then the chain rule leads to
Applying this and Equation (13.6) from Theorem 13.3 to the product
1
Theorem 13.5
I f f and g have derivatives a t x and g'(z)
# 0 then
(13.17)
Introduction to Mathematics with Maple
366
This equation can be rewritten as
(t>’ u’v
=
- uv’
212
-
Exercises
Exercise 13.2.4 Use theorems of this section to calculate the following
derivatives and then check your results by Maple.
1.
3.
(=)I;
(
3x+7
)I.
x + 1)2 ’
(22 +
@ Exercise 13.2.5
+
Prove that (f(aa: b))’ = af’(ax
+ b).
[Hint: Use
Theorem 13.4.1
@ Exercise 13.2.6
Theorem 13.4 is not valid if the derivative is replaced
by right-hand (or left-hand) derivative. Give an example of this. [Hint:
g(x) = -2, f(x) = 1x1 at x = 0.1
@ Exercise 13.2.7
Prove that Theorem 13.4 remains valid if the derivative
is replaced by right-hand (or left-hand) derivative, provided g is strictly
increasing (decreasing).
Derivative of the inverse function
Now we study the derivative of the inverse function. We consider a function f strictly monotonic on some interval I. Figure 13.2 provides a good
motivation.
The tangent to the graph of f at (x,f(x)) forms an acute angle a with
the x-axis. Hence the tangent to the graph of f-1 at (f(x),x) = (y, f-l(y))
subtends an angle a with the y-axis and the angle 7r/2 - a with the z-axis.
It follows that
4
367
Derivatives
Fig. 13.2 Derivative of the inverse
We state this as
%(
1 Theorem 13.6 (Derivative of the inverse function)
I f f is
strictly monotonic and f has a derivative f’(x) # 0 a t an interior
point x of the interval I then the inverse function has the derivative
fLl(y) at the point y = f(z)and
1
fXY)=f ‘b)
1)
(13.18)
*
The above formula is easily remembered as
dY-- 1
dx
dx
dY
with the understanding that y denotes f(x).
(13.19)
Introduction to Mathematics with Maple
368
+
h) - f-l(y). By the Theorem on the
Proof. Define k ( h ) = f-l(y
continuity of the inverse, k is continuous a t 0 with k ( 0 ) = 0. Denote
y = f(x) then x = f-l(y) and we obtain successively:
f-l(Y + h) = x + k ( h ) ,
Y +h =f ( x
(13.20)
+W)),
(13.21)
+
(13.22)
h = f(z k ( h ) )- f ( x ) .
By Theorem 13.1
with V continuous at 0. Substituting for h from Equation (13.22) gives
(13.24)
Using Equation (13.20) for k ( h ) leads to
0
Taking limit as h -+ 0 proves Equation (13.18).
A more detailed and careful examination of the proof will show that if
f’(x) = 0 then f i l ( y ) = f o o with the sign valid for a strictly increasing
f and - sign for a strictly decreasing f .
+
Example 13.7 Let f ( x ) = xm with m E N. Then f is increasing, for m
odd on all of R and for m even on [ O , o o [ . The inverse’ f-l(y) = yl/m has
the derivative
1
1
dyh fl_,(y) = - ---yn
dy
mxm--l
m
1-1
.
(13.25)
We know t h a t the formula (xn)’ = nxn-l holds for n E Z. Equation (13.25)
extends it t o n of the form l/rn with m E N. We now use the chain rule t o
extend it for any rational m = p / q , p E Z and q E N.
(
(x”)‘ = (x”p)’
‘For even m we tacitly understand that f is restricted to [0,m[.
Derivatives
369
Exercises
@ Exercise 13.2.8
The validity of Theorem 13.6 can be extended to endpoints of I = [a,b]. 14 for instance, f is strictly increasing and f $ ( a ) # 0,
then (f-1); exists at c = f ( a ) and
+
Prove it. [Hint: Define F ( z ) = f(z)for z 2 a and F ( z ) = f ( a ) f:(a)(z a ) for x < a. Show that Theorem 13.6 can be applied to F and prove
(f-1):
(4= P-d;
(c1.1
@ Exercise 13.2.9
Prove: I f f is strictly increasing on I and f'(c) = +oo
for an interior point c E I then for d = f(c) the derivative fLl(d) = 0
13.3
Significance of the sign of derivative.
If a function has a positive derivative then the tangent forms an acute angle
with the z-axis and it is plausible that then the function increases, locally at
a point or in an interval. We shall prove both theorems in this regard. We
start with the case of the derivative having a constant sign on an interval.
Theorem 13.7 (On the sign of the derivative) Assume that a
function f is continuous on the interval I and denote by I" the set of
interior points of I . Then
(i)
(ii)
(iii)
(iv)
(v)
f'(z)2 O in I" +- f
f'(z) 5 0 in I" + f
f'(z) = O in I" + f
f'(z) > 0 in I" + f
f'(z) < 0 in I" +- f
is increasing in I ;
is decreasing in I .
is constant in I ;
is strictly increasing in I ;
is strictly decreasing in I ;
:
(or fi)
This theorem remains valid if f' is replaced by f
Remark 13.5
everywhere. It is this more general version which we prove.
Proof. We prove first (i) indirectly. So assume that fi(z)2 0 in I' and
that there are points z1 < 2 2 with f(z1) > f(z2). It is easy to explicitly
define a linear function Z with the following properties; see Figure 13.3.
(a) Z'(x)
< 0 for all 2,
Introduction t o Mathematics with Maple
370
I
I
I
I
I
Fig. 13.3 A function which does not increase from
I
I
I
I
I
21
to
x2
e
Let S = {x; f(x) - Z(x) > 0, XI 5 x 5 22) and = sup {S}. Since f(x2) Z(x2) < 0 by continuity of f -1 there is a positive S such that f(x) -Z(x) < 0
for x2 - 6 < x 5 2 2 . Consequently < 22. Using (b) and continuity of
f - Z it follows similarly that > z1. By the definition of the greatest lower
bound, for every n E N,there is xn with
l/n > X n 2 5 and xn E S .
Passing to the limit as n + 00 in the inequality f(xn) - Z(zn) > 0 leads
to f(<) 2 I(<). Since f(z) - Z(x) < 0 for x > <,by continuity f(<)5 l(<),
hence f(<) = Z(S) and
<
Dividing by x -
<
<+
< and letting x --+ < gives
This contradicts (i).
To prove (ii) it is sufficient to apply (i) to the function -f.
After (i) and (ii) are established, (iii) becomes obvious.
The proof of (iv) is again indirect. By (i) f is increasing, so assume it
is not strictly increasing. Then there are two points a , b with a < b in I
such that f(a) = f(b). Obviously f is constant on [a,b],hence f+(x) = 0
for a < x < b, which is a contradiction.
Derivatives
Finally, part(v) follows from (iv) applied to -f.
371
0
Before the age of computers this theorem was an indispensable tool for
plotting functions. It is still important but at most occasion the intervals
where f decreases or increases can be found by plotting the graph of f by
Maple.
Example 13.8
W e wish to find the intervals where the following polynomial is monotonic.
>
p:=~->18*~^5-125*~^3+385*~+1;
p := x
+ 18x5 -
125x3 + 3 8 5 x + 1
W e look at the graph of p
>
plot(p,-3..3);
Fig. 13.4 Graph of a quintic
To get a better idea we plot p on [0, 21, in Figure 13.5.
>
plot(p,O. .2);
Introduction to Mathematics with Maple
372
350
300
250
200
150
100
50
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Fig. 13.5 Graph of a quintic, with restricted domain and range
The exact location of points where f changes from increasing t o decreasing
is still not clear. In order to apply apply Theorem 13.7 we calculate the points
where the derivative is zero. Between consecutive points the derivative does
not change its sign and the function is therefore monotonic.
>
solve(D(p) (x)=O) ;
1
3
1
- dz,-5
1
a,
;
6,
-;6
-9
-9
Obviously p increases for x <
then decreases till
and so on.
On reflection it was more convenient t o use f s o l v e rather then s o l v e ; this is
often so in examples like this.
>
fsolve(D(p) (x)=O);
-1.527525232, -1.354006401, 1.354006401, 1.527525232
Since we were dealing with a polynomial, f s o l v e found all solutions t o
Derivatives
373
p(x) = 0 automatically. In more general situations, additional care is needed
to find all solutions.
Example 13.9
The problem consists of finding a rectangle of largest area
cut from a metal plate with the lower left hand corner removed; see Figure 13.6.
We seek the maximum value of A(x) = (d-x)(w-1+x) on the interval [0,1].
Fig. 13.6 A rectangular plate
+
+
The derivative A’(x) = d - w 1- 22 is positive for x < c = (d - w 1)/2
and negative for c > x. If 0 < c < 1then the largest value of A is attained a t
c and the dimensions of the largest rectangle are d - c and w - 1 c. If c < 0
then A’(x) < 0 on [0,1], the function is strictly decreasing and A attains its
maximum value a t the left end-point of [0,1] and the dimensions of the largest
rectangle are d and w - 1. Similarly, if c > 1 then A has the largest value
for x = 1. We needed the zero of A but the zero in itself was of secondary
importance, the crucial importance was the sign of the derivative.
+
Example 13.10
Find intervals of monotonicity of the following function.
>
>
restart;
>
f :=~->100/(401*~^3-899*~^2+600*~)
;
f
>
:= x + 100
+ 600 x
D(f)(x);
-100
>
1
401 x3 - 899 x2
+
1203 x2 - 1798 x 600
(401 x3 - 899 x 2 600 x ) ~
L:=[solve(D(f) (x)=O,x)] ;
+
Introduction to Mathematics with Maple
374
L := [--899
1203
-
1
+-&ail
1203 1203
1
-
899
1203 m'
.5029588776, .9916379636
b=-
899
+ L
1203 1203
d m
f is discontinuous a t 0, hence Theorem 13.7 cannot be applied t o any interval
containing zero. We are left with the intervals ] - w,O[ and 30, a ] , [a,b] and
[b,w[. Near 0 the derivative is negative, by continuity it is negative on ] - 00, O[
and ]O,a[. Hence f is strictly decreasing on these intervals. For large x the
derivative is obviously also negative, hence f strictly decreases on [b, w[. On
the remaining interval f strictly increases. Our result is confirmed by the graph
>
plot(f(x),x=-1. .2,y=-2. .2);
2-
local maximum
Y
1-
0.5
1
1.5
2
X
1-
-
+
Fig. 13.7 Graph of f(x) = 100/(401x3 - 8 9 9 ~ ' 6002)
Derivatives
375
We observe from the graph in Figure 13.7 that at x = &/3 the function is larger than at nearby points although obviously f is larger still for
z positive and close to 0. We say that f has a local maximum at a / 3 .
More precisely
finition 13.2
A function f : I H R is said to have a 1 0 2
maximum at c, an interior point of I , if there exists a positive 6 such
that, for 0 < Ix - cI < S,
f ( 4 I f(c)*
(13.29)
If this inequality is strict then the maximum is also strict. If inequality (13.29) is reversed then f has a local minimum and if the reversed
inequality is strict then f has a strict local minimum.
The common name for a local maximum or a local minimum is a local
extremum. The function x H (x 1x1)(2 - x) has a local minimum at 0,
the function x I--+ x2(x - 1) has a strict local maximum at 0.
+
1) Theorem 13.8
I f f has a local extremum a t c and f'(c) exists then
1
Proof. For definiteness let us assume f has a local maximum. Then, since
f(x)-f(c) 5 0 to the right of c, it follows that f i ( c ) 5 0. Similarly f:(c) 2
0. Since the derivative exists f'(c) = f i ( c ) = fL(c) and consequently
f'(c) = 0.
a
It is important to be aware that the converse of Theorem 13.8 is false. The
function x I+ x3 has derivative 0 at x = 0 but is strictly increasing on
R and therefore cannot have a local extremum at 0. The next Theorem
guarantees the existence of a local extremum.
Theorem 13.9
I f f is continuous a t c and
f'W > 0
(13.30)
f'(4 < 0
(13.31)
on some interval ] a ,c[ and
on some interval ] c , b [ then f has a strict local maximum at c. If
reversed inequalities hold in (13.30) and (13.31) then f has a strict
local minimum a t c.
Introduction to Mathematics with Maple
376
This Theorem is easily remembered as: If f' changes sign at c then it
has a local extremum at c.
Proof. By Theorem 13.7 and by inequality (13.30), the function f increases to the left of c. Hence it is smaller to the left of c than it is at c.
Similarly, f decreases to the right of c so it is smaller there than at c. The
proof for the local minimum is similar, or we can apply the already proved
0
part of the theorem to -f.
Example 13.11
Let f(x) = (1 - x2)l0x2O. It is clear without any differentiation that f has strict local minima a t - l , O , 1. However there are other
extrema. Since
and f' changes sign a t both x = -1/dand x = l / f i the function has strict
local maxima a t these points.
Many practical problems in applications require finding the largest or
the smallest value of a given function on a given interval. Obviously, the
largest or the smallest value is either a local extremum or the value of the
function at one of the ends of the interval. The next example shows how
to use Maple in such a situation.
Example 13.12
We wish t o find the largest and smallest value of the function
below, on the interval [0,2].
>
restart;
>
f:=x->15/(l+abs(x-l))+xn2+2*~-1;
f , defined
1
f
:= x + 15
g := -15
I
1
+ Ix - 11 + x 2 + 2 x - 1
abs(1, x - 1)
+22+2
(1 Ix - 11)2
+
abs(1, x - 1) denotes sgn(x - 1). The reason why Maple uses this alternative
notation need not concern us. However we note that f is not differentiable a t
x = 1.
>
L :=solve (g ,x) ;
Derivatives
377
For better understanding we convert these numbers t o decimal fractions.
>
L1 :=evalf (1);
Ll
:= 1.674571001, -1.583911177
Now we put together the end-points, the points where the derivative does not
exist (there is just one) and zeros of the derivative which lie in [0,2].
>
K:=O,I,2,Ll[l];
K
:= 0,
1, 2, 1.674571001
We evaluate the function f a t these points, using the Muple command map
to map the set { K } onto the set {Kl}.
>
Kl:=map(f,{K});
13 29
K l := (17, 14.11084811, 2,
2)
In order to apply max and min we convert K 1 t o an expression sequence.
13 29
K2 := 17, 14.11084811, 2 ' 2
17
17 is the value of f a t x = 1, 13/2 is attained a t 0. We had better check it:
>
is(i7=f (1)); is(13/2=f (0));
true
true
Introduction to Mathematics with Maple
378
We succeeded, and have found the maximum and minimum values. However,
when using a computer algebra system, it is always advisable to check the
results. A small typing error can cause an enormous error in the final result.
Moreover, it is easy to overlook some subtle feature of Maple, for instance t h a t
solve or fsolve did not find all solutions to Of(.) = 0. Here the easiest
check is to plot f.
>
plot(f
,o..2);
16-
14-
12-
10-
...................................................
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
Fig. 13.8 Graph of the function f
If the derivative is 0 at some point then the function might or might
not have a local extremum at that point. We get a more definite answer if
the derivative is positive.
A function f is said to be increasing at a point c if there is a S > 0 such
that
f ( c - h) < f(c) and
f ( c + h) > f ( c ) ,
(13.32)
for 0 < h < 6. If the inequalities (13.32) for f are reversed the function is
said to be decreasing at c.
Derivatives
379
~
Theorem 13.10 If Df(c) > 0 or Df(c)
c or decreasing at c, respectively.
< 0 then f is increasing a t
with D(0) = Df(c) and D continuous at 0. If Df(c) > 0 then by Remark 12.15 we have D ( h ) > 0 for Ihl < 6. Consequently Equations 13.32
hold. The proof is entirely similar if Df(c) < 0.
The derivative of a function has the intermediate property even if it is not
continuous.
Theorem 13.11
I f f ‘ exists on an open interval I then it has the
intermediate value property.
Proof. For sake of definiteness let f’(s1)< L < f‘(22) with 2 1 < 2 2 .
Define an auxiliary function F : z H Lx - f(2). On the interval [XI,221
the function F attains its maximum, say at c. By Theorem 13.10 F is
increasing at x1 and decreasing at 2 2 . Consequently c # 2 1 and c # 2 2
and the maximum at c is local. By Theorem 13.8 we have F’(c) = 0 and
0
consequently f’(c) = L.
This Theorem is not valid for one-sided derivatives, For f(z)= 1x1 we have
f i ( 1 ) = 1, f;(-1) = -1 but fi(2)is never zero on ]0,1[.
Ezercises
Exercise 13.3.1
Find the local maxima and minima of
[Hint: The answer is -1,O, 1.1
v m .
@ Exercise 13.3.2
Prove that if a continuous f has only one local minimum a t c on an interval I and no local maximum then f (c) is the smallest
value o f f on I . [Hint: Indirect proof, assume that f(d) < f(c) and find a
local maximum between c and d.]
@ Exercise 13.3.3
The previous exercise is particulaxly useful if I is either
infinite or open. Find the minimum value of 2 1/x2 on 10, cm[.
+
Introduction to Mathematics with Maple
380
@ Exercise 13.3.4 Prove: If a continuous f has local minima at a and
b, b > a, then it has at least one local maximum at some point c € ] a ,b[ If
further, f has no local minimum inside ] a ,b[ then f attains its largest value
at c. Show also that the assumption that f continuous is essential.
@ Exercise 13.3.5
I f f is increasing on I with rgg c I then g and f
have the same extrema (local or otherwise). Prove it.
og
Exercise 13.3.6
A truck travels between Sydney and Brisbane, a distance of 700km, at the uniform speed of x km per hour and does not exceed
the speed limit of lOOkm per hour. Assuming that the consumption of diesel
for 1OOkm is given by a bx2 of litres, that the cost of the crew is c dollars
per hour and the cost of diesel per litre is $0.74. For what c is the limit
speed of 100km/hour the most economical?
+
Exercise 13.3.7 Among all circular cones of surface area S find the one
which has the largest volume.
Exercise 13.3.8 Find the circular cone of largest volume inscribed in a
sphere of radius R.
@ Exercise 13.3.9
Use Theorem 13.7 to prove: I f f ' is constant on some
open interval I then f is linear on I .
13.4
Higher derivatives
If a function f has a derivative at every point of an interval I then the function x H f'(z) is denoted naturally as f '. If this function has a derivative
at x then it is called the second derivative of f at x and is denoted by f"(x)
or D2f(x). The second derivative can again have a derivative, the third
derivative, denoted by f "'. The nth derivative f(") is defined inductively
(13.34)
The notation with D2 = D D or generally D" is also used. For instance
(x7)" = 7 (x6)' = 42x5 = D2x7,
Derivataves
or if f(x) = (1
+ x)"
with m E
D ( 1 + x)" = m(1
D2(1
+ x)"
38 1
Q then
+ x)m-l,
= m(m - 1)(1+ 2 y 2
(13.35)
(13.36)
(13.37)
+
+ 1)(1+ x ) ~ - " .
= m(m - 1) - ( m- n + 1).
P ( 1 x)" = m(m - 1 ) . - (m - n
(13.38)
For x = 0 we obtain f'"'(0)
The Leibniz formula for the nth derivative of the product reads
(13.39)
In this formula u(O),the zero derivative of u,is to be understood as u itself.
Similarly do)= w. This convention is not only used here but generally
throughout calculus. The proof of Equation (13.39) is by induction, and is
similar to the proof of the binomial theorem 5.4 and is left to the readers
as an exercise.
13.4.1
>
>
Higher derivatives in Maple
restart;
f:=x->x^3;
f :=x-+x3
>
D(f)(x);
3 x2
>
Df :=unapply(%,x);
Df : = x + 3 x 2
>
D(Df)(-2);
- 12
>
diff ( x 6 3 , x , x ) ;
- 12
Introduction to Mathematics with Maple
382
If we have to differentiate many times it is convenient to produce a sequence
of x's first.
>
dnu:=seq(x,k=l. .ll):diff(x"(lOO),dnu);
5653408585997652480000z8'
> dnum:=seq(x,k=l. .100):diff(I/(l+x),d~:subs(x=O,%);
9332621544394415268169923885626670049071596826438162146859\
2963895217599993229915608941463976156518286253697920827223\
758251185210916864000000000000000000000000
By Equation (13.38), for m = -1 this number is equal to
confirmed:
>
loo!; this is easily
%/loo!;
1
13.4.2
Significance of the second derivative
Applying Theorems 13.9 and 13.10 to the second derivative gives
Theorem 13.12 Assume f'(c) = 0. If f"(c) > 0 then f has a strict
local minimum at c, if f"(c) < O then f has a strict local maximum at
C.
This theorem deals with the second derivative at a point. We are now
going to study the behaviour of a function if the second derivative is of a
definite sign on some interval.
A function f is said to be convex on an interval I if
(13.40)
for 21 < x2 < 5 3 in I . If the inequality in Equation (13.40) is strict the
function is strictly convex. f is said to be concave or strictly concave on
I if -f is convex or strictly convex, respectively. The terms concave up
(upwards) and concave down (downwards) are used by some authors instead
of convex and concave. Equivalent forms of inequality (13.40) are
Derivatives
383
The geometrical meaning of Equation (13.40) is: The point ( 2 2 , f ( x 2 ) )
lies below the straight line joining ( 2 1 , f(x1)) with ( 2 2 , f(zz)), whereas
Equation (13.42) indicates that the slope of the line joining (XI , f ( 2 1 ) )
to ( 2 3 , f ( 2 3 ) ) is smaller than the slope of the line joining ( 2 3 , f ( x 3 ) ) to
( 2 2 , f ( ~ 2 ) ) . This means that the difference quotients are monotonically
increasing. The next theorem extends this monotonicity to the derivative.
Theorem 13.13 A function differentiable on an open interval I is
convex if and only if f ’ is increasing on I . I t is strictly convex if and
only if f ’ is strictly increasing on I .
Corollary 13.13.1 A function differentiable on an open interval I
is concave if and only i f f ‘ is decreasing on I . I t is strictly concave if
and only i f f ’ is strictly decreasing on I .
Proof.
Let f be convex. Sending 2 2
-+ z1
in inequality (13.42) gives
(13.43)
Taking the limit again in inequality (13.42), this time as 22
-+ 2 3 ,
leads to
(13.44)
Combining Inequalities (13.43) and (13.44) leads to f’(x1) _< f ’ ( 2 3 ) . Since
2 1 and x 3 were arbitrary, subject only to the condition 21 < 3 3 , we have
proved the monotonicity of f
If f is not convex then there are three points 21 < 2 2 < 2 3 such that
inequality (13.40) is false. In other words
’.
(13.45)
We denote
Introduction to Mathematics with Maple
384
and chose an auxiliary function
From inequality (13.45) we obtain F(x1)= 0 < F(x2), consequently, by
Theorem 13.7, there is a point J12 between x1 and 2 2 such that f’(J12) > Q.
Reasoning similarly leads to J23 between 22 and x3 for which f’(C23) < Q.
This proves f‘ is not increasing.
If f is strictly convex then f’ is still increasing, if it were not strictly
increasing it would be constant on some subinterval of I , and then f would
be linear on this subinterval by Exercise 13.3.9, hence not strictly convex.
If f is strictly increasing but not strictly convex then F defined in
Equation (13.46) would be zero at 21, x2, 2 3 and would have local extrema
in 3x1, 22 [ and 1x2,x3 [. At these points F’(x)= 0 and consequently at these
points f‘ would be equal to Q. So, f‘ would not be strictly increasing. 0
This Theorem, together with Theorem 13.7, leads to the next Theorem,
which is easier to apply.
~~
Assume that f” exists on an open interval I .
