Symbolic Math Toolbox User's Guide

Symbolic Math Toolbox User's Guide
Symbolic Math
Toolbox
For Use with MATLAB
®
Computation
Visualization
Programming
User’s Guide
Version 2
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Symbolic Math Toolbox User’s Guide
 COPYRIGHT 1993 - 1998 by The MathWorks, Inc.
The software described in this document is furnished under a license agreement. The software may be used
or copied only under the terms of the license agreement. No part of this manual may be photocopied or reproduced in any form without prior written consent from The MathWorks, Inc.
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Other product or brand names are trademarks or registered trademarks of their respective holders.
Printing History: August 1993
October 1994
May 1997
September 1998
First printing
Second printing
Third printing for Symbolic Math Toolbox 2.0
Updated for Release 11 (online only)
Contents
Tutorial
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-2
Getting Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4
Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
Symbolic Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
Creating Symbolic Variables and Expressions . . . . . . . . . . . . . 1-6
Symbolic and Numeric Conversions . . . . . . . . . . . . . . . . . . . . . . 1-7
Constructing Real and Complex Variables . . . . . . . . . . . . . . 1-9
Creating Abstract Functions . . . . . . . . . . . . . . . . . . . . . . . . 1-10
Using sym to Access Maple Functions . . . . . . . . . . . . . . . . . 1-11
Example: Creating a Symbolic Matrix . . . . . . . . . . . . . . . . . 1-11
The Default Symbolic Variable . . . . . . . . . . . . . . . . . . . . . . 1-13
Creating Symbolic Math Functions . . . . . . . . . . . . . . . . . . . . . 1-15
Using Symbolic Expressions . . . . . . . . . . . . . . . . . . . . . . . . . 1-15
Creating an M-File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-16
Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Differentiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Integration with Real Constants . . . . . . . . . . . . . . . . . . . . .
Real Variables via sym . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Symbolic Summation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Taylor Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Extended Calculus Example . . . . . . . . . . . . . . . . . . . . . . . . . . .
Plotting Symbolic Functions . . . . . . . . . . . . . . . . . . . . . . . . .
1-17
1-17
1-21
1-23
1-26
1-28
1-30
1-31
1-33
1-33
i
Simplifications and Substitutions . . . . . . . . . . . . . . . . . . . . .
Simplifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
collect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
expand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
horner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
simplify . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
simple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Substitutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
subexpr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
subs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-47
1-47
1-48
1-49
1-49
1-50
1-52
1-52
1-56
1-56
1-59
Variable-Precision Arithmetic . . . . . . . . . . . . . . . . . . . . . . . . .
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example: Using the Different Kinds of Arithmetic . . . . . . . . .
Rational Arithmetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variable-Precision Numbers . . . . . . . . . . . . . . . . . . . . . . . . .
Converting to Floating-Point . . . . . . . . . . . . . . . . . . . . . . . .
Another Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-64
1-64
1-65
1-65
1-66
1-67
1-67
Linear Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Basic Algebraic Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Linear Algebraic Operations . . . . . . . . . . . . . . . . . . . . . . . . . . .
Eigenvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Jordan Canonical Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . .
Eigenvalue Trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1-69
1-69
1-70
1-74
1-81
1-82
1-86
Solving Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-96
Solving Algebraic Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-96
Several Algebraic Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 1-104
Single Differential Equation . . . . . . . . . . . . . . . . . . . . . . . . . . 1-107
Example 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-108
Example 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-108
Example 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-109
Several Differential Equations . . . . . . . . . . . . . . . . . . . . . . . . 1-109
Integral Transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-112
The Fourier and Inverse Fourier Transforms . . . . . . . . . . . . 1-112
The Laplace and Inverse Laplace Transforms . . . . . . . . . . . . 1-120
ii
The Z– and Inverse Z–transforms . . . . . . . . . . . . . . . . . . . . . . 1-126
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-128
Special Mathematical Functions . . . . . . . . . . . . . . . . . . . . . . 1-130
Diffraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-132
Using Maple Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Simple Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Vectorized Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Debugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Trace Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Status Output Argument . . . . . . . . . . . . . . . . . . . . . . . . . .
1-136
1-136
1-139
1-141
1-141
1-141
Extended Symbolic Math Toolbox . . . . . . . . . . . . . . . . . . . .
Packages of Library Functions . . . . . . . . . . . . . . . . . . . . . . . .
Procedure Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Precompiled Maple Procedures . . . . . . . . . . . . . . . . . . . . . . . .
1-143
1-143
1-145
1-148
Reference
2
Compatibility Guide
A
Compatibility with Earlier Versions . . . . . . . . . . . . . . . . . . . . A-2
Obsolete Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-3
iii
iv
Contents
1
Tutorial
Introduction . . . . . . . . . . . . . . . . . . . . 1-2
Getting Help . . . . . . . . . . . . . . . . . . . . 1-4
Getting Started . . . . . . . . . . . . . . . . . .
1-5
Calculus . . . . . . . . . . . . . . . . . . . . . . 1-17
Simplifications and Substitutions . . . . . . . . . . 1-47
Variable-Precision Arithmetic
. . . . . . . . . . . 1-64
Linear Algebra . . . . . . . . . . . . . . . . . . . 1-69
Solving Equations
Integral Transforms
. . . . . . . . . . . . . . . . . 1-96
. . . . . . . . . . . . . . . 1-112
Special Mathematical Functions . . . . . . . . . . 1-130
Using Maple Functions . . . . . . . . . . . . . . 1-136
Extended Symbolic Math Toolbox . . . . . . . . . 1-143
1
Tutorial
Introduction
The Symbolic Math Toolboxes incorporate symbolic computation into
MATLAB®’s numeric environment. These toolboxes supplement MATLAB’s
numeric and graphical facilities with several other types of mathematical
computation:
Facility
Covers
Calculus
Differentiation, integration, limits, summation,
and Taylor series
Linear Algebra
Inverses, determinants, eigenvalues, singular
value decomposition, and canonical forms of
symbolic matrices
Simplification
Methods of simplifying algebraic expressions
Solution of
Equations
Symbolic and numerical solutions to algebraic and
differential equations
Variable-Precision
Arithmetic
Numerical evaluation of mathematical expressions
to any specified accuracy
Transforms
Fourier, Laplace, z-transform, and corresponding
inverse transforms
Special
Mathematical
Functions
Special functions of classical applied mathematics
The computational engine underlying the toolboxes is the kernel of Maple, a
system developed primarily at the University of Waterloo, Canada, and, more
recently, at the Eidgenössiche Technische Hochschule, Zürich, Switzerland.
Maple is marketed and supported by Waterloo Maple, Inc.
These versions of the Symbolic Math Toolboxes are designed to work with
MATLAB 5 and Maple V Release 4.
There are two toolboxes. The basic Symbolic Math Toolbox is a collection of
more than one-hundred MATLAB functions that provide access to the Maple
1-2
Introduction
kernel using a syntax and style that is a natural extension of the MATLAB
language. The basic toolbox also allows you to access functions in Maple’s
linear algebra package. The Extended Symbolic Math Toolbox augments this
functionality to include access to all nongraphics Maple packages, Maple
programming features, and user-defined procedures. With both toolboxes, you
can write your own M-files to access Maple functions and the Maple workspace.
The following sections of this Tutorial provide explanation and examples on
how to use the toolboxes.
Section
Covers
“Getting Help”
How to get online help for Symbolic Math
Toolbox functions
“Getting Started”
Basic symbolic math operations
“Calculus”
How to differentiate and integrate symbolic
expressions
“Simplifications and
Substitutions”
How to simplify and substitute values into
expressions
“Variable-Precision
Arithmetic”
How to control the precision of
computations
“Linear Algebra”
Examples using the toolbox functions
“Solving Equations”
How to solve symbolic equations
“Integral Transforms”
Fourier, Laplace, and z-transforms
“Special Mathematical
Functions”
How to access Maple ’s special math
functions
“Using Maple Functions”
How to use Maple ’s help, debugging, and
user-defined procedure functions
“Extended Symbolic Math
Toolbox”
Features of the Extended Symbolic Math
Toolbox
Chapter 2, “Reference,” provides detailed descriptions of each of the functions
in the toolboxes.
1-3
1
Tutorial
Getting Help
There are two ways to find information on using Symbolic Math Toolbox
functions. One, of course, is to read this manual! The other is to use MATLAB’s
command line help system. Generally, you can obtain help on MATLAB
functions simply by typing
help function
where function is the name of the MATLAB function for which you need help.
This is not sufficient, however, for some Symbolic Math Toolbox functions. The
reason? The Symbolic Math Toolbox “overloads” many of MATLAB’s numeric
functions. That is, it provides symbolic-specific implementations of the
functions, using the same function name. To obtain help for the symbolic
version of an overloaded function, type
help sym/function
where function is the overloaded function’s name. For example, to obtain help
on the symbolic version of the overloaded function, diff, type
help sym/diff
To obtain information on the numeric version, on the other hand, simply type
help diff
How can you tell whether a function is overloaded? The help for the numeric
version tells you so. For example, the help for the diff function contains the
section
Overloaded methods
help char/diff.m
help sym/diff.m
This tells you that there are two other diff commands that operate on
expressions of class char and class sym, respectively. See the next section for
information on class sym. For more information on overloaded commands, see
Chapter 14 of the Using MATLAB guide.
You can use the mhelp command to obtain help on Maple commands. For
example, to obtain help on the Maple diff command, type mhelp diff. This
returns the help page for the Maple diff function. For more information on the
1-4
Getting Started
mhelp command, type help mhelp or read the section “Using Maple Functions”
in this Tutorial.
Getting Started
This section describes how to create and use symbolic objects. It also describes
the default symbolic variable. If you are familiar with version 1 of the Symbolic
Math Toolbox, please note that version 2 uses substantially different and
simpler syntax.
(If you already have a copy of the Maple V Release 4 library, please see the
reference page for mapleinit before proceeding.)
To get a quick online introduction to the Symbolic Math Toolbox, type demos at
the MATLAB command line. MATLAB displays the MATLAB Demos dialog
box. Select Symbolic Math (in the left list box) and then Introduction (in the
right list box).
Symbolic Objects
The Symbolic Math Toolbox defines a new MATLAB data type called a
symbolic object or sym (see Chapter 14 in Using MATLAB for an introduction
1-5
1
Tutorial
to MATLAB classes and objects). Internally, a symbolic object is a data
structure that stores a string representation of the symbol. The Symbolic Math
Toolbox uses symbolic objects to represent symbolic variables, expressions, and
matrices.
Creating Symbolic Variables and Expressions
The sym command lets you construct symbolic variables and expressions. For
example, the commands
x = sym('x')
a = sym('alpha')
create a symbolic variable x that prints as x and a symbolic variable a that
prints as alpha.
Suppose you want to use a symbolic variable to represent the golden ratio
1+ 5
ρ = ----------------2
The command
rho = sym('(1 + sqrt(5))/2')
achieves this goal. Now you can perform various mathematical operations on
rho. For example
f = rho^2 – rho – 1
f =
(1/2+1/2*5^(1/2))^2-3/2-1/2*5^(1/2)
simplify(f)
returns
0
Now suppose you want to study the quadratic function f = ax2 + bx + c. The
statement
f = sym('a*x^2 + b*x + c')
assigns the symbolic expression ax2 + bx + c to the variable f. Observe that in
this case, the Symbolic Math Toolbox does not create variables corresponding
1-6
Getting Started
to the terms of the expression, a, b, c, and x. To perform symbolic math
operations (e.g., integration, differentiation, substitution, etc.) on f, you need
to create the variables explicitly. You can do this by typing
a
b
c
x
=
=
=
=
sym('a')
sym('b')
sym('c')
sym('x')
or simply
syms a b c x
In general, you can use sym or syms to create symbolic variables. We
recommend you use syms because it requires less typing.
Symbolic and Numeric Conversions
Let’s return to the golden ratio
1+ 5
ρ = ----------------2
The sym function has four options for returning a symbolic representation of
the numeric value rho = (1 + sqrt(5)/2). The 'f' option
sym(rho,'f')
returns a symbolic floating-point representation
'1.9e3779b97f4a8'*2^(0)
The 'r' option
sym(rho,'r')
returns the rational form
7286977268806824*2^(–52)
1-7
1
Tutorial
This is the default setting for sym. That is, calling sym without a second
argument is the same as using sym with the 'r' option.
sym(rho)
ans =
7286977268806824*2^(–52)
sym(.25)
ans =
1/4
The third option 'e' returns the rational form of rho plus the difference
between the theoretical rational expression for rho and its actual (machine)
floating-point value in terms of eps (the floating-point relative accuracy)
sym(rho,'e')
ans =
7286977268806824*2^(–52)
In this case, the theoretical and actual floating-point values for rho are the
same. For 1/3, however, we obtain
sym(1/3,'e')
ans =
1/3-eps/12
The fourth option 'd' returns the decimal expansion of rho up to the number
of significant digits specified by digits.
sym(rho,'d')
ans =
1.6180339887498949025257388711907
1-8
Getting Started
The default value of digits is 32 (hence, sym(rho,'d') returns a number with
32 significant digits), but if you prefer a shorter representation, use the digits
command as follows.
digits(7)
sym(rho,'d')
ans =
1.618034
A particularly effective use of sym is to convert a matrix from numeric to
symbolic form. The command
A = hilb(3)
generates the 3-by-3 Hilbert matrix.
A =
1.0000
0.5000
0.3333
0.5000
0.3333
0.2500
0.3333
0.2500
0.2000
By applying sym to A
A = sym(A)
you can obtain the (infinitely precise) symbolic form of the 3-by-3 Hilbert
matrix.
A =
[
1, 1/2, 1/3]
[ 1/2, 1/3, 1/4]
[ 1/3, 1/4, 1/5]
Constructing Real and Complex Variables
The sym command allows you to specify the mathematical properties of
symbolic variables by using the 'real' option. That is, the statements
x = sym('x','real'); y = sym('y','real');
1-9
1
Tutorial
or more efficiently
syms x y real
z = x + i*y
create symbolic variables x and y that have the added mathematical property
of being real variables. Specifically this means that the expression
f = x^2 + y^2
is strictly nonnegative. Hence, z is a (formal) complex variable and can be
manipulated as such. Thus, the commands
conj(x), conj(z), expand(z*conj(z))
return the complex conjugates of the variables
x, x – i*y, x^2 + y^2
The conj command is the complex conjugate operator for the toolbox. If
conj(x) == x returns 1, then x is a real variable.
To clear x of its “real” property, you must type
syms x unreal
or
x = sym('x','unreal')
The command
clear x
does not make x a nonreal variable.
Creating Abstract Functions
If you want to create an abstract (i.e., indeterminant) function f(x), type
f = sym('f(x)')
Then f acts like f(x) and can be manipulated by the toolbox commands. To
construct the first difference ratio, for example, type
df = (subs(f,'x','x+h') – f)/'h'
1-10
Getting Started
or
syms x h
df = (subs(f,x,x+h)–f)/h
which returns
df =
(f(x+h)-f(x))/h
This application of sym is useful when computing Fourier, Laplace, and
z-transforms (see the section “Integral Transforms”).
Using sym to Access Maple Functions
Similarly, you can access Maple’s factorial function k!, using sym.
kfac = sym('k!')
To compute 6! or n!, type
syms k n
subs(kfac,k,6), subs(kfac,k,n)
ans =
720
ans =
n!
Or, if you want to compute, for example, 12!, simply use the prod function
prod(1:12)
Example: Creating a Symbolic Matrix
A circulant matrix has the property that each row is obtained from the previous
one by cyclically permuting the entries one step forward. We create the
circulant matrix A whose elements are a, b, and c, using the commands
syms a b c
A = [a b c; b c a; c a b]
1-11
1
Tutorial
which return
A
[
[
[
=
a, b, c ]
b, c, a ]
c, a, b ]
Since A is circulant, the sum over each row and column is the same. Let’s check
this for the first row and second column. The command
sum(A(1,:))
returns
ans =
a+b+c
The command
sum(A(1,:)) == sum(A(:,2)) % This is a logical test.
returns
ans =
1
Now replace the (2,3) entry of A with beta and the variable b with alpha. The
commands
syms alpha beta;
A(2,3) = beta;
A = subs(A,b,alpha)
return
A =
[
a, alpha,
c]
[ alpha,
c, beta]
[
c,
a, alpha]
From this example, you can see that using symbolic objects is very similar to
using regular MATLAB numeric objects.
1-12
Getting Started
The Default Symbolic Variable
When manipulating mathematical functions, the choice of the independent
variable is often clear from context. For example, consider the expressions in
the table below.
Mathematical Function
MATLAB Command
f = xn
f = x^n
g = sin(at+b)
g = sin(a*t + b)
h = Jv(z)
h = besselj(nu,z)
If we ask for the derivatives of these expressions, without specifying the
independent variable, then by mathematical convention we obtain f' = nxn,
g' = a cos(at + b), and h' = Jv(z)(v/z)–Jv+1(z). Let’s assume that the independent
variables in these three expressions are x, t, and z, respectively. The other
symbols, n, a, b, and v, are usually regarded as “constants” or “parameters.” If,
however, we wanted to differentiate the first expression with respect to n, for
example, we could write
d
d n
-------f ( x ) or -------x
dn
dn
to get xn ln x.