Theorem 13.14
Then
(i) f”(x)
(ii)
(iii)
(iv)
(v)
2 O in I” + f
f”(x) 5 O
f”(x) = O
f”(x) > O
f”(x) < O
in
in
in
in
I” =+ f
I” + f
I” =+ f
I” 3 f
is convex in I ;
is concave in I ;
is linear on I ;
is strictly convex in I ;
is strictly concave in I .
Example 13.13
>
>
restart;
f :=x->(4l*x^3-119*x)--(l/3);
f
:= x + (41 x3 - 119 x)(’/~)
2 (123x2 - 119)’
82 x
-9 (41 x3 - 119 x)(5/3) (41 x3 - 119 x ) ( 2 / 3 )
+
+
238
123 x’ 119
f2 := -9 (41 x3 - 119 x)(5/3)
Derivatives
385
The numerator is positive, so the sign is determined by the denominator.
To find where the derivative is positive and where it is negative we find the
zeros of the denominator.
>
~:=[so~ve(4i*x~3-i~~*x=O,x)l;
L := [O,
1
1
-&mi,
-41
dam]
41
We denote
>
a:=L[3] ;b:=L[21 ;
1
a := -- &35
41
1
b : = -dB%
41
f is convex on 10, b[ and 3 - 00, u [ and concave on the two remaining
intervals. The graph is shown in Figure 13.9. When plotting f the command
surd must be used rather than a fractional exponent because cube roots of
negative numbers are involved.
>
>
restart;
plot(surd(41*xn3-119*~,3) ,X -2.
-2);
A function f is said to have an inflection point at c if f‘ has a strict
local extremum at c. An immediate consequence of this definition is
Theorem 13.15
f”(c) = 0.
Iff has an inflection at c and f”(c) exists then
An inflection point is of significance for the graph of f since the graph
of f attaches itself tightly to the tangent at the inflection point: the graph
crosses the tangent from one side to the other. If, for instance, f‘ has a
strict local maximum at c then, for some positive 6, the function
has a positive derivative for c - 6 < x < c and also for c
Consequently F is strictly increasing and
F ( z ) < 0 = F ( c ) for x < c,
F ( z ) > 0 for x > c.
< x < c+6 .
(13.47)
(13.48)
386
Introduction to Mathematics with Maple
Fig. 13.9 Convexity, concavity
f
c-s
C
t.
C + 6
Fig. 13.10 An inflection point a t x = c
Inequality (13.47) means that the graph of f lies first below the tangent and
then by inequality (13.48) above the tangent. Similarly, if f ' has a strict
Derivatives
387
local minimum, then the graph crosses from above to below the tangent.
If f”(c) = 0 then f might have an inflection point at c but need not.
(x4)” = 4 . 3 is~zero
~ at x = 0 but there is no inflection. The next Theorem
guarantees the existence of an inflection point.
Theorem 13.16
tion point at c.
If f’’(c) = 0 and f”‘(c)
# 0 then f
has an inffec-
Proof. This is an immediate consequence of the definition of an inflection
point and Theorem 13.12.
0
+
Example 13.14 The function f : x H 72 x3 has the second derivative
equal to zero at x = 0, the third derivative f”’(0) = 6. Consequently f has
an inflection point at IL: = 0 by Theorem 13.16.
Exercises
Exercise 13.4.1
Find intervals where the given functions are convex
and where they are concave.
+
I . f (x) = 3x5 - lox3 6 x - 7,
2. g(z) = (x - 3)6 - 32 4,
3. h(x) = x3/(x2 1).
+
+
@ Exercise 13.4.2
Prove: I f f is convex, but not strictly convex, on an
interval I , then there is a subinterval of I on which f is linear.
Exercise 13.4.3
Find a and b such that f : x
inflection point at x = 1.
H
2ax3
+ bx2 has an
@ Exercise 13.4.4
Prove: A differentiable function has an inflection between two consecutive local extrema.
@ Exercise 13.4.5 The function f : x H ZIZI has an inflection point a t
x = 0, however it is not true that f”(0) = 0. Why? [Hint: Consider f ’ ( x )
for x > 0 and for x < 0.1
@ Exercise 13.4.6
Prove: f is convex on [a, b] if and only iff;
exists on [a, b] and is increasing.
or
fL
Introduction to Mathematics with Maple
388
13.5
Mean value theorems
Figure 13.11suggests that there is a point (c, f ( c ) )on the graph off where
the tangent is parallel to the line joining the end-points, that is
(13.49)
I
I
I
a
01
c
b
Fig. 13.11 The mean value theorem
We state this as
1
Theorem 13.17 (The Lagrange mean value theorem) I f f :
[a,b] I-+ R is continuous on [a,b] and differentiable for a < IC < b then
there exists a point c E [a,b] such that Equation (13.49) holds.
Figure 13.12 exhibits the same phenomenon for a parametrically given
curve {[f(t),g ( t ) ] ;a 5 t 5 b } . We state this as
Derivatives
389
Theorem 13.18 (The Cauchy mean value theorem) I f f :
[a, b] H R and g : [a, b] H R are continuous on [a, b] and differentiable for a < t < b then there exists a point c with a < c < b such
that
The Lagrange mean value theorem is a special case of the Cauchy mean
value theorem for g ( s ) = x. It suffices to prove the Cauchy theorem. The
term mean value theorems is not really appropriate; the emphasis should
lie on the increment of the function or functions, not on the mean value c,
but in English-speaking mathematics the terminology is firmly established.
If g’(z) # 0 for a < x < b then it follows from (13.50) that2 g(b) - g(a) # 0
and Equation (13.50) takes a more convenient form
(13.51)
Formulae (13.49), (13.50) and (13.51) are often written in a different but
equivalent form. One writes x and x+ h instead of a and b. Then c becomes
x O h with 0 < 0 < 1. Equation (13.51) then becomes
+
(13.52)
The advantage in using the notation with 0 is that for negative h, equations
like (13.52) hold without any need to change the inequality for 0. The
numbers c or 0 are not uniquely determined, for instance, for constant f
the number c can be any number in [a, b] and 0 any number in ]0,1[. The
dependence of 0 on f , x and h in the Lagrange mean value theorem is
studied in Exercises 13.5.1 and 13.5.2. The importance of the mean value
theorem comes from the fact that it allows conclusions to be made about the
function from knowledge of the derivative, even if nothing more is known
about 0 than 0 < O < 1.
For the proof of the Cauchy theorem we denote
2By choosing f(s)= s.
Introduction to Mathematics with Maple
390
0
Fig. 13.12 The Cauchy mean value theorem
Readers familiar with analytic geometry will recognize that IF(x)l is twice
the area of a triangle with vertices ( f ( a ) ,g(a)), (f(z),
g(x)), ( f ( b ) , g ( b ) ) .
Proof. F is zero at the ends of the interval [a, b]. By Weierstrass’ theorem 12.22 the function F attains its extrema in [a, b]. Since F ( a ) = F(b) =
0 either the maximum or the minimum is attained at an interior point c.
By Theorem 13.8 the derivative F’(c) = 0 and this is equivalent to Equation (13.50).
0
Exercises
@ Exercise 13.5.1
Show: If f ( t ) = t2 then 0 from the Lagrange mean
value theorem is 1/2, independent of x.
[email protected]
Exercise 13.5.2
Find 0 from the Lagrange mean value theorem if
f ( t ) = t 3 . Show that 0 --+ 1/2 as h + 0.
@ Exercise 13.5.3
The Lagrange mean value theorem becomes false i f f
fails to be differentiable at one point. [Hint: f(x) = 1x1, [a, b] = [-1,ll.l
Derivatives
391
@ Exercise 13.5.4
Prove: Between two real zeros of a differentiable function there is a zero of the derivative.
@ Exercise 13.5.5
For complex valued functions the mean value theorems
are false. Consider the following counterexample to the Lagrange theorem:
f (x)= x3 - x 2(x3 - z2) and [a, b] = [0,1].
+
13.6
The Bernoulli-1’Hospital rule
Theorems for calculating limits by means of derivatives are known as
1’Hospital rules. However they were discovered by J. Bernoulli who communicated them to Marquis I’Hospital, who promptly published them under
his own name. Their importance for practical evaluation of limits is diminished when limits can be found easily by Maple. However, we need the
rule in the next section. We first make a remark indicating that the rule is
plausible.
Remark 13.6
If f ( a ) = g(a) = 0 then
h
provided f’(a), g’(a) exist and g’(a) # 0. If we try to apply this result to
f i - 1 and g(x) =
for a = 1 we encounter a difficulty: g’(1)
does not exist. However the limit can be calculated as
f(x) =
(13.54)
with the result that
lim
x+1-
fi-1
~
3
2-15
= lim -
v m
= 0.
In the above we have two variants. In the first the required limit is equal to the
ratio of the derivatives a t the point in question. In the second the limit is equal
t o the limit of the ratio of the derivatives a t that point. It is a rather subtle,
but significant, point t o recognise that these two procedures are distinct. The
combination of these two methods is important for extending the rule t o the
case where the first few derivatives of f and g are zero a t the point. The
following theorem describes the general situation.
Introduction to Mathematics with Maple
392
Theorem 13.19 Let I be an interval, a an interior point of I and
f : I R and g : I H R. If n is a non-negative integer, f(”) and g(n)
are continuous at a,
= f(,)(a) = 0,
(13.55)
g(a) = g’(a) = - - - = g(,)(a) = 0,
(13.56)
f ( a ) = f’(a) =
* * *
and EITHER
(i) lim
x-a
f(n+l) (x)
g(n+l)(2)
OR
(ii) both f
=I
(a)and gn+l ( a ) # 0 exist
then
f (4
lirn 9(x)
(13.57)
x-a
I
exists and is equal to 1 or to
fgn+l(a)
,+l (4 respectively.
’
Proof. We commence the proof of (i) for3 n = 0. Using the Cauchy mean
value theorem gives
for some O with 0 < 0 < 1. Let h, -+ 0 then4 Oh, + 0 and by assumption
(i) and the definition of a limit, f’(a+Oh,)/g’(a+Oh,)
-+ 1. Consequently
lim
x+a
fo
= 1. We proceed by induction now.
9(4
(13.59)
by assumption and by what we have already established. Since
lim
x+a
f (4= lim f” (4
g(x)
5-a
g”(z)
)
by the induction hypothesis, we now have (i).
3f(0) and g(O)
are understood as f and 9,respectively.
40depends on n but this dependence has no influence on the proof.
(13.60)
Derivatives
393
For (ii), we have by (i) that Equation (13.60) is true and by Equa0
tion (13.53) applied to f n and g n in place of f and g we obtain (ii).
The next Theorem has a very similar proof. It says, roughly speaking,
that the derivative cannot have “jump discontinuities”.
Theorem 13.20 I f f : I H R is continuous a t an interior point of
intervd I and lim f’(x) = I then f is differentiable at a and f’(a)= 1.
x-+a
Proof.
By the Lagrange mean value theorem
with 0 < 0 < 1. If h n -+ 0 then Oh, -+ 0 and the right hand side tends
(by assumption) to 1. Consequently, the left hand side has also limit I and
0
this means that the derivative exists and is 1 .
The derivative can exist at a even if lim f’(z)does not. For instance,
if f(z)= x2saw(1/z) for
derivative at 0.
2
#
x4a
0 and f ( 0 ) = 0 then f has a discontinuous
The Bernoulli-1’Hospital rule can also be applied in situation when
g ( x ) -+ 00. For sake of completeness we state the next theorem in which
the symbol Lim stands consistently throughout the theorem for any of lim
x-a
or lim or lim or lim or lim .
xTa
xJa
x+m
Theorem 13.21
x+-m
f ‘(4
If Lirn = I then
g‘(4
f (4-1
Lim 9(4
if EITHER
Lim f(z)= Lim g ( x ) = 0
OR
Lim 1g(z)1= 00
~~
This theorem remains true if I denotes
00
or
-00.
Introduction to Mathematics with Maple
394
Exercises
Exercise 13.6.1
in Exercise 12.1.1.
Use the methods of this section to evaluate the limits
@ Exercise 13.6.2
Reckless application of the Bernoulli-I’Hospital rule
can lead t o wrong results. For instance, lim ((x2 2)/(3x3 - 1)) =
+
x+1
lim(2x/9z2) = 2/9 but clearly lim ((z2
x-1
x+l
+ 2)/(3z3 - 1)) = 3/2.
Where
is the mistake.
[email protected]
Exercise 13.6.3 Show that Theorem 13.20 can be extended t o one-sided
limits and one-sided derivatives .
[email protected]
Exercise 13.6.4 The Bernoulli-1’Hospital rule can be extended to onesided derivatives. Prove: If g is monotonic, limf(z) = limg(z) = 0 and
xla
limf:(z)/g$(z)
xla
xla
= l or limfL(x)/gl(x) = l then limf(x)/g(x) = 1. [Hint:
xla
xla
For the proof and discussion of theorems of this type see Vyborny and
Nester (1989).]
13.7
Taylor’s formula
Often it is convenient and sometimes even necessary to approximate a given
function by another function, which is simpler or has better properties. It
is easy to work with polynomials, which have many simple good properties.
For example, they have derivatives of all orders and their function values
can be computed by multiplication and addition only. Even computers
can handle polynomials better than most other functions: Maple finds all
zeros of a polynomial by f solve, but need not do so for a more complicated function. We have already encountered polynomial approximation in
Weierstrass’ Theorem 12.22 which deals with a uniform approximation of
a continuous function on an interval. However, the theorem does not really
give any means of finding this approximation effectively and constructively.
Our study in this section will deal with a different approximation. It is
constructive and effective, but approximates the given function only5near
a given point.
We encountered Taylor polynomial before in Section 6.5. If f is a poly‘However, generally speaking, and given some luck, the approximation can be good
everywhere.
Derivatives
395
nomial of degree n then
is a polynomial equal to f for all x. If f is n times differentiable at a then
T, can always be formed, regardless of whether or not f is a polynomial.
It is natural to expect that T, would be a good approximation of f and
then the estimate of
would determine how good the approximation is. In the last chapter of this
book we shall derive powerful but reasonably simple estimates of &+I.
In this section we shall be satisfied with the next Theorem 13.22, which
says that Rn is of an order of magnitude smaller than (z - a),. We illustrate the nature of approximation by plotting some graphs in Maple. Let
as recall that the command6 taylor (f (x) ,x=a, k) :convert (% ,polynom) ;
produces the Taylor polynomial for f of degree k and it can be then used
for plotting. The following lines of Maple code produce the first eight approximations, t G t 8 , for the function defined below.
>
>
restart;
F:=x->l/sqrt(l+x);
>
>
>
k:=l; while k<9 do
t l Ik:=convert(taylor(F(x),x=O,k),polynom)
k:=k+l; end do:
A few approximations, together with f, are depicted in the next two
Figures. In Figure 13.13 the second and third approximations are plotted;
in Figure 13.14 the third and eighth approximations are graphed.
Maple uses the symbol h ( z ) = 0(4(z)) and g(z) = O(+(z)) to indicate
that
(13.62)
6Explained earlier for polynomials but applicable generally.
396
Introduction to Mathematics with Maple
X
Fig. 13.13 Taylor approximations
X
Fig. 13.14 Taylor approximations
Derivatives
397
or that g(z) is bounded. These symbols do not reveal the point at which
w
the limit in Equation (13.62) is taken or the interval on which 0(4(z))
is bounded. This must be understood from the context. These symbols
are convenient and widely used, but unfortunately the behavior of these
symbols might look bizarre to the uninitiated: for instance the relations7
o(z) O(z) = O(z) or o(z) o(z) = o(z) are correct, but they do not imply
that o(z) = 0. We are now ready for the main theorem of this section.
+
+
Theorem 13.22
I f f has the n-th order derivative at a then
f(z)- T n ( x ) = Rn+l(z)= o((z
Proof.
- a)n).
(13.63)
Denote g(z) = (z - u ) ~Clearly
.
D ~ R , + ~ ( u=O
)
~ ' g ( a =O
>
k = 1 , ..., n ,
for k = 1 , . . . ,n - 1,
for
Dng(a) = n!
By Theorem 13.21
This theorem is called the Taylor formula with Peano remainder. We
use it for a proof of a theorem on local extrema.
Theorem 13.23
Let f : [a,b] I-+ Iw and n be even. If
f ' ( a ) = f " ( a ) = . . . = f'"-l'(a) = 0,
f '"'(4#O,
then f has a local extremum a t a. I t is a maximum if f n ( a ) < 0 and
a minimum if f n ( a ) < 0.
Proof.
We apply Theorem 13.22 assuming f n ( a ) > 0.
f(z)= f(a)
+
(9
+
a ( x ) ) (z - q,
+
with a ( z ) -+0. By Corollary 12.8.1 there is a positive 6 such that f n ( a )
a ( z )> 0 for Iz - a1 < 6 and then f(z)< f ( a ) ,which means there is a local
7The relevant point is 0.
Introduction to Mathematics with Maple
398
minimum at a. The case f " ( a ) < 0 can be handled similarly, or we can
0
apply the already proved part to -f.
The next Theorem has very similar proof; we skip it.
Theorem 13.24
Let f : [a,b]H R and nbe odd. If
f"(.)
=
. . . = j("-f'(a>= 0,
f(")(a) # 0,
then f has an inflection at a.
Exercises
@ Exercise 13.7.1
Prove the following addition to Theorem 13.23: I f n is
odd then there is no local extremum at a.
@ Exercise 13.7.2
Prove the following addition to Theorem 13.24: I f n is
even then there is no inflection at a .
Find the local maxima, minima and inflection points
Exercise 13.7.3
for each of the following functions:
I. 2 ( 1 -
4 3 ,
2. xm(l - x)' with rn E N and I E N,
3. l / d W .
13.8
Differentiation of power series
If a function is given by a power series
then it is easy to find the derivative, with differentiation like that for polynomials:
f'(.)
= c1+ 2 ~ 2 -( U~)
+ 3 ~ 3 ( 1 1-: + - +
* *
This is plausible but it does require a proof.8
81t follows Vyborny (1987).
TZC"(Z
- a)"-'
+-
* *
. (13.65)
Derivatives
399
Theorem 13.25 Let the power series (13.64) have a positive radius
of convergence R (possibly 00). Then the power series
d(x) = c1+ 2c2(z - a ) + 3c3(x -
+ - - + ncn(x - a)"-' + - - -
has the same radius of convergence R and for all complex x with
.1 - < R
f'(x) = d(x).
Without loss of generality we take a = 0. We need
Lemma 13.1
0
Let n denote a positive integer. For x E C, h E C and
< lhl I H
(13.66)
( n ~ " - ' lI
H1 [2(1x1 + H)" + IxI"].
(13.67)
Proof. For the proof of inequality (13.66) we use the binomial Theorem 5.4.
Since
Inequality (13.67) follows from inequality (13.66) with h = H .
0
We can now prove Theorem 13.25.
Proof. For a given x with 1x1 < R choose H such that 1x1
Multiplying inequality (13.67) by I c , ~ gives
lnc,xn-ll
1
I H [2c, (1x1+ H)"
+H
< R.
+ cnlxl"].
By the comparison theorem the power series for d converges, this in turn
implies that the radius of convergence for d is R. It follows from inequal-
400
Introduction to Mathematics with Maple
ity (13.66) that
The sum on the right hand side is a well-defined finite number and as h -+ 0
the right hand side tends to 0, consequently f’(z) = d(z).
0
An important consequence of our theorem is that the derivative, being
again a power series, is differentiable. Then the second derivative, as a
power series, is again differentiable, and so on. A convergent power series
has derivatives of all orders. Substituting x = a into Equation (13.65) gives
c1 = f’(a), differentiating and substituting again gives c2 = f ” ( a ) / 2 ,and
by differentiating n-times leads to
Cn
=
f‘n’(a). This leads to
n!
This series is called the Taylor series of f at a. The special case a = 0,
that is
(13.70)
has the name of Maclaurin series. It is important to realize that we obtained these formulae under the assumption that f was representable by a
power series. This assumption must be justified for concrete functions. In
Chapter 15 we shall prove some general theorems guaranteeing the validity
of Equation (13.69). However, it might happen that a function has derivatives of all orders but is not equal to its Taylor series. Such a function is
defined in Example 14.1. Here is an example in a positive direction.
Example 13.15
The formula
is valid for 1x1 < 1. Theorem 13.25 gives
Derivatives
401
and by differentiating m - 1 times
(-1)"-'(m
+
- l)!
(1 x)m
00
C
=
IC(IC- 1) -
e .
(IC - m + 2 ) ~ ~ - ~ + ' .
(13.71)
k=m-l
It is convenient t o extend the definition of the binomial coefficient to any real
n and positive integer k by
(13.72)
for instance
3-/
1.2-3
-
These definitions allow us to rewrite the Maclaurin series for ( l + ~ ) m
- ~E ,N,
in the following form
+
(1 x)-
c
00
=
(13.73)
k=O
This equation is called the binomial expansion: it resembles Formula (5.30) in
the Newton's binomial theorem for a = 1 and b = x. The difference is that
now the formula is correct only for 1x1 < 1and the expansion is infinite.
13.9
Comments and supplements
The concepts of right-hand and left-hand derivative can be further generalized, in a similar way as one-sided limits were to limits superior and
inferior. The following four numbers
Introduction to Mathematics with Maple
402
are called the Dini derivates. The first one is the upper right-hand Dini
derivate, the third is the lower left-hand Dini derivate, with similar terminology for the other two.
D+
Y
f
/
D-
0
I
I
I
I
C
X
Fig. 13.15 All Dini derivates are distinct at c
The behaviour of a function with all Dini derivates distinct at c is indicated in Figure 13.9. It can be shown that, for a continuous f , the set
of points c such that D+f ( c ) < D-f ( c ) is at most countable. So is the set
where D-f (c) < D+f (c). A consequence of this is: the set where both the
right-hand derivative and the left-hand derivative exist and are not equal
is countable. There is another theorem in similar vein: A set of points of
strict local maxima (or minima) of a function, continuous on some interval,
is at most countable.
There is a theorem for Dini derivates similar to Theorem 13.20. If f
and one of its Dini derivates is continuous on an open interval I then all
Dini derivates are equal to f' on I .
Bolzano constructed a function continuous everywhere and differentiable
Derivatives
403
nowhere.
X
Fig. 13.16 The first approximation to the Bolzano function
Now we sketch his construction. First we define a sequence of divisions
of [0,1] as follows: D1 consists of [0,1], if u, v are consecutive points of D,
with 1 = v - u > 0 then the intervals
[u,
u
+ :l]
+
+
[u ~3 lu , -11
1
2
[u
+ 51,
1 u + -11
7
8
[u
+ g7l , v]
(13.74)
become subintervals of Dn+l. We proceed by defining a sequence of functions f, each piecewise linear and continuous on [0,1]. Let f1 : x H x. If
f, has been defined and u, v are consecutive dividing points of D, then let
f n + l ( U ) = f n ( 4 and
fA+l
fA+l
55 f;
= -fA
=
on the interiors of the first and third interval of (13.74),
on the interiors of second and fourth interval of (13.74).
It follows that f,+l(v) = fn(v). Consequently fn+l is continuous on [0, 13.
The graphs of f2, f3, f4 are shown in Figure 13.17. The functions f, very
quickly become difficult to graph. The Bolzano’s function f is the limit of
404
Introduction to Mathematics with Maple
1.2
1
0.8
0.6
Fig. 13.17 The first three approximations to Bolzano's function
fn,
that is
for z E [0,1]. For details and proofs we refer to Viborni (2001). A famous
example is by Weierstrassg, a relatively simple example due to Van der
Waerden, accessible to our readers, is in Spivak (1967). We consider now a
very simple example from McCarthy (1953). The function is given by
gThe definition of the Weierstrass function is also in McCarthy (1953)
405
Derivatives
where
The proof that F is continuous is easy and left for Exercise 1 3 . 9 . 1 . Let
N be a positive integer and h N = &2-22-2N. The function g N is h e a r
N
on intervals of length 22 / 2 , twice as long as I h N l , therefore it is possible
to choose h N positive or negative in order that x and x h N are in the
interval on which g N is linear. Then we have
+
(i) g i ( x
+ h N ) - g i ( x > = o for i > N since 22ih~ is an integerlo
1
(ii) g N ( x -k h N ) - g N ( z ) = - '
4'
(iii)
Using this we have
The right-hand side goes to infinity as N
exist.