By mathematical convention, independent variables are often lower-case
letters found near the end of the Latin alphabet (e.g., x, y, or z). This is the idea
behind findsym, a utility function in the toolbox used to determine default
symbolic variables. Default symbolic variables are utilized by the calculus,
simplification, equation-solving, and transform functions. To apply this utility
to the example discussed above, type
syms a b n nu t x z
f = x^n; g = sin(a*t + b); h = besselj(nu,z);
This creates the symbolic expressions f, g, and h to match the example. To
differentiate these expressions, we use diff.
diff(f)
1-13
1
Tutorial
returns
ans =
x^n*n/x
See the section “Differentiation” for a more detailed discussion of
differentiation and the diff command.
Here, as above, we did not specify the variable with respect to differentiation.
How did the toolbox determine that we wanted to differentiate with respect to
x? The answer is the findsym command.
findsym(f,1)
which returns
ans =
x
Similarly, findsym(g,1) and findsym(h,1) return t and z, respectively. Here
the second argument of findsym denotes the number of symbolic variables we
want to find in the symbolic object f, using the findsym rule (see below). The
absence of a second argument in findsym results in a list of all symbolic
variables in a given symbolic expression. We see this demonstrated below. The
command
findsym(g)
returns the result
ans =
a, b, t
findsym Rule: The default symbolic variable in a symbolic expression is the
letter that is closest to 'x' alphabetically. If there are two equally close, the
letter later in the alphabet is chosen.
1-14
Getting Started
Here are some examples:
Expression
Variable Returned By
findsym
x^n
x
sin(a*t+b)
t
besselj(nu,z)
z
w*y + v*z
y
exp(i*theta)
theta
log(alpha*x1)
x1
y*(4+3*i) + 6*j
y
sqrt(pi*alpha)
alpha
Creating Symbolic Math Functions
There are two ways to create functions:
• Use symbolic expressions
• Create an M-file
Using Symbolic Expressions
The sequence of commands
syms x y z
r = sqrt(x^2 + y^2 + z^2)
t = atan(y/x)
f = sin(x*y)/(x*y)
generates the symbolic expressions r, t, and f. You can use diff, int, subs,
and other Symbolic Math Toolbox functions to manipulate such expressions.
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Tutorial
Creating an M-File
M-files permit a more general use of functions. Suppose, for example, you want
to create the sinc function sin(x)/x. To do this, create an M-file in the @sym
directory.
function z = sinc(x)
%SINC The symbolic sinc function
%
sin(x)/x. This function
%
accepts a sym as the input argument.
if is equal(x,sym(0))
z = 1;
else
z = sin(x)/x;
end
You can extend such examples to functions of several variables. For a more
detailed discussion on object-oriented programming, see Chapter 14 of the
Using MATLAB guide.
1-16
Calculus
Calculus
The Symbolic Math Toolboxes provide functions to do the basic operations of
calculus; differentiation, limits, integration, summation, and Taylor series
expansion. The following sections outline these functions.
Differentiation
Let’s create a symbolic expression.
syms a x
f = sin(a*x)
Then
df = diff(f)
differentiates f with respect to its symbolic variable (in this case x), as
determined by findsym.
df =
cos(a*x)*a
To differentiate with respect to the variable a, type
dfa = diff(f,a)
which returns df/da
dfa=
cos(a*x)*x
Mathematical Function
f = xn
f' = nx
MATLAB Command
f = x^n
n–1
diff(f) or diff(f,x)
g = acos(at+b)
g = acos(a*t + b)
g' = acos(at+b)
diff(g) or diff(g,t)
h = Jv(z)
h = besselj(nu, z)
h' = Jv(z)(v/z) – Jv+1(z)
diff(h) or diff(h,z)
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Tutorial
To calculate the second derivatives with respect to x and a, respectively, type
diff(f,2) % or diff(f,x,2)
which returns
ans =
–sin(a*x)*a^2
and
diff(f,a,2)
which returns
ans =
–sin(a*x)*x^2
Define a, b, x, n, t, and theta in the MATLAB workspace, using the sym
command. The table below illustrates the diff command.
f
diff(f)
x^n
x^n*n/x
sin(a*t+b)
a*cos(a*t+b)
exp(i*theta)
i*exp(i*theta)
To differentiate the Bessel function of the first kind, besselj(nu,z), with
respect to z, type
syms nu z
b = besselj(nu,z);
db = diff(b)
which returns
db =
–besselj(nu+1,z)+nu/z*besselj(nu,z)
1-18
Calculus
The diff function can also take a symbolic matrix as its input. In this case, the
differentiation is done element-by-element. Consider the example
syms a x
A = [cos(a*x),sin(a*x);–sin(a*x),cos(a*x)]
which returns
A =
[ cos(a*x),
[ –sin(a*x),
sin(a*x)]
cos(a*x)]
The command
dy = diff(A)
returns
dy =
[ –sin(a*x)*a, cos(a*x)*a]
[ –cos(a*x)*a, –sin(a*x)*a]
You can also perform differentiation of a column vector with respect to a row
vector. Consider the transformation from Euclidean (x, y, z) to spherical (r, λ, ϕ)
coordinates as given by x = r cos λ cos ϕ, y = r cos λ sin ϕ, and z = r sin λ. Note
that λ corresponds to elevation or latitude while ϕ denotes azimuth or
longitude.
z
(x,y,z)
r
λ
y
ϕ
x
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Tutorial
To calculate the Jacobian matrix, J, of this transformation, use the jacobian
function. The mathematical notation for J is
∂( x, y, x )
J = ---------------------∂( r, λ, ϕ )
For the purposes of toolbox syntax, we use l for λ and f for ϕ. The commands
syms r l f
x = r*cos(l)*cos(f); y = r*cos(l)*sin(f); z = r*sin(l);
J = jacobian([x; y; z], [r l f])
return the Jacobian
J =
[
[
[
cos(l)*cos(f), –r*sin(l)*cos(f), –r*cos(l)*sin(f)]
cos(l)*sin(f), –r*sin(l)*sin(f), r*cos(l)*cos(f)]
sin(l),
r*cos(l),
0]
and the command
detJ = simple(det(J))
returns
detJ =
–cos(l)*r^2
Notice that the first argument of the jacobian function must be a column
vector and the second argument a row vector. Moreover, since the determinant
of the Jacobian is a rather complicated trigonometric expression, we used the
simple command to make trigonometric substitutions and reductions
(simplifications). The section “Simplifications and Substitutions” discusses
simplification in more detail.
1-20
Calculus
A table summarizing diff and jacobian follows.
Mathematical Operator
MATLAB Command
f(x) = exp(ax + b)
syms a b x
f = exp(a*x + b)
diff(x) or diff(f,x)
df
dx
diff(f,a)
df
da
diff(f,b,2)
2
d f
db
2
r = u2 + v2
t = arctan(v/u)
∂ ( r, t )
J = ----------------∂ ( u, v )
syms r t u v
r = u^2 + v^2
t = atan(v/u)
J = jacobian([r:t],[u,v])
Limits
The fundamental idea in calculus is to make calculations on functions as a
variable “gets close to” or approaches a certain value. Recall that the definition
of the derivative is given by a limit
f(x + h) – f(x)
f′(x) = lim ---------------------------------h
h→0
provided this limit exists. The Symbolic Math Toolbox allows you to compute
the limits of functions in a direct manner. The commands
syms h n x
dc = limit( (cos(x+h) – cos(x))/h,h,0 )
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Tutorial
which return
dc =
–sin(x)
and
limit( (1 + x/n)^n,n,inf )
which returns
ans =
exp(x)
illustrate two of the most important limits in mathematics: the derivative (in
this case of cos x) and the exponential function. While many limits
lim f ( x )
x→a
are “two sided” (that is, the result is the same whether the approach is from the
right or left of a), limits at the singularities of f(x) are not. Hence, the three
limits,
1
1
lim --- , lim --- , and
x
–
x
x→0
x→0
1
lim --+ x
x→0
yield the three distinct results: undefined, – ∞ , and + ∞ , respectively.
In the case of undefined limits, the Symbolic Math Toolbox returns NaN (not a
number). The command
limit(1/x,x,0) % Equivalently, limit(1/x)
returns
ans =
NaN
The command
limit(1/x,x,0,'left')
returns
ans =
–inf
1-22
Calculus
while the command
limit(1/x,x,0,'right')
returns
ans =
inf
Observe that the default case, limit(f) is the same as limit(f,x,0). Explore
the options for the limit command in this table. Here, we assume that f is a
function of the symbolic object x.
Mathematical Operation
MATLAB Command
lim f ( x )
limit(f)
lim f ( x )
limit(f,x,a) or
limit(f,a)
lim f ( x )
limit(f,x,a,'left')
lim f ( x )
limit(f,x,a,'right')
x→0
x→a
x→a
x→a
–
+
Integration
If f is a symbolic expression, then
int(f)
attempts to find another symbolic expression, F, so that diff(F) = f. That is,
int(f) returns the indefinite integral or antiderivative of f (provided one exists
in closed form). Similar to differentiation,
int(f,v)
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Tutorial
uses the symbolic object v as the variable of integration, rather than the
variable determined by findsym. See how int works by looking at this table.
Mathematical Operation
∫
n+1
x
n
x dx = ------------n+1
π⁄2
∫
sin ( 2x ) dx = 1
MATLAB Command
int(x^n) or
int(x^n,x)
int(sin(2*x),0,pi/2) or
int(sin(2*x),x,0,pi/2)
0
g = cos ( at + b )
∫ g ( t ) dt
= sin ( at + b ) ⁄ a
∫ J1 ( z ) dz
= – J0 ( z )
g = cos(a*t + b)
int(g) or
int(g,t)
int(besselj(1,z)) or
int(besselj(1,z),z)
In contrast to differentiation, symbolic integration is a more complicated task.
A number of difficulties can arise in computing the integral. The
antiderivative, F, may not exist in closed form; it may define an unfamiliar
function; it may exist, but the software can’t find the antiderivative; the
software could find it on a larger computer, but runs out of time or memory on
the available machine. Nevertheless, in many cases, MATLAB can perform
symbolic integration successfully. For example, create the symbolic variables
syms a b theta x yn x1 u
This table illustrates integration of expressions containing those variables.
1-24
f
int(f)
x^n
x^(n+1)/(n+1)
y^(–1)
log(y)
n^x
1/log(n)*n^x
Calculus
f
int(f)
sin(a*theta+b)
–cos(a*theta+b)/a
exp(–x1^2)
1/2*pi^(1/2)*erf(x1)
1/(1+u^2)
atan(u)
besselj(nu,z)
int(besselj(nu,z),z)
The last example shows what happens if the toolbox can’t find the
antiderivative; it simply returns the command, including the variable of
integration, unevaluated.
Definite integration is also possible. The commands
int(f,a,b)
and
int(f,v,a,b)
are used to find a symbolic expression for
b
∫a f ( x ) dx
b
and
∫ a f ( v ) dv
respectively.
Here are some additional examples.
f
a, b
int(f,a,b)
x^7
0, 1
1/8
1/x
1, 2
log(2)
log(x)*sqrt(x)
0, 1
–4/9
exp(–x^2)
0, inf
1/2*pi^(1/2)
bessel(1,z)
0, 1
–besselj(0,1)+1
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Tutorial
For the Bessel function (besselj) example, it is possible to compute a
numerical approximation to the value of the integral, using the double
function. The command
a = int(besselj(1,z),0,1)
returns
a =
–besselj(0,1)+1
and the command
a = double(a)
returns
a =
0.23480231344203
Integration with Real Constants
One of the subtleties involved in symbolic integration is the “value” of various
parameters. For example, it would seem evident that the expression
e
–( kx )
2
is the positive, bell shaped curve that tends to 0 as x tends to ±∞ for any real
number k. An example of this curve is depicted below with
1
k = ------2
and generated, using these commands.
syms x
k = sym(1/sqrt(2));
f = exp(–(k*x)^2);
ezplot(f)
1-26
Calculus
exp(−1/2*x^2)
1
0.8
0.6
0.4
0.2
0
−3
−2
−1
0
x
1
2
3
The Maple kernel, however, does not, a priori, treat the expressions k2 or x2 as
positive numbers. To the contrary, Maple assumes that the symbolic variables
x and k as a priori indeterminate. That is, they are purely formal variables with
no mathematical properties. Consequently, the initial attempt to compute the
integral
∞
∫e
– ( kx )
2
dx
–∞
in the Symbolic Math Toolbox, using the commands
syms x k;
f = exp(–(k*x)^2);
int(f,x,–inf,inf) % Equivalently, inf(f,–inf,inf)
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Tutorial
result in the output
Definite integration: Can't determine if the integral is
convergent.
Need to know the sign of --> k^2
Will now try indefinite integration and then take limits.
Warning: Explicit integral could not be found.
ans =
int(exp(–k^2*x^2),x= –inf..inf)
In the next section, you well see how to make k a real variable and therefore k2
positive.
Real Variables via sym
Notice that Maple is not able to determine the sign of the expression k^2. How
does one surmount this obstacle? The answer is to make k a real variable, using
the sym command. One particularly useful feature of sym, namely the real
option, allows you to declare k to be a real variable. Consequently, the integral
above is computed, in the toolbox, using the sequence
syms k real % Be sure that x has been declared a sym.
int(f,x,–inf,inf)
which returns
ans =
signum(k)/k*pi^(1/2)
Notice that k is now a symbolic object in the MATLAB workspace and a real
variable in the Maple kernel workspace. By typing
clear k
you only clear k in the MATLAB workspace. To ensure that k has no formal
properties (that is, to ensure k is a purely formal variable), type
syms k unreal
This variation of the syms command clears k in the Maple workspace. You can
also declare a sequence of symbolic variables w, y, x, z to be real, using
syms w x y z real
1-28
Calculus
In this case, all of the variables in between the words syms and real are
assigned the property real. That is, they are real variables in the Maple
workspace.
Mathematical Operation
MATLAB Commands
f(x) = e–kx
syms k x
f = exp(–k*x)
∫ f ( x ) dx
int(f) or int(f,x)
∫ f ( k ) dk
int(f,k)
1
∫0
g( x ) = e
∞
int(f,x,0,1) or int(f,0,1)
f ( x ) dx
∫–∞
– ( kx )
g ( x ) dx
2
syms k real
g = exp(–(k*x)^2)
int(g,x,–inf,inf) or
int(g,–inf,inf)
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Tutorial
Symbolic Summation
You can compute symbolic summations, when they exist, by using the symsum
command. For example, the p-series
1
1
1 + -----2- + -----2- + …
2
3
adds to π2/6, while the geometric series 1 + x +x2 + ... adds to 1/(1-x), provided
|x| < 1. Three summations are demonstrated below.
syms x k
s1 = symsum(1/k^2,1,inf)
s2 = symsum(x^k,k,0,inf)
s1 =
1/6*pi^2
s2 =
-1/(x-1)
1-30
Calculus
Taylor Series
The statement
T = taylor(f,8)
returns
T =
1/9+2/81*x^2+5/1458*x^4+49/131220*x^6
which is all the terms up to, but not including, order eight (O(x8)) in the Taylor
series for f(x).
∞
∑
(x – a)
n
(n )
f (a)
----------------n!
n=0
Technically, T is a MacLaurin series, since its basepoint is a = 0.
These commands
syms x
g = exp(x*sin(x))
t = taylor(g,12,2)
generate the first 12 nonzero terms of the Taylor series for g about x = 2.
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Tutorial
Let’s plot these functions together to see how well this Taylor approximation
compares to the actual function g.
xd = 1:0.05:3; yd = subs(g,x,xd);
ezplot(t, [1,3]); hold on;
plot(xd, yd, 'r-.')
title('Taylor approximation vs. actual function');
legend('Function','Taylor')
Taylor approximation vs. actual function
Function
Taylor
6
5
4
3
2
1
1
1.2
1.4
1.6
1.8
2
x
2.2
2.4
2.6
2.8
3
Special thanks to Professor Gunnar Bäckstrøm of UMEA in Sweden for this
example.
Then the command
pretty(T)
prints T in a format resembling typeset mathematics.
2
4
49
6
1/9 + 2/81 x + 5/1458 x + ------ x
131220
1-32
Calculus
Extended Calculus Example
The function
1
f ( x ) = -----------------------------5 + 4 cos ( x )
provides a starting point for illustrating several calculus operations in the
toolbox. It is also an interesting function in its own right. The statements
syms x
f = 1/(5+4*cos(x))
store the symbolic expression defining the function in f.
Plotting Symbolic Functions
The Symbolic Math Toolbox offers a set of easy-to-use commands for plotting
symbolic expressions, including planar curves (ezplot), contours (ezcontour
and ezcontourf), surfaces (ezsurf, ezsurfc, ezmesh, and ezmeshc), polar
coordinates (ezpolar), and parametrically defined curves (ezplot and
ezplot3) and surfaces (ezsurf). See Chapter 2, “Reference” for a detailed
description of these functions. The rest of this section illustrates the use of
ezplot to graph functions of the form y=f(x).