+ 00.
A finite derivative cannot
Exercises
0 Exercise 13.9.1
C
O 0 1
-gn(z)
i=k+l 2n
0 Exercise
Prove that F is continuous. [Hint: Choose k such that
k
< &/3. Then use continuity of
13.9.2
Bolzano function.
C 2n1
-gn(x).]
1
Use Maple to plot the third approximation to the
'OThe function saw has period 1 and hence saw(u
+ m) = saw(u) for m E N.
%
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Chapter 14
Elementary Functions
In this chapter we lay the proper foundations for the exponential
and logarithmic functions, for trigonometric functions and their
inverses. We calculate derivatives of these functions and use this
for establishing important properties of these functions.
14.1 Introduction
We have already mentioned that some theorems from the previous chapter
are valid for differentiation in the complex domain. Specifically, this is so for
Theorem 13.1 and 13.2, for basic rules of differentiation in Theorems 13.3,
13.5 and for the chain rule, Theorem 13.4. In contrast, Theorem 13.7 makes
no sense in the complex domain since the concept of an increasing function
applies only to functions which have real values. However, part (iii) of
Theorem 13.7 can be extended as follows.
For a E C and R > 0 denote by S the disc
{ z ; lz - a1 < R } or the whole complex plane C. I f f’(z) = 0 for d l
z E S then f is constant in S .
Theorem 14.1
+
Proof. For t E [0,1] let 2 = tzl (1 - t)z2. If z1 and z2 are in S so is
2. Let F : t I--+ f(Z), F l ( t ) = %ZF(t) and f 2 ( t ) = Q F ( t ) . Then
F’(t) = f’(Z)(z1 - 2 2 ) = 0.
Consequently F i ( t ) = Fi(t) = 0. By (iii) of Theorem 13.7 both F1 and F2
are constant in [0,1],hence f(z1) = f(z2) and f is constant in S.
0
An easy consequence of this Theorem is: f : C H C is a polynomial of
degree at most n if and only if f(n+l)(z) = 0 for z E C.
407
Introduction t o Mathematics with Maple
408
14.2
The exponential function
A natural question to ask is: Is there a function which is not changed
by differentiation? An obvious and uninteresting answer is, yes, the zero
function. Hence a better question is: Does a function E exist such that
E’ = E and E’(a) # 0 for some a? The answer is contained in the next
theorem.
Theorem 14.2
The equation
E’(x)= E(x)
(14.1)
has a solution exp : C H C satisfying the condition exp(0) = 1.
The function exp has the following properties
(i) exp(1) = e.
(ii) For z , y E C
exp(z
+ Y) = exp(z) exP(Y)-
(14.2)
(iii) exp(x) # 0 for all x E C.
(iv) The restriction of exp to R is strictly increasing and for a nonnegative integer n
If E is any solution to Equation (14.1) in C then there is a constant c
such that E(x) = cexp(x).
The graph of exp is shown in Figure 14.1
Proof. If exp existed then all derivatives at 0 would be exp(0) = 1 and
the Maclaurin expansion of exp would be
x2 x3
1 + x + -+ -+ * - .
2!
3!
Xn
+n!
+ a * .
.
This series converges by the ratio test, Corollary 11.9.1, for all complex x,
it therefore defines a function, say E. Obviously E(0) = 0 and by Theorem 13.25
x
x2
xn-l
E’(x) = 1 + 2 -2! + 3 - 3! - . - + n - n ! + - . .= E(+
+
E has the defining property of exp, so we call it exp and we have a very
409
Elementary finctions
1 -
0
Fig. 14.1 The exponential function
important expansion
x2 x3
Xn
exp(x)= l + x + - + - + - - - + - + - - * .
2!
3!
n!
(14.4)
(i) is obvious from the definition of e, that is, by Examples 10.13 and
10.14.
To prove (ii) consider H : x H exp(x u)exp(-x). We have that
H’(x) = 0, consequently H(x) = H ( 0 ) = exp(u). Hence we have that
exp(x+u) exp(-x) = exp(u). Since x and u are arbitrary complex numbers
we can set z = z u and -x = y. Then u = z x and Equation (14.2)
follows.
By (ii) we have exp(z)exp(-x) = exp(0) = 1, hence (iii).
exp(0) = 1 by (ii) and by the intermediate value theorem exp(x) > 0
for all x E R. Since exp(x)’ = exp(z) > 0 the function exp is increasing.
+
+
+
Introduction to Mathematics with Maple
410
For x
> 0 we have
xn+l
exp(x)>l+x+-..+-
(n
+ l)!
,p+l
>-
+
(n l)!
and therefore
The second limit relation in (14.3) follows from the first since
1
If E is a solution to Equation (14.1) in C then [E(z)/exp(z)]' = 0, consequently this function is constant and then E(x) = E(0)exp(x).
0
Equation (14.3) could also been proved using Theorem 13.19.
For f(x) = exp(-l/x2) with f(0) = 0, the derivatives
= 0 for all k E N. Firstly
Example 14.1
f(')(O)
(14.5)
It will be convenient t o consider
1
(14.6)
Since, by Equation (14.4) with
x
replaced by 1/x2, we have
Consequently the limit in Equation (14.6) is zero. It follows from this equation
with k = 1 and from Equation (14.6) that f'(0) = 0. For x # 0 the derivative
f'(x) = 2exp(-l/x2)/x3 is of the form P(l/x)exp(-1/x2), where P is a
polynomial. Simple induction shows that this is also true for any derivative
f(")(z),By Equation (14.6) then lim fck)(x)= 0. It follows that f ( k ) ( 0 )= 0
2-0
for all k E N as asserted.
This example is interesting because the Maclaurin series of f converges for
all x, it is zero for all x and hence it is n o t equal t o f(x) for any x # 0.
Elementary f i n c t i o n s
411
It is customary to write e" instead of exp(s) for any x E C. This is
justified by the fact that for r E Q, er = exp(r). Proof of this is left to
Exercise 14.2.2.
Exercises
@ Exercise 14.2.1
Prove: exp(x)exp(-2) = 1.
@ Exercise 14.2.2
Prove: exp(nx) = (exp(x))". Then use it repeatedly
to show exp(r) = er for r E Q.
Exercise 14.2.3 For f(z)= x1"exp(x2) find f'104'(0) and f(1003)(0).
[Hint: Use the expansion for exp(y) with y = z2.]
Find local extrema, points of inflection, intervals of
Exercise 14.2.4
convexity and concavity for f(x) = exp(-x2). Graph this function.
@ Exercise 14.2.5
For g ( x ) = (exp(z) - l)/x show that the discontinuity
at x = 0 is removable and find the Maclaurin expansion of 9.
Exercise 14.2.6
Find the following limits
1
(1) lim exp( -);
xyo
x
exp(l/x)
.
(2) lim
"10 1 exp( l/x) '
x 10-20exp(x) .
(3) lim
2+m 2 3
2 2 - 102 200'
+
+
+
+
(4) 2+00
lim exp(ax) for a E R. [Hint: Consider three possibilities for u.]
14.3
The logarithm
We have seen that the restriction of x H exp(x) to R is strictly increasing
on R. The inverse function to it exists and is called the natural logarithm
or simply the logarithm and is denoted by In. The graph is shown in
Figure 14.2.
We summarise the properties of In in the next theorem.
Introduction to Mathematics with Maple
412
Fig. 14.2 The graph of the logarithm
Theorem 14.3
The logarithm has the following properties
(i) it is defined on 10, oo[,is strictly increasing and maps 30, 00[
ontoR. ,
(iii) In 1 = O and In z
> 0 for x > 1; In x < 0 for 0 < z < 1; In e = 1.
(iv)
lim l n z
= 00,
X+OO
lim l n z = -00.
2-0
(v) For every x
> 0 and y > 0
+ In y,
(14.7)
In - = l n z - lny.
(14.8)
ln(zy) = In z
X
Y
Proof. (i) We already know that In is strictly increasing. By the inverse
function theorem rgln = domexp = R.
413
Elementary Functions
(ii) By Theorem 13.6 on the derivative of the inverse function
,
(iii) exp(0) = 1 hence In 1 = 0; the inequalities for In x follow from the
fact that In is increasing; exp(1) = e hence lne = 1.
(iv) follows from (i).
(v) Set x = exp(u) and y = exp(v). Then
Inxy = In (exp(u) exp(w)) = In (exp(u
+ w)) = u + w = lnx + In y.
The second formula follows by writing x/y instead of x.
0
Example 14.2 (Logarithmic differentiation) Sometimes it is easier
to evaluate Dln(f(x)) = f’(x)/f(x) rather than Df(x) and then find f’(x).
For instance,
1
1
In (f(x)) = - In (1 - x2) - - In (1
2
2
f’(4
- f(x)
+ x2)
X
-X
(1+x2)1
(1-22)
and after simplification
_. .
I
f’(x) = -
2x
(1 x2)di=?
+
Example 14.3 (Maclaurin series for the logarithm)
derivative of ln(1 + x) to obtain the expansion
We use the
The function
x 2 x3
L(x) = x - - + - - ...
2
3
(14.10)
+
has, for 1x1 < 1, the same derivative as ln(1 x), and since L(0) = In 1 = 0
we have that lnx = L(x) for 1x1 < 1. In other words the right hand side of
Equation (14.10) is the Maclaurin expansion of ln(1 x) for 1x1 < 1. We
now show t h a t the expansion holds also for x = 1. Using the fact that in an
+
Introduction to Mathematics with Maple
414
alternating series with descending terms the remainder does not exceed the
first omitted term, we have (for 5 > 0)
(14.11)
Using continuity of In and sending x -+ 1 first and then n -+
00,
we have
(14.12)
This interesting series, called the Leibniz series, converges very slowly. To
obtain In2 with accuracy of five decimal places we would have to add 100000
terms, a forbidding task. A better convergent series is obtained by replacing x
with -x in Equation (14.10) and subtracting the resulting series.
(14.13)
To calculate In2 we would set x = -1/3. The remainder after the k-th nonzero term r k can be estimated as follows:
Irk1
3-2k-1
3-2k+l
5 2 -2 k + 1 ( l + $ + . . . ) I
m.
A
To achieve accuracy of five decimal places we need Irk1 5 0.5 rough estimate suggests k = 4 or 5 and a calculator or Maple would confirm
t h a t k = 5 suffices. The series (14.13) was used in the past for calculating
logarithmic tables and although there is no need to have logarithmic tables now
the series is still useful.
Exercises
+ + x2). [Hint:
Exercise 14.3.1 Find the Maclaurin expansion of ln( 1 x
1
x2 = (1 - x3)/(1 - 4
.1
+ +
@ Exercise 14.3.2
Exercise 14.3.3
Show that In is convex everywhere where it is defined.
Find the following limits without using Maple.
415
Elementary finctions
(4) lim
z+e
14.4
lnx - 1
-.x - e
The general power
If a > 0 and r E Q then
ar = exp(r1na).
(14.14)
To prove this we define f(x) = xr exp(-r In x) for J: > 0. A simple calculation shows that f'(x) = 0 and therefore f(z)= f(1) for x > 0. Equation (14.14) follows by setting x = a and using Exercise 14.2.1. We now
use Equation (14.14) to define the meaning of ar for a > 0 and any r E R.
The symbol ar represents the exponential with base a and exponent r.
With this definition, Theorem 5.8 becomes valid for r E R and s E R. The
proofs are easy, as an example we prove part (v) of Theorem 5.8.
that is
aras = exp(rIna)exp(slna)
= exp ( ( r
+ s) In a ) = ar+'.
For a = e we have
et =exp(tIne) = exp(t),
(et)' = exp(rln(et)) = exp(rt) = ert.
For the In function the Equation (14.14) leads to
lnar = ln(exp(r1na)) = r l n a .
There are two functions naturally associated with Equation (14.14),
p : x + + x r for x > 0, r E R;
and
Ea :xI-+ ax for a > 0, x E R.
416
Introduction to Mathematics with Maple
We are familiar with p for a rational r and have already mentioned that
the usual properties expressed in Theorem 5.8 extend to real r. We prove
only the 'new' formula (a')' = rz'-l. Using the chain rule
A consequence of this formula is that Equation (13.38)' namely
Dn(I
+ x)"
= m(m - 1)
now holds for any real m, n E
- - - (m - n + 1)(1+ x
) ~ - ~ ,
(14.15)
N and x > - 1.
+
Example 14.4 (Maclaurin expansion of (1 x ) ~ )Using Equation (14.15) and the definition of binomial coefficients in (13.72) we are led t o
+ x)m
the following Maclaurin expansion of (1
(14.16)
+
and we want t o show that M ( x ) = (1 x ) ~ The
. series on the right-hand
side of (14.16) converges for 1x1 < 1 and diverges for 1x1 > 1 by the ratio test.
Hence we assume 1x1 < 1 for the rest of this example. Differentiating M gives
The coefficient of x k equals
rn
[("; '>
+
)I;:(
=m
J;(
+ z ) M ' ( z )= rnM(z). Using this we have
= (1 + q m - l [-mlM(z) + (1+ x ) M ' ( x ) ]= 0.
Consequently M satisfies (1
+
((1 2)-"M(z))'
Hence (1
+ z)-"M(x)
= M ( 0 ) = 1, and finally
Elementary Functions
417
We now turn our attention to the function Ea. The graphs of E, for
a = 5/9 and a = 9/5 are shown in Figure 14.3.
I
0
Fig. 14.3 The general exponential function
~
Theorem 14.4
~~
~~~~~~~~
The function x I-+ ax has the following properties
(i) (ax)'= ax In a
(ii) It is strictly increasing for a > 1 and strictly decreasing for
a < 1;
(iii) it is convex.
Proof.
Employing the chain rule again
(ax)'= [exp(zIn a)]' = exp(z In a ) In a = ax In a
The rest follows from (i) routinely.
0
The function inverse to E, is called logarithm with base a and denoted
by log,. It exists for a > 0, a # 1. For a = 10 it is called the common
Introduction to Mathematics with Maple
418
logarithm and denoted by log. Readers are most likely familiar with this
function whose defining property is x = 10'ogx. To obtain a formula for
log, we solve the equation y = ax with respect to x. Clearly lny = x l n a
and consequently
log, : t
In t
In a
-.
The properties of the function log, are similar to and proved by the properties of In; see Exercises 14.4.3 and 14.4.4.
Example 14.5 W e employ logarithmic differentiation t o find the derivative
of X : x H xx. We have
(14.17)
InX = x l n x ,
X'
- = Inx 1,
+
(14.18)
X
X' = xx((Inx+ 1).
(14.19)
Exercises
@ Exercise 14.4.1
Find the limit of (1
+ x ) ' / ~for x -+ 0. Compare with
Exercise 10.13.
Exercise 14.4.2
@ Exercise 14.4.3
Find the limit of x" as x -+ 0 and as x
-+
00.
If a > 0, b > 0, a # 1, b # 1, x > 0 prove
log,z = -
@ Exercise 14.4.4
If x, y, a are positive, a
log, xy = log, x
#
1, r E R, prove
+ log, Y,
X
log, - = log, x - log, Y,
Y
log, x' = r log, x.
Exercise 14.4.5
14.5
Find Dxfi.
Trigonometric functions
There was a good reason for restricting exp to R when dealing with logarithm. The function exp is not injective on C , indeed, it is periodic. A
419
Elementary Fbnctions
function f is said to be periodic with period p
# 0 if
+
(i) z E dom f + z p E dom f ,
(ii) f ( z p ) = f(x) for every x E dom f.
+
+
It is easy to see that if f is periodic then f(z mp) = f(z)for m E Z. The
periodicity of exp is a part of the next theorem. In the rest of this section
t denotes a real number.
Theorem 14.5
The exponential function has the following prop-
erties
(i) I exp(it)I = 1 for t E R.
(ii) There exist a smallest positive number, which shall be called T,
such that exp(z~/2)= 2.
(iii) z H exp(z) is periodic with period 2x2.
(iv) exp(2mz) = 1 if and only if z is an integer.
(v) For t E R the function t H exp(zt) maps IR onto the unit circle
in the complex plane.
Before we start proving the theorem we definelcost = %exp(it) and
sin t = 53 exp(it). It follows from the expansion of exp that
(14.20)
(14.21)
(sin t)‘ = cost
(14.22)
(cost)’ = - sint.
(14.23)
Proof. It follows from Equation (14.4) that exp(it) and exp(-it) are
complex conjugates. Therefore
I exp(it)12 = I exp(it)(lexp(-it)(
= I exp(0)l = 1.
(ii) For t = 2 the series (14.20) alternates and the terms decrease.
Consequently cos 2 < 1 - 2 16/24 < 0. There exists a to E [0,2] with
cost0 = 0, by the intermediate value theorem. For 0 < t < 2 the derivative (cos t)’ = - sin t < 0, hence cost strictly decreases. Therefore, t o is
uniquely determined and it is the smallest positive zero of of the function
+
‘Readers are most likely used to a different definition. However, later in this section
we reconcile our definition with the ‘school’ definition.
Introduction to Mathematics with Maple
420
cos. We denote it n/2. By Equation (14.22), the function sin increases
on [O, n/2] and by (i) we have sinn/2 = 1. So we have exp(zn/2) = 2. It
follows by successively using Equation (14.2) that exp(nz) = zz = -1 and
exp(2nz) = (--l)(-I) = 1.
(iii) now follows from Equation (14.2). (iv) Let x = %z, y = S z . Since
it follows that x = 0, since exp is increasing on R. For an integer n we have
exp(2nnz) = exp(4nm/2) = 24n = 1. Since y = LyJ t with 0 5 t < 1, it
suffices to show that exp(2tnz) = 1 implies t = 0. If exp(2tnz) = 1 then
exp(tnz) = exp(-tnz) and since these are complex conjugates it follows that
Sexp(tnz) = sin(tn) = 0. However sintn > 0 or 0 < t 5 1/2, for2 t > 1/2
also sintn = cos(t - n/2) > 0. The only possibility is t = 0.
(v) Let u = a zb, IuI = 1.
+
+
(vl) Consider first the case a 2 0, b 2 0. Since cos is continuous and
decreasing on [0, n/2] there exists t o with cos t o = a. Then
+
def
b = sinto > 0 and a zb = exp(zt0) = u.
(v2) Let a < 0, b 2 0. By (vl) there exists tl with
+
Consequently u = exp(z(t1 n/2)).
(v3) If b < 0 then by (vl) or (v2) there exists t 2 with -u = exp(zt2) and
then u = exp(z(t2 n)).
+
Given a2
0
+ b2 = 1 we proved, in part (v), the existence of $ such that
cos$ = a
sin$ = b
-n<$<n
Part (iv) asserted the uniqueness of $. For a complex number z # 0 we
have z = IzI(cos 4 zsin$). In 4 we recognize the argument of z from
Section 7.2.4 and in particular from Equation (7.18). Now we have filled
the gap from that subsection by proving the existence and uniqueness of
argument (2).
+
~~
2cos(t - n/2) + zsin(t - n/2)= exp((t - n/2)2) = -zcost
~~
+ sint.
421
Elementary Functions
We have proved that
e2.rra -
1.
(14.24)
This is an interesting relation which ties together four of the most important
numbers of mathematics, e, 7r, z and 1.
We know that eit and e-it are complex conjugates, so it follows that
eat + e-zt
cost =
2
eat
sint =
- e-at
(14.25)
'
(14.26)
22
These are known as the Euler formulae.
All properties of trigonometric functions follow easily from our definitions or from what we have already proved. We have established that sin
and cos are periodic with period 27r. We have also proved
sin2 t
+ cos2t = 1.
+
The formulae for sin(z y) and cos(z
comparing real and imaginary parts in
cos(z
(14.27)
+ y) with z, y E IW are obtained by
+ y) + zsin(z + y) = exp(z(z + 9))
+ sin Y)
= cos z cos y - sin z sin y + z(sin z cos y + cos z sin y)
= exp(zz) exp(zy) = (cos z
+
2
sin z)(cos y
2
(14.28)
For some other formulae for trigonometric functions see Exercise 14.5.1.
The graph of sin and cos (with other trigonometric functions) are shown in
Figure 14.4.
Example 14.6
Limit of (sin(2z3
+ z 5 ) / z ( l - cosz) can be easily found
by
4
Without Muple, either Theorem 13.19 or power series can be used. The
latter is far more advantageous and we leave it as an exercise to compare
both solutions.
31t is important to enclose the whole denominator in (), otherwise an insidiously
wrong result is obtained.
Introduction to Mathematics with Maple
422
3 = tanx
sin x
= cosz
K
0
/
27r
cotx
Fig. 14.4 Trigonometric functions
The function tan can now be defined as
sin x
tanx = cos x ’
and all its properties developed using sin and cos. The graph is shown in
Figure 14.4.
lim tant = -00
t+-.lr/2
and
lim tant = 00
t-iIr/z
423
Elementary finctions
Further
1
(tanz)' = 1 tan2x = cos2 x *
+
We define
def
cotx =
cos x
sin x
and have
(cot x)' = -(I
+ cot2 x) = -I/
sin2x.
Since cot x = - tan(a: - n/2) the graph of cot is the reflection of the graph
of tan on the y-axis and a shift by n/2 to the right; see Figure 14.4.
The school definition
In the x,y plane we construct the unit circle (see Figure 14.5). Moving
from A = (1,O) in the anticlockwise direction we mark a point B on the
circle with the length of the circular arc between A and B being a!. Then
we define cos a! to be the x-coordinate of B and sin a! to be the y-coordinate
of B. We call this the school definition.
sin a
Fig. 14.5 The school definition
Comparing Figure 14.5 with Figure 14.6, where exp(it) and sint and
cost are depicted according to our definitions, we see that in order to show
that both definitions agree, it is sufficient to show that t is the length of
the circular arc between 1 and exp(tz). We divide the interval [ O , t ] into n
Introduction to Mathematics with Maple
424
Fig. 14.6 Definition of sin and cos.
parts by dividing points kt/n, k = 1 , 2 , . . . n. The length of the polygonal
line inscribed in the circle between 1 and exp(it) is
(k - 1)tz
k=l
Since
= lexp
(E &)
-
z l Iexp
(2) (g)I (&)1
- exp
= 2 lsin
Since 0 < t/2n < 7r/2 for large n , the absolute value on the right hand
side of the last equation can be omitted. It is an easy consequence of the
definition of sin or of Equation (13.54) that
lim
x+o
sin xt
=t
x
and consequently
In = 2nsin
(k)
-+
t
as n + oo.
As n increases without limit the length of the polygonal line In tends to the
length of the circular arc between 1 and exp(tz).
425
Elementary Functions
Exercises
@
Exercise 14.5.1
Prove the following formulae for x , y E R:
sin 2t = 2 sin t cos t ,
cos 2t = cos2 t - sin2 t ,
/dn
/F,
x-y
x+y
sinx - sin y = 2sin -cos 2 '
2
x+y
2-y
c o s x - c o s y = -2sin-sin-.
2
2
Exercise 14.5.2
Find lim
t-4
cos 5t - cos t
1 -cost
The function f : t H t2 sin( l/t) for t # 0 with f(0) = 0
has a derivative for all real t. The derivative is discontinuous a t 0. Prove
it!
@ Exercise 14.5.3
14.6
Inverses to trigonometric functions.
The function sin is strictly increasing and continuous on [-7r/2, 7r/2]. The
restriction4 of it to this interval is also continuous and strictly increasing
and maps [ -7r/2,7r/2] onto [ - 1,1]. Its inverse is denoted by arcsin. We have
sin(arcsinx) = x for every x but arcsin(sinx) = x only for x E [-7r/2,7~/2].
Graphs of sin and arcsin are shown in Figure 14.7.
We now prove
1
Darcsinx = - for
JiT2
1x1 < 1.
(14.29)
By the theorem on the derivative of the inverse we have
1
d arcsin x - --1
-dx
dsiny
COSY'
dY
41t is a common practice to denote this restriction also by sin. When dealing with
the inverse it is important to realize that it is an inverse to the restriction of sin to
[-n/2, .-/2].
Introduction to Mathematics with Maple
426
lr
-
2
y = arcsinz
7T
--
2
0
1
$
-1
Fig. 14.7 sinz and arcsinz
Now siny = z, hence cosy = & d m .Since -7r/2 < y < 7r/2 it follows
that cosy > 0, hence cosy = d
m and (14.29) is proved.
The function cos restricted to [O,n] is strictly decreasing. Its inverse
is called arccos, which is strictly decreasing, continuous, and maps [-1,1]
onto [0, T ] . The graphs of arccos and cos are shown in Figure 14.8.
For the derivative we have
1
D arccos z = - -
dc-?.
The proof is similar to establishing (14.29).
The function tan is strictly increasing on ] - ~/2,7r/2[and continuous.
The inverse to tan restricted to this interval is called arctan. It is strictly
increasing, continuous, and maps Iw onto ] - 7r/2,7r/2[. The graphs of tan
Elementary finctions
1
I
-1
0
1
-1
Fig. 14.8 cosz and arccosz
and arctan are shown in Figure 14.9. For the derivative we have the formula
d arctan x _-- 1 - cos2y
X
d tan y
dY
=
1
1
1 tan2 y
1 22'
+
+
(14.30)
The function cot restricted to ]O,n[is strictly decreasing. Its inverse
is called arccot, which is strictly decreasing, continuous, and maps R onto
]0,7r[. The graphs are shown in Figure 14.10.
To obtain the Maclaurin expansion of arctanx is easy. As we have done
before we use the expansion for the derivative.
Darctanx = 1 - x2
+ x4 - x6 +
.
(14.31)
The power series
x - - +x3- - - +x5. . . x7
3
5
7
(14.32)
428
Introduction to Mathematics with Maple
7r
-
2
Fig. 14.9 tan x and arctan x
has, for 1x1 < 1, the same derivative as arctan and both function are equal
at x = 0, so they must be equal for 1x1 < 1.
x3 x5 x7
arctanx = IC - - - - - - . .
(14.33)
3
5
7
By the same method we used to prove the Leibniz series for log2 we
can prove that this equation also holds for x = 1, obtaining
+
1 - -1- + .1. . .
-7r = 1 - - +
4
3 5 7
+
(14.34)
This series converges very slowly. Exercise 14.6.6 contains a guideline for
efficient evaluation of 7r.
429
Elementary Functions
; y=cotx
0
Fig. 14.10 cot x and arccot x
Exercises
Use Maple to graph arcsin(sin(x)) on [-37r, 3 ~ 1 .
Exercise 14.6.1
@ Exercise 14.6.2
Prove the following identities
+ arccosx = -IT2 for
1 7 r
arctanx + arctan(-) = x
2
1
7r
arctanx + arctan(-) = -2
arcsinx
-
1 5 x 5 1;
for x > 0;
for x
< 0;
X
arctanx = arcsin
@ Exercise 14.6.3
Exercise 14.6.4
X
~
diT2
for
x E R.
Prove that: Darccot x = -l/(l
Find f' for
+ x2).
Introduction to Mathematics with Maple
430
@ Exercise 14.6.5
[email protected]
arctan x
- 1.
x
Prove lim
x+o
Exercise 14.6.6
By the previous Exercise 4 x arctan 1/5 E 4/5 M n/4.
Set a = arctan 1/5 and ,O = 4 a - 7r/4. (It can be expected that ,O and tan ,O
would be small, and the series for the arctan would converge quickly.) Show
that tan @ = 1/239,7r/4 = 4 arctan 1/5 - arctan 1/239. To evaluate 7 ~ / 4to
ten decimal places it suffices to take seven terms in the series for x = 1/5
and three terms for x = 1/239. Prove this and and confirm it numerically
with Maple.
14.7
Hyperbolic functions
The following functions are often used in applications. From a purely mathematical point of view they offer nothing new, interestingly they display
striking similarities with trigonometric functions, although in some formulae the sign
and - are opposite to what they are in corresponding
formulae for trigonometric functions. We have
+
ex - e-"
2
'
def ex
e-"
tanh =
ex e-x'
cosh2x sinh2 x = C O S
def
def
cosh x =
sinhx =
+
+
ex
+ e-"
2
'
cosh2x - sinh2x = 1,
+
(sinhx)' = coshx,
1
(cosh 2) = sinh 2,
(tanhz)' = -.
cosh2x
Maple knows hyperbolic functions. For instance
>
~ ~ X ,
expand(sinh(2*x)) ;
2 sinh(x) cosh(x)
Exercises
Exercise 14.7.1 Show that sinh is strictly increasing on R and find an
explicit formula for its inverse.
Exercise 14.7.2
Find D
(-
cosh x
sinh2 x
+ In (tanh
z))
.
Chapter 15
Integrals
In this chapter we shall present the theory of integration introduced by the contemporary Czech mathematician J. Kurzweil.
Sometimes it is referred t o as Kurzweil-Henstockl theory. The
first modern integration theory was developed by the German
mathematician B. Riemann. His definition of an integral was similar but more innovative than the one developed previously by
A. L. Cauchy. Riemann made a decisive steps forward by considering the totality of functions which can be integrated and building a
systematic theory. Initially Riemann’s theory was a great success.
Later it was found deficient in many aspects. However, unfortunate as it may be, it is this theory which is usually presented to first
year classes and dealt with in books on calculus. Our presentation
follows, in parts, Lee and Vfbornq (2000, Chapter 2).2
15.1
Intuitive description of the Integral
We considered tagged division of intervals in Section 12.5 of Chapter 12,
particularly in Inequalities (12.15). Throughout this chapter T will be a
set of dividing points of an interval [a, b]
a = to
< tl <
t,
=b
and X the set of tags X I , E [ t k - l , t k ] . The symbol T X will denote the
tagged division with dividing points T and tags X .
Henstock is a contemporary British mathematician who made significant contributions.
2We recommend this book for further reading on the theory of the integral.
431
432
Introduction to Mathematics with Maple
i
i
Definition 15.1 (Riemann Sum) With a function f : [u,b]H C
and a tagged division T X we associate the sum
n
R(f,T X ) =
1f ( X i ) ( t i
- ti-1)
i=l
This sum is called a Riemann sum, or more explicitly the Riemann
sum corresponding to the function f and the tagged division T X .
If no confusion can arise we shall abbreviate R ( f , T X ) to R ( f ) or
R ( T X ) or even just R. We shall only do this if the objects left out in
the abbreviated notation (like T X in R ( f ) )are fixed during the discussion.
Fig. 15.1 A Riemann sum
The geometric meaning of R is indicated by Figure 15.1 for a nonnegative function f . The sum R is simply the area of the rectangles which
intersect the graph of f . Our intuition leads us to believe that, for a
non-negative f , as a tagged division becomes finer and finer the sums R
approach the area of the set {(z,y); a 5 z 5 b, 0 5 y 5 f(z)}.
Integrals
433
A word of caution is needed here. Firstly, it is fairly clear what the
area of the interior of a simple polygon should be. However, the concept of
an area of an arbitrary set in R2 has to be defined and the theory of area
of sets in R2 (or R”) should be developed-we cannot merely rely on our
intuition.
Just reflect on the following question: what is the area of the set S =
{ ( I I : , ~ )0; 5 x 5 1, 0 5 y 5 1, y E
The set S is fairly complicated
and some readers may have no idea as to what the area of S should or
could be. If we try to graph the set S we would blacken the whole square
Q = {(x,y); 0 5 II: 5 1, 0 5 y 5 l} despite the fact that points (x,y)
with irrational y do not lie inside S. Since S is so dense in Q, one may
conjecture that the area of S is the same as the area of Q, namely 1. Such
a conjecture would be completely false.
The question of how and when the sums R ( f ,T X ) approach a limit as
T becomes finer and finer can be studied independently of their geometric
meaning. That is the goal of this chapter.
a}.
A tentative attempt at definition of the integral
We shall say that the function f is “integrable” if the sums R ( f , T X )
approach a number I as T X becomes finer and finer. I is called the integral
of f from u to b and is denoted by
f . One can also say that R ( f ,T X )- I
becomes arbitrarily small when T X is sufficiently fine.
The notation
f is unambiguous and in theoretical discussions preferable. However, in concrete situations when the function f is given by a
formula, for instance f(x) = x3,another notation is more common, namely
b
one writes la
f(x) d x instead of
f , for instance Jab x3 d x.
The letter x in the symbol
f(x)d x is called the variable of integration. It can be replaced by other letters without altering the meaning of
the symbol. Hence
f(x)dx = f ( u )d u = Jab f ( a )d a .
The reasons for using the notation
f(x) d x are historical and of convenience. We shall see later that this notation facilitates the use of an
b
important theorem. Also, in (x2 t ) d x, the symbol d x indicates that x
s,”
s,”
s,”
s,”
s,”
s,”
sa
s,”
+
b
+
is the variable of integration and t is a constant, for example, Ja (x2 t ) d x
denotes
f , where f : x H x2 t.
s,”
+
Example 15.1
Let f be a constant function f(x) = c on [u,b]. For any
tagged division T X ,
Introduction to Mathematics with Maple
434
n-1
n-1
i=O
i=O
All the sums R ( f , T X ) are equal t o c(b - a ) , regardless of the division T and
b
b
f = c(b - a ) or c d x = c(b - a). For c = 1 we
the tags X ,so clearly
b
haveJaldz=Ja
b
s,
s,
dz=b-a.
Maple has several commands for Riemann sums. They are leftbox,
rightbox and middlebox for plotting the function and the rectangles representing the Riemann sums. There are corresponding commands leftsum,
right sum and middlesum for numerical evaluation of Riemann sums.The
left, right and middle indicates that the tags are at the left-end, right-end
and in the middle of the subintervals. All six commands require the package
student to be loaded. The basic interval must be given and, optionally,
the number of subintervals as well. If the optional number of intervals is
not given Maple subdivides into four subintervals.
>
with(student1:
>
leftbox(x^3,x=0. .2);rightbox(x^3,x=O..2) ;
X
Fig. 15.2
X
Leftbox and a rightbox for a Riemann sum
Integrals
435
For a monotonic function the middlesum lies between the leftsum
and the rightsum and gives a better approximation. Increasing the
number of intervals from four to ten improves the approximation.
> rniddlebox(x^3,x=O. .2);middlebox(x'3,x=O.
.2,10) ;
6
4
2
0.2
0.4
0.6
0.8
1
12
1.4
1.6
18
2
O'
02
04
X
06
08
1
12
14
18
IS
2
X
Fig. 15.3 Middleboxes for Riemann sums
The command middlesum evaluates the Riemann sum.
>
middlesum(x^3,x=O. .2,10);
>
evalf(%>;
3.980000000
The correct value is 4. Let us try a division with 100 subintervals.
>
evalf (middlesum(x-3,x=O . .2,100) ) ;
3.999800000
In the next example we look at general tagged division with arbitrary
tags.
Introduction to Mathematics with Maple
436
Example 15.2
If f(x) = x on [a,b] then the evaluation of R ( f ,T X ) is not
obvious and R depends on the tagged division T X . However, for a specially
chosen XI say X = Y with
it is easy to evaluate R ( f T
, Y ) for any T .
R(fT
, Y )= f
c
2 i=l
. 1
(t?- t?-l)
+ t;
=
(tf - ti
2
=
- (b2 - a2) ,
-
tf
+
2
* * *
- tn-2
2
+ t; - tn-l)
1
2
We now show that if T is sufficiently fine then R(f,TX)-for
any Xis arbitrarily close to (b2 - u2)/2. We estimate R ( f , T Y )- R ( f , T X ) =
C:==,(yZ
- &)(ti - t i - 1 ) . We define X = Max((ti - ti-& i = 1 , 2 , . . . , n ) ,
then clearly 1% - Xi1 5 X and
If X is small then R ( f TX)
,
is close to R ( f T
, Y ) = (b2 - u2)/2. As X becomes
smaller the tagged division becomes finer and R ( f ,TX) approaches i ( b 2 - u 2 ) .
Hence
[f
= [xdx
= Z1( b 2 - a2).
We shall try to obtain more insight into the approach of the Riemann sum
to a limit by considering one more example.
Example 15.3
Let f(x) = 1-
[-
1x2] , and [a,b] = [-1, I]. Since f(x)
+
1
is 1except for x = 0, geometric intuition tells us that J-l f = 2.
The Riemann sum R ( f , T X ) depends on whether or not 0 is a tag. We
437
Integrals
distinguish three cases:
(i) X I , # 0;
(ii) XI, = 0
no subinterval is tagged by zero;
3
Xi # O
only the interval [ t k - l ,
i # k;
t ~ ,is]tagged by zero;
for
(iii) X I , = XI,+^ = 0; zero tags two adjacent intervals.
How can we ensure by one simple condition that
I R ( f , T X )- 21 < E .
(15.1)
Define a gauge 6 on [-1,1] by
S(x) = 2
for
x#0
E
S(0) = 2
If the tagged division T X is S-fine then the length of the interval which is tagged
2 Equation (15.1) is satisfied. A tagged division is
by zero is less than ~ / and
sufficiently fine if it is 6-fine. This is the key for making our tentative definition
of the integral precise.
Remark 15.1
In the above example it was convenient to have some intervals smaller than others. If one looks a t Figure 15.1 such a requirement is
quite natural. When the function t o be integrated is unbounded the requirement that some intervals in the tagged division are substantially smaller than
others becomes essential. We shall see this later in Example 15.4.
Exercises
@
&
With notation as in Example 15.2 if X < -show
b-a
that I R ( f , T X ) - $(b2 - a2)1 < E .
Exercise 15.1.1
Exercise 15.1.2 Use Maple’s boxes for sin x on the interval [0, 27r]. Use
the middlesum with 500 subintervals.
0 Exercise 15.1.3
Given E > 0, find, for the function f : x
1x1 - 1x1
and the interval [0, 101, a gauge S such that for every S-fine tagged division
T X the inequality I%L(f, O X ) - 101 < E holds. [Hint: For an integer n let
S(n)= e/22.]
Introduction to Mathematics with Maple
438
15.2
The definition of the integral
When attempting to define the limit of Riemann sums it is easy to make
precise that part of the informal statement concerning the smallness of
R - I . We can say: The number I is the integral of f if for every positive
E we have l R ( f , T X ) - I ( < E for all sufficiently fine tagged divisions T X .
The difficulty lies in making precise the phrase suficiently fine.
In Example 15.2 it was good enough to interpret the phrase T X is
suficiently fine to mean that the length X of the largest subinterval [ti+l,ti]
was smaller than & / ( b- a ) . This can be rephrased as: the tagged division
&
is 6-fine with 6 =
. (See also Exercise 15.1.1). In Example 15.3 it
2(b - a)
was convenient to require that some of the subintervals of a tagged division
were substantially smaller than others and this was ensured by requiring
that the tagged division was &fine, with the function 6 defined in that
example. So we can say that in all our examples, sufficiently fine meant
&fine with a suitable gauge 6.
We shall abbreviate the saying that T X is &fine to T X << 6.
~
\
/Definition 15.2 (Integral) The number I is said to be the integral
of f from a to b if for every positive E there exits a gauge 6 such that
I n ( f J X )-I I < E
whenever T X << 6. If a number I satisfying this condition exists then f
is said to be Kurzweil integrable, or just integrable on [a,b]. We shall,
b
of course, keep the notation introduced earlier, namely I = Ja f =
s,:
f (4d x-
Remark 15.2
function then
I
If the gauge in the above definition is a constant positive
f is said to be Riemann integrable.
Theorem 15.1
There is at most one number I satisfying the condition from Definition 15.2.
Proof.
Assume that for every positive E there exists a gauge 61 such that
I n ( f , T X )- I I < &
(15.2)
whenever T X << 61, and also that there exists a gauge 62 such that
l R ( f , T X )- JI
<&
(15.3)
Integrals
439
whenever T X << 62. Let 6 ( x ) = Min(61(2),62(2)) and T X
both Equations (15.2) and (15.3) hold and consequently
By letting
E
+ 0 we obtain
S. Then
0
11 - JI 5 0, that is I = J .
It follows from considerations in Examples 15.1, 15.2 and 15.3 that
[
(15.4)
cdx = c(b - a ) ;
1
[ x d x = --(b2
(15.5)
- a2);
(15.6)
To satisfy Definition 15.2 formally it is sufficient to choose S(x) = 1 for
&
(15.4) by Example 15.1, S(x) = -for (15.5) by Example 15.2, and S
2(b - a )
as defined in Example 15.3 for (15.6).
Example 15.4
k2 for k
Let
f ( x )= 0 for x
E
[0,1], x
# l / k , k E N and f ( l / k )
Jt f
N.We show that f
is integrable on [0,1] and
positive E choose S(l/k) = 2-k-2k-2~ if x = l / k , for k E
otherwise. Let TX << 6 then
E
=
= 0. For every
N,and S(x) = 1
where S is the sum of finitely many numbers which have the form f ( X i ) ( t i &-I), each is zero or is less than k22S(l/k) 5 2-"'~ and there are a t most
two terms for the same k. Clearly we have that S < 2~
some positive integer N . Consequently S
<E
and
So1 f = 0.
b
Example 15.5 We prove that 1~ is integrable, and t h a t Ja 1~ = 0. Note
that IQ differs from a function identically equal to 0 only a t rational points.
Let T ~ , T z , T ~ ., .. be an enumeration of all rational numbers in [a, b]. Let E be
a positive number. Define S as follows
S(x) = 1
if x is irrational,
E
6(fk) = 2k+2
for
i = 1 , 2 , . . ..
440
Introduction to Mathematics with Maple
If T X << S we have
c
n
0 5 R(TX)=
(15.7)
lQ(Xi)(ti
- ti-1).
i=l
The right hand side of this equation is a sum of finitely many numbers which
have the form l~(Xi)(ti
- ti-l), each is zero or smaller than 2 ~ / 2 ~for+ ~
some k . There are a t most two terms for the same k . Similarly to the previous
example we have that S
<E
(2 + - - + ’>
- for some positive integer N.
-
2N
Hence R(TX)5 E . This together with Inequality (15.7) proves t h a t
exists and equals 0.
s,” IQ
The function from this example is often quoted in textbooks as an example of a non-integrable function. This happens because authors of those
textbooks use the Riemann definition of integral. A great advantage of the
Kurzweil integral is that practically every bounded function is integrable.
Example 15.6 (An integral of an unbounded function)
We wish
to prove t h a t
(15.8)
where f(z) =
S(0) = E
~ Let
.
1
- for
fi
4>
E
z
#
0 and f(0) = 0. Define 6(z) = E X for x
#
0 and
> 0 and
be a S-fine partition of [O,A].
Note that this choice of S implies
t l - t o < 2~x1
< XI).Since
X1
= 0 (if
X1 >
0 then
X1 5 tl
=
and
31t can be shown with the axiom of choice that there is a bounded function which is
not Kurzweil integrable. However, nobody was able to explicitely define one, so there is
no chance of encountering one in applications.
441
Integrals
0.f
OS
Y
0.l
02
0
02
04
06
08
1
Fig. 15.4 A Riemann sum for a &fine tagged division
for Xi
# 0, we have
(15.9)
For an estimate from below note that
for i
2 2.
Hence
n
i=2
n
2 2drEX(&- V
G )
i=2
=
2Ji--E(&i
- E).
(15.10)
Introduction t o Mathematics with Maple
442
E is arbitrary, Equation (15.8) follows from Inequalities (15.9) and
(15.10). We programmed Maple t o construct a &fine partition and the corresponding Riemann sum with 6(0) = 0.0001 and 6(x) = 0.32. For a better plot
we used f / l O instead of f. Maple divided the interval into 43 subintervals
and calculated the Riemann sum R = 0.198134. The graph is shown in Figure 15.4. By contrast middlesum and rightsum produced with 43 subintervals
0.190776588 and 0.1788903398
Since
If a < c < b and 6 is a gauge on [a, b] then there exists
a 6-fine tagged division having c as one of its dividing points. To see this it
is sufficient t o find 6-fine tagged divisions of [a,c] and [c, b] and then merge
them together.
Remark 15.3
1
Example 15.7
Let f be defined by f(x) = - for x # 0 and f(0) = 0.
22
We show that f is not integrable on [0,1]. Assume, for an indirect proof, that
it is. Then there exists a number K and a gauge 81 such t h a t
whenever TX << 61. Choose a such that 0 < a < 1, and l/a > 2K, and
define S(x) = Min(61(x), a / 4 ) for x E [0,1]. Let TX << 6 be a tagged division
of [0,1] containing a as one of the dividing points, say a = t j for some j. Then
TX is also 61 fine and therefore Equation (15.11) holds. On the other hand
Xi 5 Xj 5 a for i 5 j and therefore
1
and since t l
< X1 + S(X1) < 2a/4
= I > K .
2a
This contradicts Equation (15.11) and proves that
f is not integrable.
443
Integrals
15.2.1
Integration in Maple
Maple evaluates integrals efficiently and with ease. The int 0 function can
be used to calculate integrals. The range of integration needs to be specified
and then the integral is calculated. If the integral can be calculated exactly,
.I
2
then Maple does so. In the next Maple session we calculate
JdzT
3x2dx,
2
sin2x d x and
>
x3ex2dx .
int(3*xn2,x=0..2);
8
>
int ( (x-3)*exp(xn2), x=-1 . .2) ;
3
2
- e4
Sometimes Maple cannot calculate the integral exactly. That is, the
integral must be calculated numerically. If Maple cannot calculate the
integral exactly, then rather than giving an answer, Maple displays the
expression to be integrated with an integral sign, and does no calculations.
We can use the evalf 0 function to obtain a numerical answer. Consider
following example.
We wish to evaluate
[A
d x . This integral cannot be evaluated
exactly, so Maple does no calculations. Instead, the integral is rewritten.
>
int(i/(x+exp(x))
,x=O..I);
Use the evalf 0 function to give a numerical answer.
>
evalf(%);
.5163007634
444
Introduction to Mathematics with Maple
Exercises
Exercise 15.2.1 Let f ( x ) = 0 for all x E [a,b] except possibly for x = c,
and a 5 c 5 b. Prove that f is integrable and
l
f = 0.
[Hint: This is obvious if f(c) = 0, otherwise take S ( x ) = 1 for x
S(C) = A.]
2(lf (.>I
# c and
@ Exercise 15.2.2 Prove that the characteristic function of an interval
[a,p] C [a,b] is integrable and its integral from a to b is p - a. [Hint:
Define S(x) = Min(1x - a [ ,Ix - PI) for x # a , x # p; S(a) = S(p) = e/4.]
@ Exercise 15.2.3
1
Let f ( x ) = - for
22
J:
# 0, and f(0)
= 0. Show that f
is not integrable on [-1, 11. [Hint: Do not work hard, use Example 15.7.1
@ Exercise 15.2.4
Prove that g : x H f ( x + A ) is integrable on [a-A, b-A]
if and only i f f is integrable on [a,b]. [Hint: Use S for F to define 6 for 9.1
Let A > 0. Prove that 9: x H f ( A x ) is integrable on
[ a / A ,b/A] if and only if f is integrable on [a,b]. State a similar result for
A < 0 . [Hint: Use S for f to define S for 9.1
@ Exercise 15.2.5
Exercise 15.2.6
integral.