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The function ezplot(f) produces the plot of f(x) as shown below.
1/(5+4*cos(x))
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
−6
−4
−2
0
x
2
4
6
The ezplot function tries to make reasonable choices for the range of the x-axis
and for the resulting scale of the y-axis. Its choices can be overridden by an
additional input argument, or by subsequent axis commands. The default
domain for a function displayed by ezplot is –2π ≤ x ≤ 2π. To alter the domain,
type
ezplot(f,[a b])
This produces a graph of f(x) for a ≤ x ≤ b.
Let’s now look at the second derivative of the function f.
f2 = diff(f,2)
f2 =
32/(5+4*cos(x))^3*sin(x)^2+4/(5+4*cos(x))^2*cos(x)
1-34
Calculus
Equivalently, we can type f2 = diff(f,x,2). The default scaling in ezplot
cuts off part of f2’s graph. Set the axes limits manually to see the entire
function.
ezplot(f2)
axis([–2*pi 2*pi –5 2])
32/(5+4*cos(x))^3*sin(x)^2+4/(5+4*cos(x))^2*cos(x)
2
1
0
−1
−2
−3
−4
−5
−6
−4
−2
0
x
2
4
6
From the graph, it appears that the values of f''(x) lie between -4 and 1. As it
turns out, this is not true. We can calculate the exact range for f (i.e., compute
its actual maximum and minimum).
The actual maxima and minima of f''(x) occur at the zeros of f'''(x). The
statements
f3 = diff(f2);
pretty(f3)
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compute f'''(x) and display it in a more readable format.
3
sin(x)
sin(x) cos(x)
sin(x)
384 --------------- + 96 --------------- – 4 --------------4
3
2
(5 + 4 cos(x))
(5 + 4 cos(x))
(5 + 4 cos(x))
We can simplify and this expression using the statements
f3 = simple(f3);
pretty(f3)
2
2
sin(x) (96 sin(x) + 80 cos(x) + 80 cos(x) – 25)
4 ------------------------------------------------4
(5 + 4 cos(x))
Now use the solve function to find the zeros of f'''(x).
z = solve(f3)
returns a 5-by-1 symbolic matrix
z =
[
0]
[
atan((–255–60*19^(1/2))^(1/2),10+3*19^(1/2))]
[
atan(–(–255–60*19^(1/2))^(1/2),10+3*19^(1/2))]
[ atan((–255+60*19^(1/2))^(1/2)/(10–3*19^(1/2)))+pi]
[ –atan((–255+60*19^(1/2))^(1/2)/(10–3*19^(1/2)))–pi]
each of whose entries is a zero of f'''(x). The command
format; % Default format of 5 digits
zr = double(z)
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Calculus
converts the zeros to double form.
zr =
0
0+ 2.4381i
0– 2.4381i
2.4483
–2.4483
So far, we have found three real zeros and two complex zeros. However, a graph
of f3 shows that we have not yet found all its zeros.
ezplot(f3)
hold on;
plot(zr,0*zr,'ro')
plot([–2*pi,2*pi], [0,0],'g-.');
title('Zeros of f3')
Zeros of f3
3
2
1
0
−1
−2
−3
−6
−4
−2
0
x
2
4
6
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This occurs because f'''(x) contains a factor of sin(x), which is zero at integer
multiples of π. The function, solve(sin(x)), however, only reports the zero at
x = 0.
We can obtain a complete list of the real zeros by translating zr
zr = [0 zr(4) pi 2*pi–zr(4)]
by multiples of 2π
zr = [zr–2*pi zr zr+2*pi];
Now let’s plot the transformed zr on our graph for a complete picture of the
zeros of f3.
plot(zr,0*zr,'kX')
Zeros of f3
3
2
1
0
−1
−2
−3
−6
−4
−2
0
x
2
4
6
The first zero of f'''(x) found by solve is at x = 0. We substitute 0 for the
symbolic variable in f2
f20 = subs(f2,x,0)
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Calculus
to compute the corresponding value of f''(0).
f20 =
0.0494
A look at the graph of f''(x) shows that this is only a local minimum, which we
demonstrate by replotting f2.
clf
ezplot(f2)
axis([–2*pi 2*pi –4.25 1.25])
ylabel('f2');
title('Plot of f2 = f''''(x)')
hold on
plot(0,double(f20),'ro')
text(–1,–0.25,'Local minimum')
The resulting plot
Plot of f2 = f’’(x)
1
0.5
0
Local minimum
−0.5
f2
−1
−1.5
−2
−2.5
−3
−3.5
−4
−6
−4
−2
0
x
2
4
6
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indicates that the global minima occur near x = -π and x = π. We can
demonstrate that they occur exactly at x= ±π, using the following sequence of
commands. First we try substituting -π and π into f'''(x).
simple([subs(f3,x,–pi),subs(f3,x,pi)])
The result
ans =
[ 0, 0]
shows that -π and π happen to be critical points of f'''(x). We can see that -π and
π are global minima by plotting f2(–pi) and f2(pi) against f2(x).
m1 = double(subs(f2,x,-pi)); m2 = double(subs(f2,x,pi));
plot(-pi,m1,'go',pi,m2,'go')
text(-1,-4,'Global minima')
The actual minima are m1, m2
ans =
[ –4, –4]
as shown in the plot on the following page.
1-40
Calculus
Plot of f2 = f’’(x)
1
0.5
0
Local minimum
−0.5
f2
−1
−1.5
−2
−2.5
−3
−3.5
−4
Global minima
−6
−4
−2
0
x
2
4
6
The foregoing analysis confirms part of our original guess that the range of
f''(x) is [-4, 1]. We can confirm the other part by examining the fourth zero of
f'''(x) found by solve. First extract the fourth zero from z and assign it to a
separate variable
s = z(4)
to obtain
s =
atan((–255+60*19^(1/2))^(1/2)/(10–3*19^(1/2)))+pi
Executing
sd = double(s)
displays the zero’s corresponding numeric value.
sd =
2.4483
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Plotting the point (s, f2(s)) against f2, using
M1 = double(subs(f2,x,s));
plot(sd,M1,'ko')
text(-1,1,'Global maximum')
visually confirms that s is a maximum.
Plot of f2 = f’’(x)
1
Global maximum
0.5
0
Local minimum
−0.5
f2
−1
−1.5
−2
−2.5
−3
−3.5
−4
Global minima
−6
−4
−2
0
x
2
4
6
The maximum is M1 = 1.0051.
Therefore, our guess that the maximum of f''(x) is [–4, 1] was close, but
incorrect. The actual range is [–4, 1.0051].
Now, let’s see if integrating f''(x) twice with respect to x recovers our original
function f(x) = 1/(5 + 4 cos x). The command
g = int(int(f2))
1-42
Calculus
returns
g =
–8/(tan(1/2*x)^2+9)
This is certainly not the original expression for f(x). Let’s look at the difference
f(x) – g(x).
d = f – g
pretty(d)
1
8
[ –––––––––––– + ––––––––––––––– ]
5 + 4 cos(x)
2
tan(1/2 x) + 9
We can simplify this using simple(d) or simplify(d). Either command
produces
ans =
1
This illustrates the concept that differentiating f(x) twice, then integrating the
result twice, produces a function that may differ from f(x) by a linear function
of x.
Finally, integrate f(x) once more.
F = int(f)
The result
F =
2/3*atan(1/3*tan(1/2*x))
involves the arctangent function.
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Though F(x) is the antiderivative of a continuous function, it is itself
discontinuous as the following plot shows.
ezplot(F)
2/3*atan(1/3*tan(1/2*x))
1
0.8
0.6
0.4
0.2
0
0.2
0.4
0.6
0.8
−1
−6
−4
−2
0
x
2
4
6
Note that F(x) has jumps at x = ± π. This occurs because tan x is singular at
x = ± π.
1-44
Calculus
In fact, as
ezplot(atan(tan(x)))
shows, the numerical value of atan(tan(x))differs from x by a piecewise
constant function that has jumps at odd multiples of π/2.
atan(tan(x))
1.5
1
0.5
0
−0.5
−1
−1.5
−6
−4
−2
0
x
2
4
6
To obtain a representation of F(x) that does not have jumps at these points, we
must introduce a second function, J(x), that compensates for the
discontinuities. Then we add the appropriate multiple of J(x) to F(x).
J = sym('round(x/(2*pi))');
c = sym('2/3*pi');
F1 = F+c*J
F1 =
2/3*atan(1/3*tan(1/2*x))+2/3*pi*round(1/2*x/pi)
1-45
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and plot the result.
ezplot(F1,[–6.28,6.28])
This representation does have a continuous graph.