Prove the following formula using the definition of
l
cos z dx = sin b - sin a.
Il
n
~ ( C O S- X
cosyi)(ti
~
- ti-1) . Since I cosxi
- cosyil
5 Ixi
- yil by the
445
Integrals
15.3
Basic theorems
The next few theorems are similar to theorems on limits of sequences or
functions.
Theorem 15.2 If f is integrable on [a,b]and c f R, then cf is
integrable on [a,b] and
S”.f cS”
=
a
f.
(15.12)
a
Proof. If c = 0 the theorem is obvious. Let c # 0,
positive function S such that
E
> 0. There exists a
whenever TX << S. It follows that
If cf is integrable and c
Equation (15.12) holds.
Remark 15.4
Theorem 15.3
and
# 0 then f
is also integrable and
Iff and g are integrable on [a,b] then so is f
+g
(15.13)
Proof.
that
For every positive
E
there exist positive functions 61 and 62 such
(15.14)
for T X << 61, and
(15.15)
Introduction to Mathematics with Maple
446
for T X << 62. Define 6(x) = Min(&(x), 62(2)). If T X << 6 then Inequalities
(15.14) and (15.15) hold and since R(f g , T X ) = R(f , TX) + R ( g , TX),
we have
+
Remark 15.5
It is an easy exercise to extend Formula (15.13) to a sum
of n functions.
If S is countable and f(x) = 0 for x E [a,b]\ S
then f is integrable and f = 0.
Theorem 15.4
s,”
The proof combines ideas from Examples 15.4 and15.5.
Proof. Obviously we can assume that S is enumerable, (otherwise we
enlarge it by joining it with an arbitrary enumerable part of [a,b ] ) . Let
n H sn be an enumeration of S. Define
C
S(sn) = 2”+2(1 f ( S J
+ 1)’
and S(12;)= 1 for x
4 S.
In a Riemann sum denote by
f ( X i ) ( t i-&-I) the sum of terms for which
X i E S , and C ” f ( X i ) ( t i- ti-1) the sum of terms for which Xi 4 S;clearly
C ” f ( X i ) ( t i - ti-1) = 0 and therefore
R(f,TX) = C ’ f ( X Z ) ( t i - ti-1).
If TX << S then (si can tag two adjacent intervals)
This proves f is integrable and J: f = 0.
0
Let f be an integrable function on [a,b] and let g
be a function which differs from f only at points of a countable set
S C [a,b]. Then g is integrable and
Theorem 15.5
+
Proof. Since g = f (g - f ) , this theorem is an immediate consequence
0
of Theorems 15.3 and 15.4.
Integmls
Remark 15.6
447
The last theorem can also be expressed as follows: A change
in the definition of a function4 f a t countably many points affects neither the
b
f. This is used very often; it also allows us to
existence nor the value of
assign a meaning to the integral of a function which is not defined for all x in
the basic interval.
s,
For example,
f
-1
z d x=
1x1
l2
f(x)dx, where f(x) =
-1
f (0) = 0 (or something else). Therefore,
Theorem 15.6
1x1
J_: idx = 1.
I f f and g are integrable on [a,b] and f 5 g then
[f
Proof.
X
- for x # 0 and
I[ 9 .
For every positive E there exist gauges 61 and 62 such that
whenever T X is &-fine, and
[g-&<R(g,TX)<
(15.17)
whenever T X is &-fine. Let 6(x) = Min(&(x),62(x)). If T X is 6-fine then
(15.16) and (15.17 hold simultaneously. Since R ( f , T X ) 5 R ( g , T X ) , it
follows from (15.16) and (15.17) that
Letting
E -+ 0
completes the proof.
0
Corollary 15.6.1
Iff and g are integrable and f (x)5 g(x) for all
x E [a,b] except on a countable set then
*Strictly speaking, “change in the definition of the function” defines a new function,
but such points are usually ignored.
448
Introduction to Mathematics with Maple
Corollary 15.6.2
I f f is integrable and f 5 M on [a,b]for some
M E R, then
IM(b-a).
[f
Similarly,
b
m(bfor an integrable f satiseing f
Corollary 15.6.3
then
Proof.
4 I J, f ,
2 m on [a,b].
I f both the functions f and
If I
are integrable
We have
-If1 I f I
If1
and therefore
as required
0
Warning: Even if f is integrable, the function If I need not be. An
example of such a function is given later in Example 15.17
Remark 15.7
If 6(x) I x - a for x > a then the first tag X 1 of any
&fine tagged division must be a. Indeed, assuming that a = t o < X1 I t l , we
have XI - a I tl - t o < S(X1) 5 X1 - a , a clear contradiction. Similarly, if
d(x) < b - x for x < b, then for a &fine tagged division of [a,b] the last tag
X , is equal to b. Further, if S(x) 5 Ix - CI for x # c and a < c < b, then for
any &fine P-tagged division of [a,b] there exists an integer j such that X j = c.
We shall refer t o choosing S in such a way that c becomes a tag of any &fine
tagged division as anchoring the tagged division on c.
Integrals
449
It is now easy to see t h a t if S is a finite set in [a,b] then there exists a
positive function 6, such that every 6-fine tagged division T X is anchored on
S-by that we mean that all points of S become tags.
Theorem 15.7
If f is integrable over [a,c] and [c,b] then f is
integrable over [a,b] and
Proof. For every positive E there exist positive functions 4 and such
that if T X is a &fine tagged division of [ a , ~and
] TX is a J-fine tagged
division of [c, b] then
(15.18)
and
&
<-
2'
Let
6 = Min(&(z),(c - x)), for a 5 z < c;
6 = Min(z(z), (x - c)), for c < z 5 b;
6(c) = Min(X(c)J(c)).
Clearly,
(15.19)
Introduction to Mathematics with Maple
450
and
Hence by (15.18) and (15.19)
In the proof of Theorem 15.7 we split the Riemann sum into two, one
for the interval [ a , ~and
] another for [c,b]. This was possible because we
anchored the tagged division on c. This trick is very useful and we will
use it without further explanation in future. If f is integrable on [a, b]
then it is integrable on any subinterval of [a, b]. A simple proof uses the
Bolzano-Cauchy principle which we consider in the next section.
A function f is called a step function on [a,b] if there exists a division
T E { a = to < tl < - < i?n = b } such that f is constant on ] t k , t k + l [
for k = 0, 1, . . . ,n - 1. Every step function is a linear combination of
characteristic functions of intervals and one-point sets. The next theorem
is a direct consequence of Theorems 15.2, 15.3, 15.4, 15.7 and integrability
of constant functions.
Theorem 15.8
Every step function is integrable.
Exercises
Exercise 15.3.1 Show that the functions 1x1 and g(z) = (z - 1
integrable on [l,31.
~ 1 are
)~
Exercise 15.3.2 Iff is integrable on [al,a2],[az,a3],. . . , [an-l, an] then
it is integrable on [ a l ,an]. Prove this and use it to show that f (z) = LzJ is
integrable over any interval [a,b].
15.4
Bolzano-Cauchy principle
We encountered the Bolzano-Cauchy principle for limits of sequences and
functions. For the integral it reads
45 1
Integrals
Theorem 15.9 A function f : [a, b] H C is integrable if and only if
for every positive E there exists a gauge 6 such that for any two tagged
divisions T X << 6 and SY << 6
IWf, T X ) - R(f, SY>I< E .
The proof of the necessity of the condition is very similar to the proof
of Theorem 10.16 and we therefore omit it.
Proof. Let 6, be the gauge associated with E = 1/n by the condition
of the theorem. We can assume that 6, 2 &+I, otherwise we just replace
d,+l(z) by Min(dl(z), . . .6n(z)). For each n let US choose TnXn << 6,. For
n > N we have
1
IW,Z V X N ) - R ( f T, n X J < N
(15.20)
This implies that the sequence with terms equal to R ( f , T n X n ) is Cauchy,
so it has a limit, say I . Letting n -+ 00 gives
For a given positive E let N
2
> - and T X << 6 ~ Then
.
&
With the Bolzano-Cauchy principle it is easy to prove integrability on
subintervals.
Theorem 15.10 I f f is integrable on [a, b] and [a,
p] C [a, b] then
f is integrable on [a,PI.
Proof. We prove the theorem for a < a = c and P = b. A proof for a = a
and p < b is similar and the general case follows by a combination of these
special cases. For every positive E there is a gauge 6 with the following
property: if T X and SY are 6-fine tagged divisions of [a, b] then
(i) T X and SY are anchored on c,
452
Introduction to Mathematics with Maple
(ii)
I R ( T X ) - R ( S Y ) (< E
(15.21)
Let
a = t o 5 X1 I
tl 5 - . . I
X m 5 tm =
C
I
Xm+l 5 . . .I
X n 5 tn = b
Denote by T X the tagged division
to I
X1 5 tl 5 ... tm
and let UW and V Z be 6-fine tagged divisions of [c, b].
R(TX)+ R(UW)
is a Riemann sum for a tagged division of [a, b], which we may denote
R ( T X ) . Similarly R(=) R ( V 2 )= R ( S Y ) .For both tagged divisions,
R ( T X ) << 6 and R ( S Y )<< 6. Clearly
+
R(UW)- R ( V 2 )= R ( T X ) - R ( S Y )
It follows from Inequality (15.21) and by the definition of 6 that
IR(UW)- R(VZ)I < &.
0
The Bolzano-Cauchy principle is the necessary and sufficient condition
for integrability. Theorem 15.11 below gives only a sufficient condition but
it is easier to apply.
Theorem 15.11 If for every positive E there exist integrable functions h, H such that
for all x E [a, b] except possibly a countable set and
LbH-l
b
h l E ,
(15.23)
then f is integrable on [a, b].
Proof. Since change of a function on a countable set does not affect
either the existence or the value of the integral, we can and shall assume
453
Integrals
that Inequality (15.22) holds everywhere on [a, b]. For E
gauge 6 such that all inequalities
S, H +
> 0 there exists a
b
R(H, O X ) <
E,
b
R(H,SY)<L H+E,
b
R ( h , D X ) > L h-E,
b
R ( h , S Y ) > L h-E.
hold whenever D X << 6 and SY << 6. The Riemann sums for f are smaller
than Riemann sums for H and they are bigger than Riemann sums for h.
Hence
R(f, O X ) - R ( f ( S Y )I R ( H ,O X ) - R ( h ,S Y ) 5
J,” H J,” h +
-
2e
< 3E.
Similarly
R(f,O X ) - R ( f S
, Y ) > -3e.
The last theorem is often applied to step functions h, H .
Theorem 15.12
[a, bl.
I f f is monotonic on [a, b] then it is integrable on
Proof. Without loss of generality, let f be increasing. Let T be a division
of [a, b] into n equal parts with dividing points t k . Define H ( t ) = f ( t k ) and
h(t) = f ( t k - 1 ) for t k - 1 5 t < t k . Then
Introduction to Mathematics with Maple
454
Clearly, for n > ( b - a ) ( f ( b )- f ( u ) ) / &Inequality (15.23) is satisfied.
0
It is important in the above theorem that f is monotonic
on a finite and closed interval [a,b].The function f , defined by f(z)= 1/x2
for x ~ ] 0 , 1 and
] f(0) = 0 is decreasing on 10, I] but it is not integrable on
[0,13 (by Example 15.7). If, however, f is monotonic and bounded on ] a ,b[,
Remark 15.8
then it is integrable because f has finite one sided limits a t the end points and
therefore differs a t a t most two points from its monotonic extension to [a, b].
A function f is called piecewise monotonic on [a,b] if there
exists a division T of [a, b] such that f restricted to ]ti,&+I[ is monotonic for
i = 0,1,. . . ,n - 1. If f is piecewise monotonic on [a,b] and bounded then it
is integrable.
Remark 15.9
Integrability of continuous functions is proved by the same method as
Theorem 15.12.
1 Theorem 15.13
Proof.
For
E
Iff is continuous on [a,b] then it is integrable on
1
> 0 let 6 be a gauge with the property that
For a 6-fine tagged division TX let
Both Inequalities (15.22) and (15.23) are satisfied and f is integrable by
Theorem 15.11.
0
455
Integrals
Exercises
Exercise 15.4.1
any interval [a, b].
Show that the function x
H
x - LxJ is integrable on
@ Exercise 15.4.2
Let S be a countable set. Prove that a function
bounded and continuous on [a, b] \S is integrable on [a, b]. Give an example
of a non-integrable f which fails to be continuous only at one point.
15.5 Antiderivates and areas
In this section we shall assume that we know intuitively what the area of
a planar set is. We denote by I" the set of all interior points of an interval
I ; for example [O,oo["=]O, OO[, [0, I]" =]O, I[.
The function F is said to be an antiderivative o f f on I if F is continuous
on I and F'(x) = f ( x ) for every point x E I". The word "primitive" is used
interchangeably for the word antiderivative. If F and G are antiderivatives
of f on I then there is a constant c such that
F ( x ) = G(x)
+ c.
To prove this consider H = F - G, then H' = 0 on I" and consequently H
is constant on I" and therefore on I .
Let us now consider a function f which is continuous and non-negative
on [a,b] and let F be an antiderivative of f on [a,b]. Let us denote by A(w)
the area of
+
For h positive, A(w h) - A(w) is the area of the set
v h, 0 5 y 5 f(x)}. Clearly
+
Since f is continuous at v, for every E
for Iv - x1 < 6. Therefore if 0 < h
( ( 2 , ~ )21; 5
> 0 there exists S > 0 such that
< S then
x 5
Introduction to Mathematics with Maple
456
This proves A/,(w) = f(w) for a 5 w < b; similarly, one can show that
A/_(w) = f ( w ) for a < w 5 b. It follows that A’(w) = f(w) and A is an
antiderivative of f . Consequently A(w) = F(w) c. By setting x = a it
follows that c = -F(a) and hence A(x) = F ( x ) - F ( a ) . In particular, for
x = b we have
+
A(b) = F ( b ) - F ( a ) .
(15.25)
We have discovered a way to compute the area of the set
by (15.25), the difference of the values of an antiderivative to f at b and a.
Example 15.8
Consider the area of the set
s= {(x,y); 0 L x 5 1, x2 5 y 5 x}.
By the previous discussion the areas A1 and A2 of the sets { (2, y); 0
1, 0 5 y 5 x} and { (z, y); 0 5 x 5 1 , O I y 5 x2} are respectively
Al(v)
W2
=
-
2’
w3
A ~ ( w=)-
3’
=
5x5
1
57
1
A2(1) = 3’
1
6
A more detailed analysis of our discussion would show that we have
actually proved the following theorem.
Obviously the area of S is
-.
Theorem 15.14 If it is at all possible to assign to every function
f which is continuous and non-negative on [u,b],and which has an
antiderivative F , and to every interval [u,w] c [a,b] a number AE(f)
in such a way that
(i)
(ii)
(iii)
(iv)
A: 2 0;
A: + A,”(f) = A r ( f ) for all a 5 u 5 w I w 5 b;
A: = c(w - u) if f(x) = c for u 5 x 5 w;
A: 5 A:(g) if f(x) 5 g ( x ) for u I
x 5 w;
then A:(f)
= F(w) - F ( u ) . In particular, A:(f)
= F(b) - F ( a ) .
For any reasonable theory of area of planar sets, the area A: (f)of the set
{(x,y); u 5 x 5 v, 0 5 y 5 f(x)} must satisfy the requirements (i)-(iv).
Integrals
457
Our theorem asserts that AE(f)is uniquely determined and AE(f) = F ( v )F ( u ) , where F is any primitive of f . In other words: No matter how the
area of a planar set is defined, as long as requirements (i)-(iv) are satisfied
we always have AE(f)= F ( v ) - F ( u ) .
The theory which deals with the concepts of length, area and volume is
measure theory. We shall not attempt to expound it and refer our readers
to Lee and Vfbornf (2000). Theorem 15.14 is a kind of justification5 for
evaluating areas by antider ivat ives.
15.6
Introduction to the fundamental theorem of calculus
s,"f
In Examples 15.1 and 15.1 and in Exercise 15.1.1 we found that
was
always the difference between the values of the antiderivative F of f at b
and a. This is by no means so by chance. In Theorem 15.14 we found
that the area of the set {(x,y); a 5 x 5 b, 0 5 y 5 f ( x ) } was the same
difference. On the other hand, our intuition led us to believe that this area
is
All this indicates that
s,"f.
(15.26)
where F is an antiderivative o f f . Equation (15.26) is often referred to as the
Newton-Leibniz formula. The theorem which states that Formula (15.26)
is valid for every function f possessing an antiderivative F is called the
Fundamental Theorem of Calculus. We shall prove it in the next section.
The difference F(b)- F ( a ) is often denoted by F(x)l: or FI:, or by [ F ( x ) ]
Hence (15.26) can be rewritten in the form
Example 15.9
16
:.
laf = F(x)l:.
b
By the Newton-Leibniz formula
xn dx
1
n+l
= -(bn+l
- an+l),
for
nE
N.
This result can also be established by using Maple.
Example 15.10
By the Fundamental Theorem of Calculus we have
(15.27)
5Provisional until readers become familiar with measure theory
Introduction to Mathematics with Maple
458
if either a < b < 0 or 0 < a < b. We know from Example 15.7 that the
function f, defined by f(x) = 1/x2 for x # 0 and f(0) = 0 is not integrable
on any interval containing zero.
15.7
The fundamental theorem of calculus
Theorem 15.15 (The Fundamental Theorem of Calculus.)
Let F be an a antiderivative of f on [a,b]. Then f is (Kurzweil)
integrable and
s,D
(15.28)
f = F(b) - F ( a ) .
We commence with a plan of the proof. For any tagged division T we
have
c
n
F(b) - F ( a ) =
( F ( t i )- F(t2-1)).
(15.29)
i=l
We wish to show that R ( f T
, X ) is close to F(b) - F ( a ) . We are going to
do this by showing that F ( t i ) - F(ti-1) is very close to f ( X i ) ( t i - ti-1).
For that we employ the relation F’(Xi) = f(Xi).
However this need not
hold for i = 1 or i = n. ( X i can be either a or b and F need not have a
derivative at a or b ) . Therefore we split the sum (15.29) and R ( f ,T X ) into
three summands and estimate the differences between them separately. We
write
R ( f , T X )= f(Xl)(tl - a )
+
c
n-1
f ( X i ) ( t i- ti-1)
+ f(Xn)(b- tn-1)
i=2
and
F(b) - F ( a ) = F(t1) - F ( a )
+
c
n-1
[F(ti)- F(ti-1)I
+ F(b) - q n - 1 ) .
i=2
During the proof we shall consider only tagged divisions anchored at a and
b, that is, we shall have X1 = a and X n = b. We can always ensure this by
demanding that the tagged division is 6-fine for some gauge.
459
Integrals
We denote
A5 =
C ( F ( t 2 )- F(t2-1)- f(Xi)(ti- ti-1))
*
r i=2
-l
and b - y < t 5 b implies that
[email protected]) - F(t)I < E .
(15.32)
Since limf(a)(t - a ) = 0 and limf(b)(b - t ) = 0 there exists a positive
ttb
tla
w such that
whenever a 5 t
< a + w , and
whenever b - w < t 5 b. Since F’(x) = f(x) for x E ( a ,b) we can find a
positive q = q(x) such that
and
whenever X - q(X) < u 5 X
ties (15.34) and (15.33)
I w < X + q(X).
Combining Inequali-
Introduction to Mathematics with Maple
460
Now define 6 by
&(a)= 6(b) = Min(y,w)
6(x) = Min ( ~ ( x (x
) , - a ) , ( b - 2)), for x E ( a ,b)
For the rest of the proof, let T X by &fine. (Recall from Remark 15.7 that
(x- a ) and ( b - x) appear in the definition of 6 to anchor X1 at a and X,
at b). Since tl - a < 6(X1) = &(a)I y we can use (15.31) with t = tl and
we have
( F ( t 1 )- F ( u ) (= A1 < E .
(15.36)
Similarly
( F ( b )- F(tn-l)l = A2
Since tl - a
< E.
(15.37)
< & ( X I )5 w we can use (15.7) with t = tl and we have
Similarly
We can use (15.35) with X = X i , w = ti, and u = ti-1 and we obtain
JF(t2)- F(ti-1) - f(XZ)(tZ - t i 4 ) l
I &(ti
- ti-1)
Summing these inequalities for i = 2 , . . . ,n - 1, we have
A5
< ~ ( -ba).
(15.40)
Combining (15.30) with (15.36), (15.37), (15.38), (15.39) and (15.40) gives
IR(f,T X ) - ( F ( b )- F(a))l < E ( 4
which completes the proof since E was arbitrary.
+b - a)
0
Remark 15.10 If F is continuous on [ q b ] and F’(x) = f(x) for all x E
] a ,b[ except possibly a finite set then f is integrable and s,” f = F(b) - F ( a ) .
This follows immediately by dividing [a,b] into a finite number of intervals,
where the Fundamental Theorem is applicable. It can be shown that (15.28)
holds if there is a countable set S such that F’(x) = f(x) for every x E [a,b]-S
and F is continuous on [a,b]. (See Exercise 15.7.6.)
46 1
Integrals
Direct integration
For evaluation of an integral of f we need a primitive of f . It is denoted
by
J
f(x) d x. A primibiwe to a given function is not uniquely determined.
However, two primitives to the same function differ at most by a constant.
Some authors write, for instance,
J
x d x = x2/2
+ C, emphasizing in this
way that the primitive to id is x H x2 or a function differing from it by
a constant function. We prefer to regard equations involving primitives
as equivalences: two functions are equivalent if they differ by a constant.
Then we do not have to write C in the equations, but this requires some
caution. The symbol f(x) d x denotes a primitive of f but not necessarily
the same function at each occurrence. For instance, we have both
J
J
(X
(x+ 1)2
+ 1)dx = 2
'
(X
X2
+ 1)dx = + X.
2
At many occasions the primitive can be easily obtained by reading a formula
for differentiation backwards. Table 15.7 summarises such formulae, which
we established earlier by differentiation. The formulae for
interval ] - oo,O[)and
s
1
-are partly new. These are easily verified
GT-i
by differentiation. Extensive tables of primitives have been compiled (see,
for example, Gradshtein and Ryzhik (1996)). Now, however, Maple will
find the primitive with the command int (f (XI ,XI. Of course, there is no
point in finding a primitive with Maple for evaluating an integral by the
Fundamental Theorem. Maple will find the integral instantly. However if
you want to have a primitive, Maple will find it for you on most occasions.6
Maple also omits the constant C as we do. Sometimes the primitive can be
found by using Table 15.7 easily, so easily that it is not worth opening the
computer. Here are some examples:
Example 15.11
it does not the primitive cannot be expressed by a simple formula anyhow.
Introduction to Mathematics with Maple
462
Table 15.1 A short table of primitives
Range of validity; comments
xn+l
/ x n d x = n+l
n E Z, n # -1; x unrestricted
for n 2 0; x # 0 for n < O
X E P , a E R , a # -1
either x E] -00, O[ or x €10,00[
[exdx = ex
no restriction
/ a x d x = ax
I
I
sinxdx =
-
acP,a#1
cosx
cos x dx = sin x
cos2 x
dx = tanx
s
'
sin2 x
J
1
dx = cot x
dx = arcsinx
dx = arctanx
1
no restriction
no restriction
any interval not containing
(2k l ) r / 2 , k E Z
+
any interval not containing
kr, k E Z
x
€1
- 1,1[
no restriction
no restriction for
+ sign;
] - 00, -I[ or 31, 00[ for
- sign
463
Integrals
Example 15.12
sin2 x + cos2x
dx
dx
J sin2xd cos2
x
=
J
dx= Jz
J
+
z
z - tan x-cot
x
sin2 x cos2 x
1
x.
+
If F is the primitive of f on an interval [a, b] then x I--+ -F(Ax
B ) is
A
the primitive of x H f(Ax + B) on the interval with endpoints Aa + B and
Ab + B.7 Indeed, by the chain rule
(iF(A2
+ B))‘ = ; F ( A X + B)A = AX + B ) .
Example 15.13
sin2xdx =
J1-czos2x
x
dx=--
41 sin2x.
There is another simple formula very useful in direct integration, namely
valid on any interval on which f(x) # 0. If for instance f(x)
lf(x)I = -f(x) and by the chain rule
1
[h(-f(x))]’ = -(-f’(x))
=
-f (4
< 0 then
f ‘(4
f (4-
Example 15.14
J
tanxdx=-
J
- sin x
-d x = lnlcosxl,
cosx
/ L d x = /
sin2x cos2 x d x = J e d x + J - d x cos x
sin 2x
2 sin x cos x
2 cos x
2 sin x
1
1
= --In I cosxl - In I sinxl,
2
2
+
+
J
X
2 2 - 4x
+5
dx=L/
2x-4
dx
2
x2-445+5 d x + 7 / 1 (x - 2 ) 2
1
= - ln(x2 - 42 5) 7 arctan(x - 2).
2
+
+ +
We now illustrate the use of the F’undamental Theorem by several examples
7This is a simple special case of change of variables, but it is convenient to consider
it here, before Theorem 15.17
464
Introduction to Mathematics with Maple
Example 15.15
We have
Just apply the Newton-Leibniz formula. Note that fi is not differentiable
a t zero. In this example we can see how convenient it was t o assume in the
theorem the existence of the derivative only in the open interval ] a ,b[. However,
for the validity of the theorem it is essential that F is continuous on the closed
interval [a,b].
Example 15.16 Using the Fundamental Theorem gives an instant solution
to Example 15.7. If the integral l i ( l / x 2 ) d x existed then (with 0 < E < 1)
l1
2
Jd’-$dx
fdx= 1 - 1.
&
For E -+ 0 the right hand side diverges to infinity - a contradiction.
Example 15.17 Let F be defined by F ( x ) = x2cos(n/x2)for x # 0 and
F ( 0 ) = 0. By the Fundamental Theorem of Calculus F’ is integrable on [0,1]
and
F’ = -1. However IF’I is not integrable. For an indirect proof assume
it is and define fi to be IF’( on
1/&[ (we denote this interval
-]ai,bi[), and fi = 0 otherwise. Then
ll
]l/Jm,
Jd’ 1 Ibi 1
bi
bi
fi =
IF’[ 2
fi =
a,
For every n E
ai
N and every x
F’I = IF(bi)-F(ai)l
ai
=
1
-+:
2 + 1
1
2
2 -.
2
2 + 1
E [0,1] we have
n
i=l
Consequent Iy
Since the right hand side of this inequality diverges as n --+
contradiction.
00
we have a
00
Example 15.18
Let f ( x ) =
CnXn
with radius of convergence r . Then
n=O
00
the function F defined by F ( x ) =
n=O
CnXn+l
.
IS
n t l
‘
a primitive of
f by the
465
Integrals
theorem on differentiation of power series (that is, Theorem 13.25). Hence by
the Fundamental Theorem of Calculus
00
< a < /3 < r .
provided that -r
Example 15.19
for every
Using the previous example with f(x) = e - x 2 we obtain
x > 0.
The function
@(x)
=
Jdx
e-t2 dt plays an important role in probability and
areas of applied mathematics such as heat conduction. Equation (15.41) gives
a meaningful expression for <p and a method for calculating it, a t least for small
x. Extensive tables for the function @ have been produced. It is interesting to
see how Maple handles this integral.
1
- fierf(x)
2
If you look in Help for the meaning of erf(x) Muple will tell you that
erf(x) =
$J, e x p ( - t 2 ) d t .
The answer, a t the first glance, looks silly: it says that the integral in
question equals to itself. However, the use of erf makes sense: erf is a higher
transcendental function which is encountered often. It cannot be expressed in
terms of more familiar functions' but other integrals or quantities of interest
can be expressed in terms of erf. For instance
>
int(x^lo*exp(-x^2) ,x=O. .2);
16201
-~
16
e(-4)
945
+fierf(2)
64
Like rational, exponential, logarithmic or trigonometric functions.
Introduction t o Mathematics with Maple
466
Of course, if you need a numerical value of the last expression you can obtain
it with the evalf command.
Example 15.20
Noting from Example 14.3 that
- ln(1 - x)
X
n=l
we can apply Example 15.18 to f(x) = -
In( 1 - x)
, and then we have
2
We cannot set t = 1 directly in this equation because the radius of convergence
of the power series is 1and Examplel5.18 is not applicable with [a,
p] = [0,1].
tn
However, if we set F ( t ) =
- and show that F is continuous a t 1 from
-
n=O
n2
the left, then F will be an antiderivative of f on [0,1] and by applying the
Fundamental Theorem of Calculus we obtain
-
l1
" 1
-.
dz = F(1)- F ( 0 ) =
n=l
n2
To prove that F is continuous from the left a t 1we write
tn-1
00
tn - 1
n=l
Since
00
" 1
n=l
n2
n=N+l
With N now fixed,
N
lim
tt1
tn 1
C= 0.
n=l
-
n2
1
&
4'
- < - Then
- converges, we can find N such that
n2
467
Integrals
Consequently there exists a 6 such that for 1- 6
< t 5 1we have
Combining these results we have
which completes the proof. The continuity of F from the right follows immediately from the Abel theorem. This theorem asserts that a power series with
radius of convergence r is continuous from the left a t r if the power series
converges for x = r . For Abel's theorem we refer to Lee and V$born$ (2000,
Theorem A.5.2). The Maple solution is
1
6
- 7r2
This answer only looks different from ours: the sum of the series is 7r2/6,as
can be confirmed by Muple.
>
sum(l/nn2,n=l.. i n f i n i t y ) ;
1
6
- 7r2
'.
An elementary proof of the equation
O0
1
(1961).
1
7T2
n2
6
- = - can be found in Matsuoka
Introduction to Mathematics with Maple
468
Exercises
Exercise 15.7.1
1. J ( a
+ bx3)4d x ;
J Cot2x d x ;
7. J
dx .
1- sinx’
4.
dx
x2 16
+
Find
2. Jaxb2x d x ;
5.
J
11.
x2
3. J t a n 2 x d x ;
+ 72 - 5
x+2
J d9 +d42x -
dx;
22’
2x+3
6. J m d x ;
dx
d x 2 4x
+ + 29’
Let f(z) = 2xsin & - nxl-n cos & for z # 0, and
Exercise 15.7.2
f ( x )dx and
f ( x )dx. [Hint: It is not difficult to
f(0) = 0. Find
guess the primitive.]
J’,fLyT
Exercise 15.7.3
Show that for any x > 0
Exercise 15.7.4
Show that
if r > 0 and the series on the right hand side converges absolutely. [Hint:
Use the same method as in Example 15.20.1
@ Exercise 15.7.5
Using the Fundamental Theorem of Calculus prove
that f ( x ) = 1 / x is not integrable on [0,1]. Also give a simpler proof of
Example 15.7. [Hint: J’,, f 2 JE1 2 - lne.]
@ Exercise 15.7.6
Prove that if
(i) F’(x) = f(x) for all x E [a,b] - S with countable S, and
(ii) F is continuous on [a,b]
then
[
f ( x ) d x = F(b) - F ( a ) . [Hint. Find q ( x ) as in (15.35) for x E
[a,b] - S and let q ( x ) = 1 otherwise. Let n I+ xn be an enumeration of
S , define 6(xn) > 0 to be such that for I X - zn1 < 6(xn) we have IF(x) F(zn)l < ~ / and
2 If(xn)l
~
< ~ / and
2 let
~ 6(z) = 1 for x
S. Define
4
Integrals
469
6(x) = Min(q(x),h(x)) and proceed as in the proof of the Fundamental
Theorem of Calculus. ]
15.8
Consequences of the fundamental theorem
The rule for differentiation of a product and the chain rule lead to theorems
on integration by parts and substitution.
Theorem 15.16 (Integration by parts) If F and G are continuous on
[a,b], F' = f and G' = g on [a,b] except on a finite setg then
- F(u)G(u).
(15.42)
Proof. We have (FG)' = Fg+Gf except on a finite set, the function F G
is continuous on [a, b] and by the Fundamental Theorem, Equation (15.42)
follows.
0
Provided that one of the integrals in Equation (15.43) below exists we
obtain useful formula
[
Fg = F(b)G(b)- F(a)G(a)-
[
fG.
(15.43)
The above formula is often rewritten in the easily remembered form
(15.44)
Ja
Ja
Example 15.21
The assumptions of the theorem alone do not guarantee the existence of either integral in Equation (15.43) as the following
( z X) ~ C O S Z - ~for z # 0
example shows. For F ( z ) = ~ ~ s i n z - ~ , G =
and F ( 0 ) = G(0) = 0 neither
Fg nor
Gf exists because then both
would exist by Equation (15.43) and so would their difference. However
F ( z ) g ( z )- G ( z ) f (z) = 4z-l is not integrable. For F and G chosen as above,
Equation (15.42) holds but Formula (15.43) does not make sense. Moreover
this example also shows that the product of an integrable function f and a
continuous function G need not be integrable.
$
Remark 15.11
so
We illustrate the use of integration by parts in three cases.
( i ) integrating a product in which one factor has a simple derivative and
the other is not too difficult t o integrate,
91f one is prepared to use Exercise 15.7.6 this set can be countable.
Introduction to Mathematics with Maple
470
(ii) by inventing a factor which is easy to integrate (this is actually a subcase
of (i)),
(iii) by integrating by parts we obtain an equation from which the integral
can be found (sometimes to obtain such an equation one must integrate
by parts twice).
Example 15.22 We illustrate (i) with the integral J: za l n z d z , Q! # -1,
a E R. In applying Equation (15.44) we set u' = xa, v = Inz and have
ta+l
ta+l
ta+l - 1
lx"lnxdx= a + l l n t - l s d x = - c U + l lnt - ( a 1)2 -
+
As a teaching tool, Maple has a command intparts in the student package.
Besides the integral and the limits of integration, Maple also requires the factor
to be differentiated, in other words the function v in Formula (15.44).
>
student[intparts] (Int(x^(alpha)*ln(x) ,x=l.. t ) , l n ( x ) ) ;
>
simplify(%);
The command intparts is useful in seeing how integration by parts works
or as a practical introduction to integration by parts but a better insight
into the method is obtained by working with pen and paper. If we want just
the integral Maple would compute it instantly with the i n t o command.
An example illustrating (ii) is
Example 15.23
arctan z d z = - -
n 1
- - -1n2.
We deal with the integral from Example 15.22 for a = -1
Example 15.24
to illustrate (iii)
1
2
consequently J = - In2 t.
471
Integrals
Example 15.25 (Wallis' formula) By integration by parts we diminish
k in the integral SI, = Jo5 sin'xdx. Such formulae are called reduction
formulae. In Equation (15.44) we set u' = sin x, v = sin'-'
SI, = - sink-' x cos xl;l2
+ (k - 1)
1
x and have
TI2
z cos2x d x
This formula leads to an interesting limit for 7r, established by the British
mathematician Wallis. For an even k = 2 n E N we have
S2n =
2n-12n-3
l7r
2n - 1
-S2n-2= --. . . - 2n
2n
2n-2
22
since SO= 7r/2. Similarly
since S1 = 1. For z E [0,7r/2] we have 0 5 sinz
sin2n+2x <
- Sin2n+lz 5 sin2nz, and consequently
This implies
5 1 and it follows that
5 S Z n + l 5 SZn.
S2n+2
and by the Squeeze principle
+
2n 2
Since --+ 1 we also have
2n+1
Equations (15.45) and (15.46) are the Wallis formulae. It follows from these
formulae that lim f i S n =
n+m
m.
Application of the chain rule leads to
472
Introduction to Mathematics with Maple
Theorem 15.17 (Change of variables.)
If
(i) the function 4 : [a, b] H [A, B],
(ii) 4 is continuous on [a, b],
(iii) 4 has a derivative on ] a , b[;
(iv) there is a function F , continuous on [A, B] with F’(z) = f(x)
for x E ( A , B),
then
~~
The theorem is often referred to as integration by substitution.
The theorem as stated suggests that 4 ( a ) < 4 ( b ) . However, this is not guaranteed by the assumption of the theorem. Therefore we
extend the definition of the integral first.
Remark 15.12
6efinition 15.3
If a = b we define
LUf= o
and if b
(15.48)
< a we define
[f
= -/af
(15.49)
b
Most of what we learned about the integral extends easily to the case
of b < a. For instance, integration by parts (15.43) is valid without any
change, if b < a. On the other hand the formula
lf
must be modified to
in order to be valid for any pair a , b.
) every t E ( a , b) and
Proof. Noting that [F(4(t))]’= f ( 4 ( t ) ) @ ( tfor
applying the Fundamental Theorem to the integral on the right-hand side
473
Integrals
of (15.47) we have
If $(t)= $(b) then both sides of Equation (15.47) are zero. If 4(a) < +(b)
then by the Fundamental Theorem the left-hand side of (15.47) is F($(b))F ( ~ ( u )and
) both sides of Equation (15.47) are equal. If 4 ( a ) > 4(b) then
0
the last equation holding by the Fundamental Theorem.
Equation (15.47) can be used in two different ways: to evaluate the
integral on the left hand side by finding the value of the integral on the right
hand side; or by going in the opposite direction. In either case, when making
the substitution x = 4(t) one has to substitute not only 4(t) for x but
also dx = +'(t)dt. This equation is easy to remember: it is obtained from
dx
- = #'(t),as if by multiplication by dt. When using Equation (15.47) from
dt
right to left an experienced mathematician recognizes the factor #(t)dt for
substitution as dx; often an expert creates that factor by some manipulation
of the integrand and achieves considerable simplification. Very important
for the use of the theorem is to change not only the variables but also the
limits of integration. If we wish to change variables x = 4(t)in the integral
f we need to find a and b such that +(u) = a and 4(p) = b. This is easy
if 4 has an inverse, since then, a = $ - ~ ( aand
) b = 4(p).
The strength of this theorem lies in the fact that no assumption is made
concerning the monotonicity of 4 or existence of 4-1. This is illustrated in
Example 15.27.
:1
Example 15.26
The integral
+
l'
d
n
d x represents the area of a
quarter circle { (x, y); x2 y2 5 r 2 } .To apply the theorem we set x = $(t)=
r sint with t E [-7r/2, 7r/2] and dx = r cos t d t. When x = 0 or x = r then
t = 0 or t = n/2, and we have
l ' d m d x = r2
cos2t d t
7rr2
474
Introduction to Mathematics with Maple
We have recaptured the geometric significance of r , for the second time. We
originally defined 7r/2 as the smallest positive root of the equation cosz = 0.
Maple has a command changevar for changing variables. It resides in the
student package. The structure of the command is as follows:
changevar(equati0n defining the substitution,
(Int(f(z), x = a..b), new variable) ;
We give two examples of changing variables in
>
>
student [changevar] (x+sqrt (1+xn2)=t,
Int(l/sqrt(l+xn2),x=0. .2),t);
lfi+2f
dt
>
>
student [changevar] (x=sin(t> ,
Int(sqrt ((i-x>/(i+x>>,x=O..1) ,t>;
sin(t)
+ 1 cos(t) d t
Maple will not easily simplify the integrand in the last example, but
it is clear that the integrand is 1 - sint and so the integral can be easily
evaluated. Part of the skill of using change of variables is anticipation of
the form of the new integral and it is our belief that such an anticipation is
acquired by working examples on change of variables with pen and paper.
So we give two more examples working without Muple.
s_,
tdt
we use [email protected]
tion (15.47) from left to right. When making the substitution t2 16 =
$(t)= x we recognize in t d t half of d x and we have
Example 15.27
L
In order to calculate
~t " z l 6
= z1 J 2 5 d x = &jZO
25 = 5 - 2&.
20 h
Note that $ is not monotonic on [-2,3].
+
Integrals
Example 15.28
integral I =
?r
475
Using the substitution y = t a n x and d y =
dx
leads to
1 8cos2x
dx
-in the
cos2 x
+
The result is obviously wrong: an integral of a continuous positive function
must be positive. We must look for an explanation. The function tan is discontinuous a t n/2, violating assumption (ii) in Theorem 15.47, so the theorem
cannot be applied. The correct evaluation is as follows:
Jd
?r
dx
l+8cos2x
=I
dx
?r
dx
1 + 8 c 0 s ~ x + 1~+/8~c o s 2 x '
For the second integral we apply the substitution x = 7r - t, d x = -dt and
have
dx
=-L12
dt
1+8cos2t
=1
dx
1+8cos2x'
Consequently
?r
dx
l+8cos2x
dx
'1+8cos2x
First we show thatlo
b-m/2
lim
1
0
dx
1+8cos2x
=1
dx
1+8cos2z'
Indeed
4
Now we evaluate (with b
dx
dx
5 ~ / -2 b.
l+8cos2x
< 7r/2)
tan b
"If f f is integrable on [a, b] then lim
tTb
Section15.10
tan b
Jdt Jdb
f =
1
arctan
(3)b
tan
f . We shall prove that later in
.
Introduction to Mathematics with Maple
476
The limit of the right hand side for b t 7r/2 is 7r/6. Finally we have the result
dx
7 r 7 r
1+8cos2x
=2-=-
6
3'
It is interesting to compare our work in the last three examples with the
ease with which Maple evaluates these integrals.
> restart;
> int (sqrt(r-2-x-2),x=O..r) assuming r > O ;
1
4
int (x/sqrt(xA2+l6) ,x=-2. .3);
-r27r
>
5 - 2 6
>
int(l/(l+8*(cos(x>)'2)
,x=O..Pi);
1
3
-7r
The last example on Maple use is particularly pleasing. Although mechanical use of change of variables could easily lead to a wrong result, Maple
was sophisticated enough to avoid any mistake.
Assumption (iv) in Theorem 15.17 is a little inconvenient. On some occasions we might like to change the variables because we do not know F but
then it might be difficult to justify the use of (15.47).11 The next theorem
does not have this weakness. However there is an additional assumption
that 4 is monotonic.
Theorem 15.18 (Monotonic change of variables) If we awume
(i)-(iii) of Theorem 15.17 and if q5 is strictly monotonic then Formula (15.47) holds in the following sense: If one of the integrals appearing in (15.47) exists then so does the other and (15.47) is valid.
For the proof we refer to Lee and Vfbornf (2000, Theorem 2.7.8).
Exercises
Use integration by parts to evaluate the following inExercise 15.8.1
tegrals, with and without Maple.
"Iff
is continuous on [a,b] then (iv) holds because of Corollary 15.21.1.
477
Integrals
(1)
{
zexd x ;
X!
(2)
J
t3-tdt;
X
!
(4)
J
0
rX
(5)
tsintcostdt;
+
J, %d t , provided -5 < a < x < 5.
Exercise 15.8.2
(1)
SU(l+
Change the variables in the following integrals
+
x y o 0 dx, t = 1 x.
-1
sin x
15.9
d x, t = cos x assuming -n/2
< x < n/2;
Remainder in the Taylor formula
The Peano form of the remainder in the Taylor formula says that the remainder is smaller than the last term by an order of magnitude. Sometimes
a more precise estimate is needed. It is provided by the next theorem.
Theorem 15.19 (Integral remainder) If f , f', . . . , f(") are continuous on an interval I with end-points a, b and f ( n + l ) exists on I
except possibly a finite subset of I , then the function t H f("+')(t)(bt)"/n! is integrable on I and
lI)
f ( b ) = f(a)
+ f'(a)(b - a ) + - - - + f
( b - a)"
n ( a ) T
+ %+l,
(15.50)
where
(15.51)
Introduction to Mathematics with Maple
478
If one is prepared to use Exercise 15.7.6 then the excepRemark 15.13
tional set in the above theorem can be countable.
Proof.
By induction. For n = 0 the result is just the Fundamental
Theorem.12 For n = 1 we have
Employing integration by parts (15.44) with u = t - b and w' = f " we have
J" (ff ( s ) d s )
a
= J"(b - t ) f " ( t ) d t .
a
a
Turning to the induction hypothesis, by Equation (15.44) with u = f(n+l)
and v(t) = ( b - t)("+')/(n
l)!we have (the existence of the first integral
is guaranteed by the induction hypothesis)
+
This means
Rn+l
= f ("+l)(U)
( b - a)"+'
(n I)!
+
+ Rn+2.
Equation (15.50) is often given a different form
These ways of writing the formula have the advantage that they do not
suggest that h is positive or that x > a.
12The case n = 1 is considered as motivation for the rest of the proof, from a strictly
logical view this step is not needed.
Integrals
479
A particularly useful estimate of Rn+l follows from from the remainder
formula (15.51) if f n + l is bounded on the interval [a, b]. If Ifn+'(x)l 5 M
for x E [a, b] then
(15.52)
We wish to approximate tan J: just by x. Using the Maple
command taylor for obtaining the Taylor approximation gives
Example 15.29
>
taylor(tan(x) ,x=0,3);
x
+ 0(x3>
XI
This tells us that ltanx - 5 KJ2J3,
for some constant K . This can be .001
or 1000. In concrete application a precise estimate of K can be important. We
use Inequality (15.52).
>
t3:=diff (tan(x) ,x,x,x);
+
t3 := 2 (I + tan(z>2>2 4 tan(^)^ (1 + tan(x>2>
>
simplify(%) ;
2
+ 8 tan(^)^ + 6 tan(^)^
We need some preliminary estimate of tan.
>
a:=0.01/l-(0.01n2)/2;
u := .0099500000
>
M:=2+8*a^2+6*an4;
M := 2.000792079
Finally
Introduction to Mathematics with Maple
480
for 1x1 5 .01. In the next example we wish to approximate exp(tanx)) by a
polynomial of fifth degree.
>
taylor(exp(tan(x))
13- x
,x=0,6);
37
+ -21x2 + -21x3 + g3 x4 + x5 + O(x6)
120
+ tan(^)^)^ etan(z)+ 136 (1 + tan(^)^)^ etan(z)
+ 416 tan(^)^ (1 + tan(x)2)2etan(z)+ 1168tan(z)2(1 + tan(^)^)^ etan(z)
+ 480 tan(x) (1+ tan(^)^)^ etan(%)+ 40 (1+ tan(^)^)^ etan(z)
+ 32 tan(^)^ (1 + tan(x)2)etan(z)+ 496 tan(^)^ (1+ tan(x)2)2etan(z)
+ 720 tan(^)^ (1 + tan(^)^)^ etan(’)+ 260 tan(x)2 (1+ tan(^)^)^ etan(z)
+ 30 tan(x) (1 + tan(^)^)^ etan(z)+ (1 + tan(x)2)6etan(z)
t6 := 272 tan(x) (1
>
subs(tan(x)=a,t6);
185.0005512 e*0099
>
M: =evalf (%);
M := 186.8504949
~~
-
for 1x1 5 0.01. Such
The error is smaller than 10-12M/(6!) < .026
high accuracy is rarely needed but it was easy to achieve with MapZe.
Remark 15.14 (The Lagrange remainder)
x E [a, b] then there exists a c € ] a ,b[ such that
If fn+’(x) exists for all
(15.53)
For n = 0 this formula reduces to the Lagrange mean value theorem and it is
called the Lagrange remainder. This form is easy to remember: the remainder
looks like the next term in the expansion but the derivative is evaluated a t a
shifted point c. The Lagrange remainder is valid if f ( n + l ) exists on [a,b]. In
proving it we assume, for sake of simplicity, that f ( n + l ) is bounded. Let rn, Ad
be the greatest lower bound or the least upper bound of f ( n + l ) on [a, b]. Then
48 1
Integrats
By the intermediate value property of the derivative there exists a c with
Exercises
Exercise 15.9.1
given intervals:
(I) sinx 1M x
+
(2) 1 xl+ x3
(3) cosx = 1
@
Find the accuracy of the following formulae on the
[-.rr/6,.rr/61;
=1-x
[o, 0.011;
[- 10-3,10-31.