2/3*atan(1/3*tan(1/2*x))+2/3*pi*round(1/2*x/pi)
2.5
2
1.5
1
0.5
0
−0.5
−1
−1.5
−2
−2.5
−6
−4
−2
0
x
2
4
6
Notice that we use the domain [–6.28, 6.28] in ezplot rather than the default
domain [–2π, 2π]. The reason for this is to prevent an evaluation of
F1 = 2/3 atan(1/3 tan 1/2 x) at the singular points x = –π and x = π where the
jumps in F and J do not cancel out one another. The proper handling of branch
cut discontinuities in multivalued functions like arctan x is a deep and difficult
problem in symbolic computation. Although MATLAB and Maple cannot do
this entirely automatically, they do provide the tools for investigating such
questions.
1-46
Simplifications and Substitutions
Simplifications and Substitutions
There are several functions that simplify symbolic expressions and are used to
perform symbolic substitutions.
Simplifications
Here are three different symbolic expressions.
syms x
f = x^3–6*x^2+11*x–6
g = (x–1)*(x–2)*(x–3)
h = x*(x*(x–6)+11)–6
Here are their prettyprinted forms, generated by
pretty(f), pretty(g), pretty(h)
3
2
x – 6 x + 11 x – 6
(x – 1) (x – 2) (x – 3)
x (x (x – 6) + 11) – 6
These expressions are three different representations of the same
mathematical function, a cubic polynomial in x.
Each of the three forms is preferable to the others in different situations. The
first form, f, is the most commonly used representation of a polynomial. It is
simply a linear combination of the powers of x. The second form, g, is the
factored form. It displays the roots of the polynomial and is the most accurate
for numerical evaluation near the roots. But, if a polynomial does not have such
simple roots, its factored form may not be so convenient. The third form, h, is
the Horner, or nested, representation. For numerical evaluation, it involves the
fewest arithmetic operations and is the most accurate for some other ranges of
x.
The symbolic simplification problem involves the verification that these three
expressions represent the same function. It also involves a less clearly defined
objective—which of these representations is “the simplest”?
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This toolbox provides several functions that apply various algebraic and
trigonometric identities to transform one representation of a function into
another, possibly simpler, representation. These functions are collect,
expand, horner, factor, simplify, and simple.
collect
The statement
collect(f)
views f as a polynomial in its symbolic variable, say x, and collects all the
coefficients with the same power of x. A second argument can specify the
variable in which to collect terms if there is more than one candidate. Here are
a few examples:
1-48
f
collect(f)
(x–1)*(x–2)*(x–3)
x^3–6*x^2+11*x–6
x*(x*(x–6)+11)–6
x^3–6*x^2+11*x–6
(1+x)*t + x*t
2*x*t+t
Simplifications and Substitutions
expand
The statement
expand(f)
distributes products over sums and applies other identities involving functions
of sums. For example,
f
expand(f)
a∗(x + y)
a∗x + a∗y
(x–1)∗(x–2)∗(x–3)
x^3–6∗x^2+11∗x–6
x∗(x∗(x–6)+11)–6
x^3–6∗x^2+11∗x–6
exp(a+b)
exp(a)∗exp(b)
cos(x+y)
cos(x)*cos(y)–sin(x)*sin(y)
cos(3∗acos(x))
4∗x^3 – 3∗x
horner
The statement
horner(f)
transforms a symbolic polynomial f into its Horner, or nested, representation.
For example,
f
horner(f)
x^3–6∗x^2+11∗x–6
–6+(11+(–6+x)*x)*x
1.1+2.2∗x+3.3∗x^2
11/10+(11/5+33/10*x)*x
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factor
If f is a polynomial with rational coefficients, the statement
factor(f)
expresses f as a product of polynomials of lower degree with rational
coefficients. If f cannot be factored over the rational numbers, the result is f
itself. For example,
f
factor(f)
x^3–6∗x^2+11∗x–6
(x–1)∗(x–2)∗(x–3)
x^3–6∗x^2+11∗x–5
x^3–6∗x^2+11∗x–5
x^6+1
(x^2+1)∗(x^4–x^2+1)
Here is another example involving factor. It factors polynomials of the form
x^n + 1. This code
syms x;
n = 1:9;
x = x(ones(size(n)));
p = x.^n + 1;
f = factor(p);
[p; f].'
returns a matrix with the polynomials in its first column and their factored
forms in its second:
[
[
[
[
[
[
[
[
[
1-50
x+1,
x+1 ]
x^2+1,
x^2+1 ]
x^3+1,
(x+1)*(x^2–x+1) ]
x^4+1,
x^4+1 ]
x^5+1,
(x+1)*(x^4–x^3+x^2–x+1) ]
x^6+1,
(x^2+1)*(x^4–x^2+1) ]
x^7+1, (x+1)*(1–x+x^2–x^3+x^4–x^5+x^6) ]
x^8+1,
x^8+1 ]
x^9+1,
(x+1)*(x^2–x+1)*(x^6–x^3+1) ]
Simplifications and Substitutions
As an aside at this point, we mention that factor can also factor symbolic
objects containing integers. This is an alternative to using the factor function
in MATLAB’s specfun directory. For example, the following code segment
one = '1'
for n = 1:11
N(n,:) = sym(one(1,ones(1,n)));
end
[N factor(N)]
displays the factors of symbolic integers consisting of 1s.
[
[
[
[
[
[
[
[
[
[
[
1,
11,
111,
1111,
11111,
111111,
1111111,
11111111,
111111111,
1111111111,
11111111111,
1
(11)
(3)*(37)
(11)*(101)
(41)*(271)
(3)*(7)*(11)*(13)*(37)
(239)*(4649)
(11)*(73)*(101)*(137)
(3)^2*(37)*(333667)
(11)*(41)*(271)*(9091)
(513239)*(21649)
]
]
]
]
]
]
]
]
]
]
]
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Tutorial
simplify
The simplify function is a powerful, general purpose tool that applies a
number of algebraic identities involving sums, integral powers, square roots
and other fractional powers, as well as a number of functional identities
involving trig functions, exponential and log functions, Bessel functions,
hypergeometric functions, and the gamma function. Here are some examples.
f
simplify(f)
x∗(x∗(x–6)+11)–6
x^3–6∗x^2+11∗x–6
(1–x^2)/(1–x)
x + 1
(1/a^3+6/a^2+12/a+8)^(1/3)
((2*a+1)^3/a^3)^(1/3)
syms x y positive
log(x∗y)
log(x) + log(y)
exp(x) ∗ exp(y)
exp(x+y)
besselj(2,x) – ...
2*besselj(1,x)/x +
besselj(0,x)
0
gamma(x+1)–x*gamma(x)
0
cos(x)^2 + sin(x)^2
1
simple
The simple function has the unorthodox mathematical goal of finding a
simplification of an expression that has the fewest number of characters. Of
course, there is little mathematical justification for claiming that one
expression is “simpler” than another just because its ASCII representation is
shorter, but this often proves satisfactory in practice.
The simple function achieves its goal by independently applying simplify,
collect, factor, and other simplification functions to an expression and
keeping track of the lengths of the results. The simple function then returns
the shortest result.
1-52
Simplifications and Substitutions
The simple function has several forms, each returning different output. The
form
simple(f)
displays each trial simplification and the simplification function that produced
it in the MATLAB command window. The simple function then returns the
shortest result. For example, the command
simple(cos(x)^2 + sin(x)^2)
displays the following alternative simplifications in the MATLAB command
window
simplify:
1
radsimp:
cos(x)^2+sin(x)^2
combine(trig):
1
factor:
cos(x)^2+sin(x)^2
expand:
cos(x)^2+sin(x)^2
convert(exp):
(1/2*exp(i*x)+1/2/exp(i*x))^2–1/4*(exp(i*x)–1/exp(i*x))^2
convert(sincos):
cos(x)^2+sin(x)^2
convert(tan):
(1–tan(1/2*x)^2)^2/(1+tan(1/2*x)^2)^2+4*tan(1/2*x)^2/
(1+tan(1/2*x)^2)^2
collect(x):
cos(x)^2+sin(x)^2
1-53
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Tutorial
and returns
ans =
1
This form is useful when you want to check, for example, whether the shortest
form is indeed the simplest. If you are not interested in how simple achieves
its result, use the form
f = simple(f)
This form simply returns the shortest expression found. For example, the
statement
f = simple(cos(x)^2+sin(x)^2)
returns
f =
1
If you want to know which simplification returned the shortest result, use the
multiple output form
[F, how] = simple(f)
This form returns the shortest result in the first variable and the simplification
method used to achieve the result in the second variable. For example, the
statement
[f, how] = simple(cos(x)^2+sin(x)^2)
returns
f =
1
how =
combine(trig)
The simple function sometimes improves on the result returned by simplify,
one of the simplifications that it tries. For example, when applied to the
1-54
Simplifications and Substitutions
examples given for simplify, simple returns a simpler (or at least shorter)
result in two cases:
f
simplify(f)
simple(f)
(1/a^3+6/a^2+12/a+8)^(1/3)
((2*a+1)^3/a^3)^(1/3)
(2*a+1)/a
syms x y positive
log(x∗y)
log(x)+log(y)
log(x*y)
In some cases, it is advantageous to apply simple twice to obtain the effect of
two different simplification functions. For example, the statements
f = (1/a^3+6/a^2+12/a+8)^(1/3);
simple(simple(f))
return
1/a+2
The first application, simple(f), uses radsimp to produce (2*a+1)/a; the
second application uses combine(trig) to transform this to 1/a+2.