Exercise 15.9.2 Give an example showing that the Lagrange remainder
is not valid if f ( n + l ) fails to exist at one point of ]a, b[. [Hint: Go back to
the mean value theorem.]
15.10
The indefinite integral
The function
F(x) =
Lx.f
(15.54)
is called the indefinite integral of f , or simply the indefinite integral. We
shall study its properties in this section. Whatever we say or prove about F
applies with little or no change to F ( z ) = f . If T X is a tagged division
s,”
then by T X I : we denote the tagged division
Lemma 15.1
Iff is integrable on [a, 61, a 5 c
with the property that
< d 5 b and 6 a gauge
(15.55)
Introduction to Mathematics with Maple
482
then,
iP3 0x1: << 6
(15.56)
Proof.
For
r)
> 0 let 61 5 6 and 62 5 6 be gauges such that
tagged divisions SY << 61 and U V << 62 of [a, c] and [d, b], re~pective1y.l~
The sum
R(f
7
sy>+ Wf,D X If, + R(f,U V )
is a Riemann sum for a 6-fine tagged division of f on [a,b]. Using (15.55)
and (15.57) leads to
0
Sending q + 0 gives Equation (15.56).
Theorem 15.20 I f f is integrable on [a, b] then its indefinite integral is continuous on [a, b].
I
Proof. We prove continuity of F from the right at c, a 5 c < b. Continuity form the left at a point which is not the left end-point of [a, b] is
proved similarly. For E > 0 find a gauge S such that
(15.58)
Obviously lim f(c)(t - c) = 0 and there exists w
t.Lc
~ / for
2 c
< t < c = w.
Let c
> 0 such that f ( c ) ( t- c ) <
< t < Min(w,G(c)) then
the lemma can be
13We should have said: if 0x1: is fine with the restriction of 6 t o [c, d ] . We shall not
make a distinction between a gauge and its restriction.
141f a = c or d = b terms involving R ( S Y )or R ( U V ) , respectively, should be omitted.
483
Integrals
Consequently
[email protected])
- F(4
The next natural question “0 ask, after continuity, is differentiability.
Generally speaking, the indefinite integral need not be differentiable on all
of ] a ,b[. For instance, if f : x H x - 1x1 then
forO<x< 1
forl<xL2
Hence FL (1) = 1, F$ (1) = 0 and F is not differentiable at 1. The next
question is: if F is differentiable, is F’(x) = f(x)? The answer is again no.
If f = IQ,then F(x) = F’(x) = 0 # l ~ ( x =
) 1 if x is rational. However
the next theorem shows that F is differentiable at points of continuity of f
and then F’(x) = f(x).
Theorem 15.21
I f f is integrable on [a, b] then
(i) FL(c) = f(c) i f f is continuous from the left at c, a < c < b;
(ii) F$(c) = f(c) i f f is continuous from the right at c, a 5 c < b.
(iii) F’(c) = f(c) i f f is continuous at c, a < c < b.
Corollary 15.21.1
I f f is continuous on [a, b] then it has a primitive, more precisely there exists a function G such that
G’(4 = f ( 4
for a
(15.59)
< x < b and GL(a)= f ( a ) , G’+(b)= f ( b ) .
Proof. We prove only (i) since (ii) is entirely similar and (iii) follows
from (i) and (ii). By continuity off from the left at c for E > 0 there exists
q > 0 such that
Introduction to Mathematics with Maple
484
It follows by integrating this inequality that
(f( c ) - E ) ( C - ). F F(c) - F ( z ) 5 (f(c) + .)(c
- z)
and FL(c) = f(c).
0
Theorem 15.20 says, for an integrable f , that
1imSbf = F(b) - F ( a ) =
tla
t
l
f-
If we knew without assuming the integrability of f that this equation holds,
we can use it profitably for evaluation of integrals. We established in Example 15.22 that
s,1
In t d t = - 1 - z In z
+ 2.
The right hand side has limit equal to -1 as z -+ 0. If we knew that the
limit of the left hand side is
In t d t then we have evaluated the integral.
The next theorem gives an affirmative answer.
Jl
Theorem 15.22 (Hake's theorem)
every c with a < c < b and
lim
cla
[f
Iff is integrable on [c, b] for
exists and equals A
(15.60)
b
then f is integrable on [a, b] and Ja f = A .
Similarly
Remark 15.15
If f is integrable on [a, c] for every c with a
li$Lc
f exists and equals A
then f is integrable on [a, b] and
jab f
< c < b and
(15.61)
= A.
Proof.
We can and shall assume without loss of generality that f(a)= 0.
Take a strictly decreasing sequence {c,) with c, 4 a and co = b. For every
positive E there exists S, such that if T X ( n )is a &-fine tagged division of
[ C n , cn-11 then
(15.62)
485
Integrals
According to the hypothesis there exists r such that
(15.63)
whenever a < c < r . Now define a gauge 6 which has, for all n E N, the
following properties. l 5
S(Z) I Sn(Z) for Z E [cn, cn-11,
In addition we also require that
(15.66)
d(a) = r - a,
1
S(b) = - ( b - ~
2
(15.67)
1 ) .
Let now T X be a S-fine partition of [a, b],
Relations (15.64) and (15.65) imply that X 1 = a and hence f ( X I ) = 0.
The Riemann sum R ( T X ) starts with the term f (X,)(t, - t l ) . Let N be
the first integer for which c ~ + 15 tl. By Inequality (15.66)
tl < t o
+ & ( X I )= a + &(a) < r
and consequently by (15.63)
Since
it suffices to show that
(15.68)
15The first inequality implies that S(c,) 5 Min(S,(c,),
&+I
(c,)).
Introduction to Mathematics with Maple
486
Claim: The S-fine partition T X anchors on C O , c 1 , . . .C N .
Accepting the claim for a moment we have
N
n=l
and consequently
The sum on the right hand side is smaller then ~ / by
2 Inequalities 15.62.
Applying Lemma 15.1 with the roles of a and p played by tl and C N we
have
Thus we have Equation (15.68).
It remains to prove the claim. For k E { 0 , 1 , . . . ,N } each
[ti-l, ti] for some i E {2,3, . , . ,n}. Then
If c k < X i then X i
(15.65)l6
~ ] c jc j, + l ]
ck
belongs to
for some j 5 k. Then by (15.64) or by
contradicting Inequality (15.69). The possibility X i < ck can be ruled out
similarly. For k = 0, 1, . . .C N the term c k tags the subinterval in which it
lies. The claim has been proved.
0
Theorem 15.22 yields the next theorem
Theorem 15.23 (Integrability test) H, for every c, a < c < b, f
is integrable on [c, b] and there are functions G, g integrable on [a, b]
and such that
g(4
I f ( 4 L G(4
for all z E [a, b] except a finite set then f is integrable on [a, b].
l6If
xz = C j + l
487
Integrals
Proof.
The limits
b
b
liml g
lx il am l G
and
zla
exist by Theorem 15.20. By the Bolzano-Cauchy convergence principle for
limits of functions, for every positive E there is a d such that
whenever a
liiL
< a', a" < d. Consequently
b
and
f exists by the Bolzano-Cauchy principle.
Example 15.30
0
The integral
is called elliptic. It cannot be easily evaluated by the Fundamental Theorem,
as the primitive is not an elementary function. We show that
integrable on [0,1]. Factorizing, 1 - x4 = (1 - z)(l x)(1
(1 x)(1+ x2) 2 1 for 0 5 x 5 1 we have
+
+
05-
1
diT?
<-
1 / d m is
+ z2).
Since
1
diTi
and Theorem 15.23 can be applied with g ( x ) = 0 and G ( x ) =
show integrability. Maple can evaluate the integral
l / d G to
1.311028777
In the intermediate result Maple employed the function Beta, also called
Euler's integral of the first kind. It is defined for p > 0 and q > 0 by the
Introduction to Mathematics with Maple
488
equation
1
1
B(p,q) =
x*-'( 1 - x)q-'d x.
Except for some special values of p and q the Beta function cannot be expressed
in terms of elementary functions of p and q. However, as in the above example,
many other integrals occurring in applications can be expressed in terms of the
Beta function.
Exercises
@ Exercise 15.10.1 Prove: I f f is integrable on [c, 13 for 0 < c < 1 and
limxaf(x) = L # 0 then f is integrable on [0,1] if and only if a < 1. [Hint:
40
Use Theorem 15.23.1
Exercise 15.10.2 Use the previous exercise to show the integrability of
l/JZE and nonintegrabilty of 1/ sinx on [0, 13.
15.11
Integrals over unbounded intervals
In applications one encounters integrals over unbounded intervals. For instance
00
appear in physics and probability, respectively. So far we have not defined
integrals over unbounded intervals. We remedy that now. We denote by I
any of the following intervals
positive
E
15.4 A number I is the integral o f f over I if for
there are a gauge S and a positive number K such that
IWTX)-
4< E
(15.71)
whenever T X is a &fine tagged division of a bounded interval [A, B]
with I 3 [A, B] 3 I n [-K, K ] . Integral of f over I is denoted by
Integrals
489
If I = [a, b] then this definition has the same meaning as Definition 15.2.
The geometric meaning behind the definition above is that for a nonnegative function f on an unbounded interval I the area under the graph
of f and above the x-axis is approximated by Riemann sums for &-fine
tagged divisions of sufficiently large bounded intervals. There is at most
one number I satisfying the requirements of the definition: in other words,
the number I is well-defined. The proof is rather similar to the proof of
Theorem 15.1 and is omitted. If I is one of the intervals in (15.70) then
is denoted by
respectively, or by similar symbols where f is replaced by f (2)d IC or by
f(u)d u , etc.
s;"
We wish to show that
I C - ~ ~
=Z1. We choose 6(zj =
and K > 3 / ~ . Let T X be a 6-fine tagged division of [l,tn]
K . Motivated by the Fundamental Theorem and by the fact that
Example 15.31
m/3(1
with
--d
dx
+
E)
t, >
z-' = I C - ~ we shall approximate
1
-(ti
Xf
-ti-1)
by
1
1
ti-1
ti
-- -.
FirstI y
1L
$ (A );
-
= 1-
1
&
- > 1- 3'
tn
(15.72)
secondIy
xi
15-<
ti-1
xi
xi - S ( X i )
&
- 3(1+ E )
-<1+-
3+2&
3'
Introduction to Mathematics with Maple
490
This leads to
1 I 1% $1 (& ); ;
L -t;;
5
-
-
1
(ti_l-
);
.
Using these estimates on the Riemann sum yields
Combining this inequality with (15.72) gives
A combined version of Theorems 15.20 and 15.22 for an infinite interval
reads
f is KH-integrable on
Theorem 15.24 (Hake)
if
b+oo
lim
[a, 003 if and only
[f
exists and then
b+oo
lim
J,of
(15.73)
=L m f .
J!
Remark 15.16 Similar theorems hold for m
f . The integral
f and for s-”, f
= J!oo
f
+
if it exists but the limit on the right hand side might exist and the integral
might not. If, for instance, f(z)= sinz or z then the limit is zero but the
integral does not exist.
Example 15.32
We give a one line solution to Example 15.31. By the
Fundamental Theorem
Jim
b-oo
[
+ d z = lim (1 X,
b+oo
->b1 = 1.
491
Int egmls
Example 15.33
W e prove integrability of (sinx)/x on [ l , 001. Integrating
by parts gives
Consequently, for a
>K
and
b >a
By the Bolzano-Cauchy Theorem the limit lim
b+m
Theorem 15.25 (Integrability test) I< for every c, a < c < 00,
f is integrable on [a, c] and there are integrable functions G, g such
that
dx)
I
I f (4I
G(4
for all x E [a,00[ then f is integrable on [a,00[.
The proof is very similar to the proof of the integrability test in Section 15.10, Theorem 15.23. For an application of this integrability test see
Exercise 15.11.1. An analogous test holds for c -+ -00 if f is integrable on
[c, b] for every c < b.
Since exp(-x2) 5 1/x2 for x # 0 the integrability test
can be applied with g(z) = 0 and G(x) = 1/x2 on both intervals [l,00[and
] - 00, -11 to conclude integrability of exp(-x2) on ] - 00, 00[.
Example 15.34
In this chapter we encountered the special functions erf and the Euler
integral ( p , g) H B(p,a). One of the most important higher transcendental
functions is the function, also called Euler’s integral of the second kind.
It is defined its
r(s)=
for s
lm
exp(-x)zs-l d 2,
(15.74)
> 0. For a positive integer s = n it becomes
(15.75)
Introduction to Mathematics with Maple
492
Many integrals can be expressed in terms of the Gamma function as in our
cos 22
T d x
next example, where we evaluate
Jom
-
&csc(-
2
>
2
1
9
7r) sin(- .;rr) qE)
5
10
3
r(5)
evalf(%);
.2066317466
Exercises
@ Exercise 15.11.1
c > a and
Let f : [ a , o o [ I
~R be integrable on [a, c] for every
lim x"f(x) = L
2+m
Show that if a! > 1 then
# 0.
f exists and i f a! 5 1 then the integral does
not exist.
Exercise 15.11.2
15.12
Use Maple to evaluate
s-",exp(-x2)dx.
Interchange of limit and integration
In pure and applied mathematics, interchange of limit and integration,
according to the equation
(15.76)
is often needed. Since the sum of a series is nothing but the limit of partial
sums, Equation (15.76) translates into
493
Integrals
It would be wrong to assume that Equation (15.76) always holds. If f n =
1
1
n l ~ o , l / then
~[
1
Jof n
= l'for every n E N, hence lim
n-mo
fn
= 1. On
the other hand lim nllo,$[(z) = 0 for every z E [0,1] and consequently
Jo' ;.%
Let
n-+m
fn
fn
= 0.
and f be functions defined on an interval I . Define
The sequence of functions n H f n is said to be
Definition 15.5
uniformly convergent on I to f if r n -+ 0.
For instance, the sequence n -+ (sinnz)/n is uniformly convergent to
the zero function on R, since, in this case, r n = l/n.
Under uniform convergence Equation (15.76) is valid. More precisely,
we have
If the functions f n are integrable on an interval
Theorem 15.26
[a, b] and the convergence fn -+ f is uniform on [a, b] then f is integrable and Equation (15.76) holds.
Proof. It follows from the definition of uniform convergence that for every E > 0 there exists a number N such that
for all x E [a, b] and all n > N . The integrability of f follows from these
inequalities and Theorem 15.11. By Theorem 15.6
for all n > N .
0
This theorem has simple wording and a very easy proof. It is a useful
theorem, however, the condition of uniform convergence is sometimes too
restrictive. If j n ( x ) = xn then, as is easily checked by direct evaluation,
Equation (15.76) is valid with [a, b] = [0,1]. The theorem is not applicable
because the convergence is not uniform. The theorem is no longer valid
Introduction to Mathematics with Maple
494
if the interval [a, b] is replaced by an unbounded interval. For instance, if
f, = ln,2,/n then fn converge uniformly to zero but
The concept of uniform convergence is, nevertheless, important, as the
next theorem shows.
Theorem 15.27
If f, are functions continuous at c € ] a , b[ and
converging uniformly on [a, b] to f then f is continuous at c.
Proof.
The following inequality is obvious
First we find n such that r,
such that
< &/3and then with this n fixed we find S > 0
The continuity of f at c follows.
We now return to the interchange of limit and integration.
0
Integrals
Theorem 15.28 (Monotone Convergence Theorem)
that
495
Assume
(i) the functions f n are integrable on an interval I for every n E N,
(ii) n-mo
lim f n ( x ) = f(x) for every x E I ,
(iii) f n ( x ) L f n + l ( x ) for n E N and all 2 E I , or f n ( x ) 2 f n + l ( s )
for n E N and all x E I .
Then
1 exists. I f it is finite then f is integrable and
if it is infinite then f is not integrable.
For the proof we refer to Lee and V9born9 (2000, Theorem 3.5.2). Here we
illustrate the theorem by two examples.
Example 15.35
Denote by iI( the length of the interval I . Let
where the intervals are pairwise disjoint. Then
(15.78)
provided the series converges. Let An = U;EZ1Ik,
then l ~converge
,
increasingly t o 1~ and
Taking limits as n --+ 00 on both sides of this equation and applying the
Monotone Convergence Theorem proves Equation (15.78)
Example 15.36 (The Laplace integral) Integral
exp(-x2)dx is
called the Laplace integral. Maple already told us that it equals to &/2.
Now we show how this can be proved.17 From the power series expansion of
17There are more elegant proofs, however they require knowledge which is not yet
available to us. For a short proof see Lee and Vfbornf (2000, Example 6.6.9).
496
Introduction to Mathematics with Maple
+
In( 1 x) it follows that
lim
2-0
+
In( 1 ax)
=a
x
and consequently
lim
n+oo
(
"nzn
1- -
=exp(-x 2 ).
Using the arithmetic-geometric mean inequality, it can be proved similarly
in Inequalities (10.23) and (10.24) that the sequence, with terms fn(2)
(1 - x2/n)n for 0 5 x 5 & and fn(x) = 0 otherwise, is increasing for every
x. It follows from the Monotone Convergence Theorem that
exp(-z2)dx=
lim
n-mo
lfi(l-:)n.
(15.79)
We evaluate the integral I n on the right hand side by changing variables x =
f i c o s t , d x = - s i n t d t and have
We evaluated the integral S n in Example 15.25 where we also proved t h a t
&Sn -+
Hence
m.
(15.81)
Altogether, we have by (15.79), (15.80) and (15.81)
Another important theorem on interchange of limit and integration is the
next theorem.
Integrals
497
Theorem 15.29 (Dominated Convergence Theorem)
that
Assume
(i) the functions f n are integrable on an interval I for every n E N,
(ii) n4co
lim f n ( x ) = f (x)for every x E I ,
(iii) there exists integrable functions g and G such that
g(x) I
fn(x) IG(x)
for all x E I and all n E N.
Then f is integrable on I and
1I
Again, we only illustrate this theorem by an example.
Example 15.37 The r function was defined in Equation (15.74). We wish
to prove the formula
r(t)= lim
n-oo
for
n! nt
t(t
+ I )- - (t + n ) '
a
t > 0. It is not difficult t o show that
ln
(1 - X ) n u t - ' d u
1
1
= nt
(1 - y)nyt-ldy.
The first formula can be proved by induction, the second by substitution, with
u = n y and d u = n d y. Hence we want to show that
Since
we seek an integrable non-negative function G such t h a t 0
5 f n 5 G, where
Introduction to Mathematics with Maple
498
The inequality ln(1 - x / n ) 5 - x / n follows from the power series expansion
of ln(1 x ) and it yields (1 - x / n ) n 5 e-” for 0 5 x 5 n. W e can take
G ( x ) = e-”xt-l, this function is integrable on [0, m[ because it is continuous
and
+
(15.83)
The first inequality guarantees integrability of
Ic > t 1 on [I,m[.
+
15.13
G on [0,1], the second with
Comments and supplements
The basic ideas for integration can be found in antiquity, in the exhaustion
method and in the work of Archimedes. There is a long list of mathematicians contributing to the subject before Newton and Leibniz, including
Fermat, Cavalieri, Kepler; see Eves (1981). Surprisingly, the Fundamental
Theorem of Calculus was discovered by Barrow, a predecessor of Newton at
Cambridge. Newton and Leibniz developed systematic methods for calculating derivatives and integrals and open ways to apply calculus to problems
in other sciences. The logical foundations of Calculus had some important
critics. The mathematicians of the seventeenth and eighteenth century
were aware of the shortcomings of the logical foundations but had different
priorities. D’Alembert even said: “Let’s move ahead, the confidence will
arrive later.” The first definition of integral which can stand up to modern
scrutiny was Riemann’s. Shortly before him, Cauchy proved that the rightsums (and the leftsums) converge to a limit when the maximal length of
the subintervals tended to zero. Riemann not only allowed the tag to float
anywhere in the subinterval but made a decisive step forward by considering the set of (Riemann) integrable functions, namely those functions for
which the Riemann sums had a limit as the maximal length of subintervals
tended to zero. Riemann’s definition was originally a success, but as time
went by, it was realized that the definition was far from ideal. One of the
deficiencies of Riemann theory is that none of the monotone convergence
theorem, the dominated convergence theorem or the Hake theorems are
valid for the Riemann integral. Lebesgue set himself a goal to create an
integration theory such that:
(i) To every function bounded on the interval [a, b] there is a number
Integrals
499
I associated with it such that the following axioms are satisfied. If
I = s,” f = s,” f(z)d z then
(ii)
(iii) for all a , b, c
[f +
(iv) If f 2 0 and a
f+
JC
b
laf
= 0;
< b then
(v) For all h, a , b
(vi)
l1
l d x = 1;
(vii) If f n ( x ) 5 fn+l and f n ( z )+ f ( x ) for every x in [a, b] then
b
b
...J, J f.
fn
=
In Lebesgue’s requirement (vii) we recognize the monotone convergence theorem. Lebesgue created the integration satisfying his axioms. His theory
had an immediate and beneficial impact on mathematical analysis and is
still the integration theory predominantly used by professional mathematicians. Lebesgue, however, did not define the integral for every bounded
function. Instead, he created a class of summable, or Lebesgue integrable
functions. These include all Riemann integrable functions and also many
unbounded functions. However, in Lebesgue theory, Hake’s theorems are
still not valid and neither is the Fundamental Theorem, at least not in such
a simple and powerful form as in Theorem 15.15. Lots of effort was devoted
to making Lebesgue theory accessible to non-specialist physicists and engineers. Some of the users of integration can and do master Lebesgue theory.
Experience shows, however, that Lebesgue theory can be too subtle and
500
Introduction to Mathematics with Maple
too difficult for general consumption. In contrast, the Kurzweil integration
theory is relatively simple and has all the advantages of Lebesgue theory.
This is the reason why we made perhaps an unusual step in adopting it for
an elementary exposition. Kurzweil’s definition is equivalent to two definitions proposed during the time after Lebesgue and before Kurzweil: one
by Perron and another by Denjoy. Theories based on these definitions are
even more demanding that the Lebesgue theory. In recent years, several
publications presented Kurzweil theory in detail: we recommend Lee and
Vfbornf (2000) and Bartle (2001), where further references can be found.
Appendix A
Maple Programming
A.l
A. 1.1
Some Maple programs
Introduction
It is not our aim to expound Maple programming. However, some examples
can be well illuminated by exposing the entire structure of the computation by a Maple program. So we give here the mere rudiments of Maple
programming, just what we need in these examples.
One occasion when readers might find it profitable to write their own
program is when they need to carry out the same operation1 many times.
A Maple procedure starts with its name, followed by the assignment operator :=, the word proc and parameters to be operated on, enclosed in
parenthesis. Hence it looks like
name := proc(parameters)
Wide choice is allowed for the parameters. They can be integers, real or
complex numbers, polynomials or even sets. You must end the procedure either with end proc : or end proc ;.2 The semicolon is better: Maple comes
back and prints the procedure or gives an error message. The program itself consists of commands operating on the parameters, each individual
command ending with a ; character or a : character.
The symmetric difference of two sets is the set which contain all elements
which lie in one of the sets but not in both. Our first procedure computes
symmetric difference of two sets.
>
s d i f f :=proc(A,B)
'Not available in Maple itself.