The simple function is particularly effective on expressions involving
trigonometric functions. Here are some examples:
f
simple(f)
cos(x)^2+sin(x)^2
1
2∗cos(x)^2–sin(x)^2
3∗cos(x)^2–1
cos(x)^2–sin(x)^2
cos(2∗x)
cos(x)+(–sin(x)^2)^(1/2)
cos(x)+i∗sin(x)
cos(x)+i∗sin(x)
exp(i∗x)
cos(3∗acos(x))
4∗x^3–3∗x
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Substitutions
There are two functions for symbolic substitution: subexpr and subs.
subexpr
These commands
syms a x
s = solve(x^3+a*x+1)
solve the equation x^3+a*x+1 = 0 for x.
s =
[
[
[
1-56
(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)–
1/3*a/(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)
–1/2*(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)+
1/6*a/(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)+
1/2*i*3^(1/2)*((–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)+
1/3*a/(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3))
–1/2*(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)+
1/6*a/(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)–
1/2*i*3^(1/2)*((–1/2+1/18*(12*a^3+81)^(1/2))^(1/3)+
1/3*a/(–1/2+1/18*(12*a^3+81)^(1/2))^(1/3))
]
]
]
Simplifications and Substitutions
Use the pretty function to display s in a more readable form.
pretty(s)
s =
[
1/3
[
%1
– 1/3
[
[
[
[
1/3
a
[ – 1/2 %1
+ 1/6 ----- + 1/2 i
[
1/3
[
%1
[
[
1/3
a
[ – 1/2 %1
+ 1/6 ----- – 1/2 i
[
1/3
[
%1
%1 :=
a
----1/3
%1
1/2 / 1/3
a \
3
|%1
+ 1/3 -----|
|
1/3|
\
%1
/
1/2 / 1/3
a \
3
|%1
+ 1/3 -----|
|
1/3|
\
%1
/
]
]
]
]
]
]
]
]
]
]
]
]
]
]
3
1/2
– 1/2 + 1/18 (12 a + 81)
The pretty command inherits the %n (n, an integer) notation from Maple to
denote subexpressions that occur multiple times in the symbolic object. The
subexpr function allows you to save these common subexpressions as well as
the symbolic object rewritten in terms of the subexpressions. The
subexpressions are saved in a column vector called sigma.
Continuing with the example
r = subexpr(s)
1-57
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Tutorial
returns
sigma =
–108+12*(12*a^3+81)^(1/2)
r =
[
[
[
1/6*sigma^(1/3)–2*a/sigma^(1/3)]
–1/12*sigma^(1/3)+a/sigma^(1/3)+
1/2*i*3^(1/2)*(1/6*sigma^(1/3)+2*a/sigma^(1/3))]
–1/12*sigma^(1/3)+a/sigma^(1/3)–
1/2*i*3^(1/2)*(1/6*sigma^(1/3)+2*a/sigma^(1/3))]
Notice that subexpr creates the variable sigma in the MATLAB workspace.
You can verify this by typing whos, or the command
sigma
which returns
sigma =
–108+12*(12*a^3+81)^(1/2)
1-58
Simplifications and Substitutions
subs
Let’s find the eigenvalues and eigenvectors of a circulant matrix A.
syms a b c
A = [a b c; b c a; c a b];
[v,E] = eig(A)
v =
[ –(a+(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2)–b)/(a–c),
–(a–(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2)–b)/(a–c),
[ –(b–c–(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2))/(a–c),
–(b–c+(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2))/(a–c),
[ 1,
1,
1]
1]
1]
E =
[ (b^2–b*a–c*b–
c*a+a^2+c^2)^(1/2),
[
0,
[
0,
0,
–(b^2–b*a–c*b–
c*a+a^2+c^2)^(1/2),
0,
0]
0]
b+c+a]
Suppose we want to replace the rather lengthy expression
(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2)
throughout v and E. We first use subexpr:
v = subexpr(v,'S')
which returns
S =
(b^2–b*a–c*b–c*a+a^2+c^2)^(1/2)
v =
[ –(a+S–b)/(a–c), –(a–S–b)/(a–c),
[ –(b–c–S)/(a–c), –(b–c+S)/(a–c),
[
1,
1,
1]
1]
1]
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Next, substitute the symbol S into E with
E = subs(E,S,'S')
E =
[
[
[
S,
0,
0,
0,
0]
–S,
0]
0, b+c+a]
Now suppose we want to evaluate v at a = 10. We can do this using the subs
command:
subs(v,a,10)
This replaces all occurrences of a in v with 10
[ –(10+S–b)/(10–c), –(10–S–b)/(10–c),
[ –(b–c–S)/(10–c), –(b–c+S)/(10–c),
[
1,
1,
1]
1]
1]
Notice, however, that the symbolic expression represented by S is unaffected by
this substitution. That is, the symbol a in S is not replaced by 10. The subs
command is also a useful function for substituting in a variety of values for
several variables in a particular expression. Let’s look at S. Suppose that in
addition to substituting a = 10, we also want to substitute the values for 2 and
10 for b and c, respectively. The way to do this is to set values for a, b, and c in
the workspace. Then subs evaluates its input using the existing symbolic and
double variables in the current workspace. In our example, we first set
a = 10; b = 2; c = 10;
subs(S)
ans =
64^(1/2)
1-60
Simplifications and Substitutions
To look at the contents of our workspace, type whos, which gives
Name
A
E
S
a
ans
b
c
v
Size
Bytes
3x3
3x3
1x1
1x1
1x1
1x1
1x1
3x3
Class
878
888
186
8
140
8
8
982
sym object
sym object
sym object
double array
sym object
double array
double array
sym object
a, b, and c are now variables of class double while A, E, S, and v remain symbolic
expressions (class sym).
If you want to preserve a, b, and c as symbolic variables, but still alter their
value within S, use this procedure.
syms a b c
subs(S,{a,b,c},{10,2,10})
ans =
64^(1/2)
Typing whos reveals that a, b, and c remain 1-by-1 sym objects.
The subs command can be combined with double to evaluate a symbolic
expression numerically. Suppose we have
syms t
M = (1–t^2)*exp(–1/2*t^2);
P = (1–t^2)*sech(t);
and want to see how M and P differ graphically.
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One approach is to type
ezplot(M); hold on; ezplot(P)
but this plot
(1−t^2)*sech(t)
1
0.8
0.6
0.4
0.2
0
−0.2
−0.4
−0.6
−0.8
−6
−4
−2
0
t
2
does not readily help us identify the curves.
1-62
4
6
Simplifications and Substitutions
Instead, combine subs, double, and plot
T = –6:0.05:6;
MT = double(subs(M,t,T));
PT = double(subs(P,t,T));
plot(T,MT,'b',T,PT,'r–.')
title(' ')
legend('M','P')
xlabel('t'); grid
to produce a multicolored graph that indicates the difference between M and P.
1
M
P
0.8
0.6
0.4
0.2
0
−0.2
−0.4
−0.6
−0.8
−1
−6
−4
−2
0
t
2
4
6
Finally the use of subs with strings greatly facilitates the solution of problems
involving the Fourier, Laplace, or z-transforms. See the section “Integral
Transforms” for complete details.
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Variable-Precision Arithmetic
Overview
There are three different kinds of arithmetic operations in this toolbox.
• Numeric
MATLAB’s floating-point arithmetic
• Rational
Maple’s exact symbolic arithmetic
• VPA
Maple’s variable-precision arithmetic
For example, the MATLAB statements
format long
1/2+1/3
use numeric computation to produce
0.83333333333333
With the Symbolic Math Toolbox, the statement
sym(1/2)+1/3
uses symbolic computation to yield
5/6
And, also with the toolbox, the statements
digits(25)
vpa(1/2+1/3)
use variable-precision arithmetic to return
.8333333333333333333333333
The floating-point operations used by numeric arithmetic are the fastest of the
three, and require the least computer memory, but the results are not exact.