21n older versions of Maple, use just end;
501
Introduction to Mathematics with Maple
502
>
(A union B) minus (A intersect B);
>
end proc;
sdiff := proc(A, B ) (A union B ) minus (A intersect B ) end proc
Let us test our program
>
A:={1,2,3);
B:={2,3,4);
A
:= (1, 2, 3)
B := (2, 3, 4)
>
sdiff (A,B) ;
A.1.2
The conditional statement
When programming, it is important that the computation be able to branch
according to some condition which can only be tested midway during the
computation itself. This is achieved in Maple by the statement if. The
most basic form of this statement has the structure
if condition(s)
then cornrnand(s) else commands end if;
It is important that you end each if statement with end if;.3 The use
of if is best explained by our next example in which the procedure picks
up from two sets the one which has the smaller number of elements. In
the example we use the command nops(A) which counts the number of
elements of A.
>
smaller :=proc (A, B)
>
if nops(A)<nops(B)
>
else B; end if;
>
end proc;
then A;
31n older versions of Maple you have to use f i instead of end i f .
Maple Programming
smaller := proc(A, B) if nops(A)
503
< nops(B) then A else B end if end proc
Testing the procedure, we have
>
smaller(A,B) ;
Note that our program picked up B as the smaller set; this is as expected.
If the number of elements of A is not smaller than the number of elements
of B then the else alternative is activated. Let us say that we overlooked
this and that we want both sets printed when the number of elements in the
sets is the same. The next procedure does that, and provides an example
of nested if statements. Note the indentation of the nested statements,
which improves readability. If you need to use nested if statements more
often, then you might like to learn the elif statement from Help.
>
>
>
>
Smaller:=proc (A ,B)
if nops(A)<nops(B) then A;
else
if nops(A)=nops(B) then {A,B};
>
else B;
>
>
end if
end if;
>
end proc;
Smaller := proc(A, B )
if nops(A) < nops(B) then A
elseif nops(A) = nops(B) then { B , A}
else B end if
end if
end proc
Testing again gives
Introduction to Mathematics with Maple
504
>
A.1.3
Smaller ( A , B) ;
The while Statement
The while statement allows one or more commands to be executed multiple
times (as a loop). Commands are executed while a stated condition is
satisfied, with execution terminating when the condition fails. The while
statement has the structure
while c o n d i t i o n do command(s)
end do;
Do not forget to end the statement with end do; !4 The following procedure halves a number a until the quotient becomes smaller than
In
the procedure we assign q to be a , in order to prevent this assignment from
taking effect outside the procedure, we use the command local 9;. Actually, Maple automatically makes any variable introduced in a procedure,
local to that procedure.
>
pulka:=proc(a)
>
local q;
>
q:=a;
>
while 10A5*q>i do
>
q:=q/2;
>
end do;
>
q;
> end proc:
Let us test our program for a = 3.
>
pulka(3);
3
524288
401der versions of Maple use od instead of end do.
Maple Programming
505
In the old days when computations were limited to pen and paper the
following formula was used for efficiently computing the square root of a.
Some algebraic manipulation leads to the estimate
Hence in order to obtain ,/Z within k decimal places we let the computation
run while 2(xn - x,+1) 2
>
rt2:=proc(a,k::nonnegint)
>
x:=a:
oldx:=x+lO*(-k) :
> while 2*(oldx-x)>(1/2) *lO*(-k) do
>
>
oldx: =x:
end do:
>
end:
x: =O. 5* (x+a/x)
Warning, 'x' is implicitly declared local
Warning, 'oldx' is implicitly declared local
We test our procedure and compare it with the square root procedure provided by Maple:
> rt2(2,9) ;sqrt (2.0); rt2(7225,8) ;
> sqrt (7225.0) ;
1.414213563
1.414213562
85.00000001
85.00000000
The discrepancies at the last decimal place are not caused by some
imperfection of our program but by Maple using only ten significant digits
Introduction to Mathematics with Maple
506
(as a default) for the computation. Increasing the number of significant
digits to, say twelve, will remove the blemish. Of course, we wrote our
program as an illustration on the use of while and not for calculating the
square root.
A.2 Examples
In our first example we automate the calculation of the greatest common
divisor of two positive5 integers which we did on Page 59 by repeated use
of the command irem.
Example A. 1 (Greatest common divisor, the Euclid algorithm)
The idea is to run irem by using while as long as the result is not zero.
>
>
>
>
>
>
>
>
>
>
mygcd:=proc(m::posint,n::posint)
local a,b,r;
a :=m; b := n;
while b > 0 do
r := irem(a,b);
a := b;
b := r;
end do;
a;
end;
mygcd := proc(m::posint,n::posint)
locala, b, r;
a := m ; b := n ; while0 < bdor := irem(a, b) ; a := b ; b := rend do; a
end proc
Let us test our procedure and compare the results with Maple igcd
> mygcd(987654321123456789,123456789987654321);
> igcd(987654321123456789,123456789987654321);
1222222221
1222222221
5The case when one of the numbers is zero need no computing.
Maple Programming
507
The following example provides a program for calculating the exact value
of the square root of a complex number.
Example A.2
The equation
from Theorem 7.2 can be modified that it covers any complex number a+zb,
not only the case b # 0. This is done by replacing b/lbl by S(b) where S is
defined as follows
S ( x )=
{, ;
for x
<0
for x 2 0
(A.2)
The easy verification is left to readers. The function S differs from the
Maple function signum only at zero. However the command
hvsignumO= a;
redefines signum at zero to be a. We can now write a procedure for computing the exact value of the square root of a complex number, we call it
compsqrt
>
>
compsqrt :=proc ( A )
local a, b, t:
>
a:=Re(A); b:=Im(A);
>
,EnvsignumO:=l;
> t :=sqrt (an2+bn2);
> sqrt( (t+a)/2)+I*signum(b)*sqrt(
> end proc:
(t-a)/2)
Here are a few numerical examples
>
compsqrt (-1) ;
>
compsqrt (-1) ;
I
>
compsqrt (3-4*1);
2-1
Now we save the procedure compsqrt in a file with the extension .m. We
call the file Ourprocs .m. Note the quotation marks around the file name.
Introduction to Mathematics with Maple
508
>
save compsqrt, "Ourprocs.mn;
In a future session you can recall the file and the function by the command read. The following Maple worksheet illustrates this. There is a
similarity to calling a package via the command with(package) ;. In each
case, one has to recall the file first, and then the procedure can be used.6
>
restart;
First we call the file with the read command then we use the procedure for
finding the exact value of the square root
>
>
read "Ourprocs.mII ;
>
compsqrt (-1) ;
-1 f i - z 1I f i
2
Other Maple objects can be saved the same way we saved compsqrt.
Several functions, expressions or programs can be saved in a file. The
structure of the command is
save function, expression,
. . . , "myfi1e.m"
Note the comma after the words function and expression.
The next Example illustrates the practical use of the idea in the proof
of Theorem 12.15.
Example A.3 (Bisection method for finding roots) We halve the
interval ( A , B ) to locate a zero of a given f within ten significant digits.
6Function, expression.
509
Maple Programming
> bisect:=proc(f ,A,B)
> local a,b,g;
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
if O>evalf (f (A))
then g:=f
else g:=-f
end if;
#
#now we make sure that f takes values
#of opposite signs at the ends of the interval
#
if evalf (g(B))<O then
Error ( ' f (A) *f (B) must be negative' )
else a:=A; b:=B;
while evalf ( 10nlO* (b-a)) > l do
if O>evalf(g(a/2+b/2)) then a:=(1/2)*a
b:=b;
else b:=(l/2)*a+(1/2)*b;
a:=a;
end if;
end do;
+(1/2)*b;
#
#it is easier to read it in decimal form,
#
(a+b) /2, evalf ( (a+b) /2) ;
end if;
end proc;
bisect := proc(f, A, B )
locala, b, g;
ifevalf(f(A)) < 0 theng := f e l s e g := -fend if;
ifevalf(g(B)) < 0 thenError('f(A) * f ( B ) must be negative')
else
a :=A;
b:= B ;
while 1 < evalf (10000000000* b - 10000000000 * a) do
if evalf ( g ( 1/2 * a 1/2 * b)) < 0
t h e n a := 1/2 * a 1/2 * b ; b := b
else b := 1/2 * a 1/2 * b ; a := a
end if
end do;
1/2 * a 1/2 * b, evalf(l/2 * a 1/2 * b)
end if
end proc
+
+
+
+
+
Introduction to Mathematics with Maple
510
An advantage of this method is that it always finds a zero. Other
methods might become unstable if more than one zero of f is present in
[A,B]. We now illustrate the use of the procedure on several examples.
>
f:=x->x^3-3*x+l;
f :=x-+x3-3x+1
The polynomial has zeros in [-2, -11, [0,13 and [l,21. Let us see what
happens for [-3,7] and [-2,5].
-258300740771, -1.879385242
137438953472
421 137386305 1.532088886
274877906944 '
The use of bisect is not restricted to polynomials.
> f:=x->(1/2)*x-sin(x);
f := x
1
2
-+- x - sin(x)
>
bisect (f ,Pi/2,Pi) ;
>
41462209955 T , 1.895494267
68719476736
save bisect, "Bisect.mtt ;
References
Arrow, K. (1963). Social choice and individual values. New Haven: Yale
University Press.
Bartle, R. G. (2001). A modern theory of integration. Providence, Rhode
Island: American Mathematical Society.
Boas, R. P. (1972). A primer of real functions. The Mathematical Association of America.
Eves, H. (1981). Great moments in mathematics (after 1650). Mathematical Association of America.
Gradshtein, I., & Ryzhik, I. (1996). Table of integrals, series and products
(computer file). Boston: Academic Press.
Hadlock, C. R. (1978). Field theory and its classical problems. Mathematical Association of America.
Halmos, P. R. (1974). Naive set theory. Springer.
Kelly, J. (1978). Arrow impossibility theorems. New York: Academic Press.
Knopp, K. (1956). Infinite sequences and series. New York: Dover.
Kurosh, A. (1980). Higher algebra. Mir.
Landau, E. (1951). Foundations of analysis. Chelsea.
Lee, P. Y., & Vfbornf, R. (2000). The integral: An easy approach after
Kurxweil and Henstock. Cambridge, UK: Cambridge University Press.
Matsuoka, Y. (1961). An elementary proof of the formula
l/k2 =
7r2/6. Amer. Math. Monthly, 68, 485-487. (Reprinted in Selected
papers on calculus, The Mathematical Association of America)
McCarthy, J. (1953). An everywhere continuous nowhere differentiable
function. Amer. Math. Monthly, 60, 709.
Spivak, M. (1967). Calculus. New York, Amsterdam: W.A. Benjamin, Inc.
Stromberg, K. R. (1981). Foundations of analysis. Belmont, California:
Wadsworth.
xE1
511
512
References
Vyborny, R. (1987). Differentiation of power series. Amer. Math. Monthly,
94, 369-370.
V$born$, R. (2001). Bolzano’s anniversary. The Australian Mathematical
Society Gazette, 28, 177-183.
Vyborny, R., & Nester, R. (1989). L’HGpital rule, a counterexample. Elemente der Mathematik, 44, 116-121.
Youse, B. (1972). Introduction to real analysis. Boston: Allyn and Bacon.
Index of Maple commands
used in this book
The following table provides a brief description of the Maple commands
used in this book, together with the pages on which they are described, and
some of the pages on which they are used.
Most of the commands in the following table are set in the type used
for Maple commands throughout this book, such as collect. Any which
are set in normal type, such as Exit, are not, strictly, Maple commands,
but are provided here to assist in running a Muple session.
Table A.l: List of Maple commands used in this book
Command
Description
Pages
abs (x)
annuity
The absolute value of the number x
Calculates quantities relating to annuities:
requires the finance package to be loaded
Calculates and graphs the eight circles
which touch three specified circles: requires
the geometry package to be loaded
Make an assumption about a variable:
holds for the remainder of the worksheet (or
until restart)
Make an assumption about a variable:
holds only for the command it follows
Evaluates the binomial coefficient
The ceiling of x (the smallest integer not
less than 2)
Carry out a change of variables in an integral
Collect similar terms together
A collection of commands for solving
problems in combinatorial theory: loaded
by using the command with(combinat)
Combine expressions into a single expression
The complex conjugate B of a complex
number z
Continued on
75
31
Apollonius
assume
assuming
binomial
ceil (x)
changevar
collect
combinat
combine
conjugate
513
30
112
111
151
75, 118
474
24, 26
37
25, 26
199
next page
514
Index of Maple commands
List of Maple commands (continued)
Command
Description
Pages
convert
Convert from one form to another
cos (x)
dif f
D(f ( a >
The trigonometric function cos x
Differentiate a function
Evaluates the derivative of the function f at
x=a
Set the number of digits used in calculations
Find all the divisors of an integer: requires
the numtheory package to be loaded
The error function:
17, 17, 173,
184
75
36 1
359
Digits
divisors
e r f (x)
evalc
f solve
Changes a complex valued expression into
the form a bz
Evaluate an expression in numerical terms
Exit from Maple session
The base of natural logarithms:
e = 2.7182818284.. .
The exponential function ex
Expand a n expression into individual terms
Factorise a polynomial
The same as f a c t o r except that
calculations are carried out in a field
modulo a prime p
A collection of commands for financial
calculations: loaded by using the command
with (f inance )
The floor of z (the largest integer not greater
than x)
Find numerical solution(s) of an equation
geometry
A collection of commands for solving
evalf
Exit
exp(1)
exp (XI
expand
factor
Fact o r
finance
f l o o r (x)
+
15, 16
30, 215
465
199, 203
16, 17, 141
8
12
75
21, 25, 26
22, 26, 187
189
31
75,118
94, 209,
2 10
30
problems in geometry: loaded by using the
command with (geometry)
Continued on next page
515
Index of Maple commands
List of Maple commands (continued)
Command
Description
Pages
int
intersect
intparts
iquo
Integrate a function
Intersection of two sets
Carry out an integration by parts
The quotient when one integer is divided by
another
The remainder when one integer is divided
by another
Test the value of a Boolean function
Find integer solutions of equations
Finds the ith prime number
The logarithmic function In x
The logarithmic function logx (the same as
In x)
The maximum of two or more real numbers
Map elements of the set S into another set
by the function f
The minimum of two or more real numbers
Difference of two sets
Counts the number of items in a list or set
Collects a sum of fractions over a common
denominator
A collection of commands for solving
problems in number theory: loaded by
using the command with(numtheory)
Select one or more items from a list or set
The value of 7r: 3.1415926535 . . .
Plot a function
Finds the sets comprising the power set of a
given set: requires the combinat package to
be loaded
The product of a number of terms
Quotient when one polynomial is divided by
another
Continued on
443
38
470
29
irem
is
isolve
ithprime
I n (x>
log (XI
max(x,y,z)
map(f, s)
m i n h , y,z>
minus
nops
normal
numthe ory
OP
Pi
plot
powerset
product
QUO
29
80
27, 28
188
75
75
75
377
75
38
40
23, 26
30
41, 81
12
81
37
143
29, 176
next page
516
Index of Maple commands
List of Maple commands (continued)
Command
Description
Pages
Quo
The same as quo except that calculations
are carried out in a field modulo a prime p
Rationalise a result with a complicated
denominator
Remainder when one polynomial is divided
by another
The same as rem except that calculations
are carried out in a field modulo a prime p
Remove one or more items from a set or a
list
Reset the Maple environment to its starting
setting with no variables defined
Finds rational roots of a polynomial
Selects some elements from a list
Used to generate an expression sequence
Simplify a complicated expression
177
rationalize
rem
Rem
remove
restart
roots
select
seq
simplify
s i n (x)
solve
sort
spline
sqrt(x)
subs
SUm
surd
t d x )
tau
taylor
The trigonometric function sin x
Find a solution of an equation
Sort a sequence of terms
Approximate a function by straight lines or
curves
The square root function
Substitutes a value, or an expresseion, into
another expression
The sum of a series
Finds the real value of an odd root of a real
number
The trigonometric function tan z
The number of factors of an integer
Used to find the product of two polynomials,
or a Taylor series
27
29, 176
177
81
19
216
80
41
22,26, 142,
143
75
108, 209
24, 26
352
11, 12, 13,
75,
26
141, 143,
289
256
75
30
173, 183
Continued on next page
517
Index of Maple commands
List of Maple commands (continued)
Command
Description
Pages
union
with
Union of two sets
The command with(newpackage1 loads the
additional Maple commands available in the
package newpackage
Using worksheets in Maple
38
30
worksheets
5, 6
This page intentionally left blank
Index
Euclid, 55
field, 97
!, 11
8
1, 72
'I,
GF(2), 99, 192
order, 100
Peano, 121
ring, 57, 98
0,xi
*,
11
+, 11
-, 11
/, 11
backspace, 7
Barrow.1, 498
Bernoulli, 309
inequality, 157
rule, 390, 392
Bernoulli, J , 390
binomial coefficient, 400
Bolzano, 271, 273
function, 402
Bolzano-Cauchy principle, 451
bound
lower, 115
upper, 115
bounded, 115
bounded above, 115
bounded below, 115
;, 6
:=, 18
?, 9
#, 13
%, 8
-, 11
Abel, 220, 306, 467
abs, 76
algebra, 18
antiderivative, 455
a r c c o t , 427
Archimedes, 309, 498
arcsin, 425
arctan, 426
Argand, 199
arithmetical operators, 11
Arrow, Kenneth, 54
assigning variables, 18
axiom
least upper bound, 114, 116
of choice, 236
axioms, 1, 53
GF(4), 193
Cantor, Georg
diagonal process, 235
Cardano H., 221
Cartesian product, see sets,
Cartesian product
case sensitivity, 7
Cauchy, 270, 309
condensation test, 304
519
520
Introduction to Mathematics with Maple
convergence principle, 272
definition, 324
mean value theorem, 388
product, 308
Cavalieri, 498
ceil, 76
change of variables, 472, 476
characteristic function, 72
coefficient binomial, 400
Cohen, Paul, 56
comments, 13
complex number
absolute value, 196
complex conjugate, 196
geometric representation, 199
imaginary, 196
imaginary part, 195
purely imaginary, 196
real part, 195
square root, 197
trigonometric form, 199
constants
in Maple, 12
continuity
at a point, 332
from the left, 333
from the right, 333
of the inverse, 340
on intervals, 338
uniform, 346
convergence
uniform, 493
cos, 76
cosh, 430
cot, 427
Cousin
lemma, 343
D’Alembert, 498
limit test, 298
test, 297
Darboux
property, 355
de Morgan, Augustus, 44
decimal fraction, 162
Dedekind, 124, 306
Dedekind cut, 124
definition
Cauchy, 324
derivative, 358
limit, 316
limit from the left, 313
limit from the right, 313
definitions
Absolute and Conditional
Convergence, 293
nth root of a, 153
degree
of a polynomial, 171
Denjoy.A, 500
derivative
definition, 358
from the left, 359
from the right, 359
Descartes R., 218
Dini, 401
derivate, 401
derivates, 401
Dirichlet, 306
divisor, 184
domain, 63
e, 12
Euclid, 51, 55
Euler, 488
formulae, 421
Euler ’s
integral, 488, 492
evalf 0 , 16
exact numbers, 13, 14
exp, 76
expression sequence, 39
Fermat, 498
Ferro del S., 221
field
isomorphic, 171
floating point number, 14
floating point numbers, 13
floor, 76
formal power series, 169
formula
Index
reduction, 471
Wallis, 470
formulae
Euler, 421
Fourier J., 218
fraction
decimal, 162
function, 69
bijection, 90
decreasing, 257
increasing, 257
injective, 90
inverse, 91
one-to-one, 90
onto, 90
periodic, 419
step, 450
surjective, 90
uniformly continuous, 346
Functions in MupZe, 75
Godel, Kurt, 55, 56
Galois, 220
Gamma, 492
function, 492
Gauss
plane, 199
Gauss K. F, 213
greatest lower bound, 117
Hake, 484, 490
Hake ’ s
theorem, 484, 490, 498, 499
Heaviside, Oliver, 72
help, 9
Henstock, R, 431
Hermite, C., 261
Hilbert, David, 237
Horner, 180
if statement, 502
increasing at a point, 378
induction hypothesis, 131
inequality
arithmetic-geometric mean, 158
Bernoulli, 157
521
inference, 131
infimum, 117
inflection, 385
integral, 438, 472
definition, 438
over unbounded intervals, 489
integral domain, 174
integration
by parts, 468
by substitution, 472, 476
interrupting commands, 7
interval, 104
closed, 104
end points, 104
open, 104
isomorphic, 171
isomorphism, 171
Kepler, 498
Kronecker, Leopold, 113
Kurzweil
integrable, 438
Lagrange, 480
mean value theorem, 387, 480
remainder, 480, 481
Laplace, 495
integral, 495
Lebesgue, Henri, 498
axioms, 499
integration, 499
Leibniz, 498
formula, 380
series, 414
test, 292
Library of functions, 75
limit
at infinity, 316
limit inferior, 353
limit superior, 353
list, 39
In, 76
local, 504
log, 76
Map 1 e
Introduction to Mathematics with Maple
522
commands, 513
integration, 443
Using
convert0 , 17
collect0,24
diffo, 360
Digitso, 15
expando, 21
factor0, 22
int 0 , 443
normal 0 , 23
simplify(), 22
sort(), 24
Maclaurin, 400
max, 76
maximum
local, 375
mean
arithmetic, 158
geometric, 157
min, 76
minimum
local, 375
Moivre, 201
multiple commands, 7
N, the
set of natural numbers,
43
No, the set of non-negative
integers, 43
Newton, 498
binomial theorem, 150
Leibniz formula, 457
nops, 502
Oresme, 309
IF',
the set of positive real
numbers, 43
Peano, 477
axiomatic approach, 166
axioms, 121, 127
remainder, 397
Peano, Giuseppe, 114
Perron.0, 500
pi, 12
Polya, G, 134
polynomial, 167, 170
unending, 169
polynomials
multiplication, 170, 173
ring of, 170
previous results, 8
proc, 501
products
in Maple, 143
Q , the set of rational numbers,
43
quitting Map'le, 8
R , the set of real numbers, 43
Raabe
test, 304
radius
of convergence, 301
range, 63
reducible, 185
relation, 63
inverse, 91
reflexive, 66
symmetric, 66
transitive, 66
Riemann
integrable, 438
sum, 432
Riemann, Bernard, 306
ring
isomorphic, 171
of polynomials, 170
root
multiple, 217
primitive of unity, 204
rational, 214
Ruffini Paulo, 220
Russell, Bertrand, 53
scheme
Horner, 180
semicolons, 6
sequence, 145
decreasing, 257
Index
Fibonacci, 148
increasing, 257
series
formal power, 169
set
countable, 232
sets
Cartesian product, 43
countable, 232
difference, 36
enumerable, 232
intersection, 35
union, 35
sgn, 74
signum, 74
sin, 76
singleton, 53
sinh, 430
spaces, 7
sqrt, 11, 76
starting Maple, 4
statement if, 502
step function, 450
stopping Maple, 8
Stusm, 218
sums
in Maple, 141
tagged division, 431
&fine, 342, 437
tan, 76
Tarski, Alfred, 55
Taylor, 400
polynomial, 180
Taylor expansion, 181
Taylor polynomial, 181
Taylor theorem, 477
test
D’Alembert, 297
D’Alembert limit, 298
Leibniz, 292
Raabe, 304
root, 299
theorem
binomial, 150
Bolzano-Cauchy, 271, 291, 354
523
Bolzano-Weierstrass, 273
dominated convergence, 497
greatest lower bound, 117
least upper bound, 124
monotone convergence, 495
square root, 198
Taylor, 477
The Intermediate Value
Theorem, 340
Weierstrass, 344, 347
Vieta, 214
von Neumann, John, 96
Wallis, 470
formula, 471
What is Maple?, 4
Z, the set of integers, 43
zero polynomial, 171
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