The number of digits in the printed output of MATLAB’s double quantities is
controlled by the format statement, but the internal representation is always
the eight-byte floating-point representation provided by the particular
computer hardware.
In the computation of the numeric result above, there are actually three
roundoff errors, one in the division of 1 by 3, one in the addition of 1/2 to the
1-64
Variable-Precision Arithmetic
result of the division, and one in the binary to decimal conversion for the
printed output. On computers that use IEEE floating-point standard
arithmetic, the resulting internal value is the binary expansion of 5/6,
truncated to 53 bits. This is approximately 16 decimal digits. But, in this
particular case, the printed output shows only 15 digits.
The symbolic operations used by rational arithmetic are potentially the most
expensive of the three, in terms of both computer time and memory. The results
are exact, as long as enough time and memory are available to complete the
computations.
Variable-precision arithmetic falls in between the other two in terms of both
cost and accuracy. A global parameter, set by the function digits, controls the
number of significant decimal digits. Increasing the number of digits increases
the accuracy, but also increases both the time and memory requirements. The
default value of digits is 32, corresponding roughly to floating-point accuracy.
The Maple documentation uses the term “hardware floating-point” for what we
are calling “numeric” or “floating-point” and uses the term “floating-point
arithmetic” for what we are calling “variable-precision arithmetic.”
Example: Using the Different Kinds of Arithmetic
Rational Arithmetic
By default, the Symbolic Math Toolbox uses rational arithmetic operations, i.e.,
Maple’s exact symbolic arithmetic. Rational arithmetic is invoked when you
create symbolic variables using the sym function.
The sym function converts a double matrix to its symbolic form. For example, if
the double matrix is
A =
1.1000
2.1000
3.1000
1.2000
2.2000
3.2000
1.3000
2.3000
3.3000
its symbolic form, S = sym(A), is
S =
[11/10, 6/5, 13/10]
[21/10, 11/5, 23/10]
[31/10, 16/5, 33/10]
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For this matrix A, it is possible to discover that the elements are the ratios of
small integers, so the symbolic representation is formed from those integers.
On the other hand, the statement
E = [exp(1) sqrt(2); log(3) rand]
returns a matrix
E =
2.71828182845905
1.09861228866811
1.41421356237310
0.21895918632809
whose elements are not the ratios of small integers, so sym(E) reproduces the
floating-point representation in a symbolic form.
[3060513257434037*2^(–50), 3184525836262886*2^(–51)]
[2473854946935174*2^(–51), 3944418039826132*2^(–54)]
Variable-Precision Numbers
Variable-precision numbers are distinguished from the exact rational
representation by the presence of a decimal point. A power of 10 scale factor,
denoted by 'e', is allowed. To use variable-precision instead of rational
arithmetic, create your variables using the vpa function.
For matrices with purely double entries, the vpa function generates the
representation that is used with variable-precision arithmetic. Continuing on
with our example, and using digits(4), applying vpa to the matrix S
vpa(S)
generates the output
S =
[1.100, 1.200, 1.300]
[2.100, 2.200, 2.300]
[3.100, 3.200, 3.300]
and with digits(25)
F = vpa(E)
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Variable-Precision Arithmetic
generates
F =
[2.718281828459045534884808, 1.414213562373094923430017]
[1.098612288668110004152823, .2189591863280899719512718]
Converting to Floating-Point
To convert a rational or variable-precision number to its MATLAB
floating-point representation, use the double function.
In our example, both double(sym(E)) and double(vpa(E)) return E.
Another Example
The next example is perhaps more interesting. Start with the symbolic
expression
f = sym('exp(pi*sqrt(163))')
The statement
double(f)
produces the printed floating-point value
2.625374126407687e+17
Using the second argument of vpa to specify the number of digits,
vpa(f,18)
returns
262537412640768744.
whereas
vpa(f,25)
returns
262537412640768744.0000000
We suspect that f might actually have an integer value. This suspicion is
reinforced by the 30 digit value, vpa(f,30)
262537412640768743.999999999999
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Finally, the 40 digit value, vpa(f,40)
262537412640768743.9999999999992500725944
shows that f is very close to, but not exactly equal to, an integer.
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Linear Algebra
Linear Algebra
Basic Algebraic Operations
Basic algebraic operations on symbolic objects are the same as operations on
MATLAB objects of class double. This is illustrated in the following example.
The Givens transformation produces a plane rotation through the angle t. The
statements
syms t;
G = [cos(t) sin(t); –sin(t) cos(t)]
create this transformation matrix:
G =
[ cos(t),
[ –sin(t),
sin(t) ]
cos(t) ]
Applying the Givens transformation twice should simply be a rotation through
twice the angle. The corresponding matrix can be computed by multiplying G
by itself or by raising G to the second power. Both
A = G*G
and
A = G^2
produce
A =
[cos(t)^2–sin(t)^2,
2*cos(t)*sin(t)]
[ –2*cos(t)*sin(t), cos(t)^2–sin(t)^2]
The simple function
A = simple(A)
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uses a trigonometric identity to return the expected form by trying several
different identities and picking the one that produces the shortest
representation:
A =
[ cos(2*t), sin(2*t)]
[–sin(2*t), cos(2*t)]
A Givens rotation is an orthogonal matrix, so its transpose is its inverse.
Confirming this by
I = G.' *G
which produces
I =
[cos(t)^2+sin(t)^2,
0]
[
0, cos(t)^2+sin(t)^2]
and then
I = simple(I)
I =
[1, 0]
[0, 1]
Linear Algebraic Operations
Let’s do several basic linear algebraic operations.
The command
H = hilb(3)
generates the 3-by-3 Hilbert matrix. With format short, MATLAB prints
H =
1.0000
0.5000
0.3333
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0.5000
0.3333
0.2500
0.3333
0.2500
0.2000
Linear Algebra
The computed elements of H are floating-point numbers that are the ratios of
small integers. Indeed, H is a MATLAB array of class double. Converting H to
a symbolic matrix
H = sym(H)
gives
[ 1, 1/2, 1/3]
[1/2, 1/3, 1/4]
[1/3, 1/4, 1/5]
This allows subsequent symbolic operations on H to produce results that
correspond to the infinitely precise Hilbert matrix, sym(hilb(3)), not its
floating-point approximation, hilb(3). Therefore,
inv(H)
produces
[ 9, –36,
30]
[–36, 192, –180]
[ 30, –180, 180]
and
det(H)
yields
1/2160
We can use the backslash operator to solve a system of simultaneous linear
equations. The commands
b = [1 1 1]'
x = H\b
% Solve Hx = b
produce the solution
[ –3]
[–24]
[ 30]
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All three of these results, the inverse, the determinant, and the solution to the
linear system, are the exact results corresponding to the infinitely precise,
rational, Hilbert matrix. On the other hand, using digits(16), the command
V = vpa(hilb(3))
returns
[
1., .5000000000000000, .3333333333333333]
[.5000000000000000, .3333333333333333, .2500000000000000]
[.3333333333333333, .2500000000000000, .2000000000000000]
The decimal points in the representation of the individual elements are the
signal to use variable-precision arithmetic. The result of each arithmetic
operation is rounded to 16 significant decimal digits. When inverting the
matrix, these errors are magnified by the matrix condition number, which for
hilb(3) is about 500. Consequently,
inv(V)
which returns
[ 9.000000000000082, –36.00000000000039, 30.00000000000035]
[–36.00000000000039, 192.0000000000021, –180.0000000000019]
[ 30.00000000000035, –180.0000000000019, 180.0000000000019]
shows the loss of two digits. So does
det(V)
which gives
.462962962962958e–3
and
V\b
which is
[ 3.000000000000041]
[–24.00000000000021]
[ 30.00000000000019]
Since H is nonsingular, the null space of H
null(H)
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and the column space of H
colspace(H)
produce an empty matrix and a permutation of the identity matrix,
respectively. To make a more interesting example, let’s try to find a value s for
H(1,1) that makes H singular. The commands
syms s
H(1,1) = s
Z = det(H)
sol = solve(Z)
produce
H =
[ s, 1/2, 1/3]
[1/2, 1/3, 1/4]
[1/3, 1/4, 1/5]
Z =
1/240*s–1/270
sol =
8/9
Then
H = subs(H,s,sol)
substitutes the computed value of sol for s in H to give
H =
[8/9, 1/2, 1/3]
[1/2, 1/3, 1/4]
[1/3, 1/4, 1/5]
Now, the command
det(H)
returns
ans =
0
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and
inv(H)
produces an error message
??? error using ==> inv
Error, (in inverse) singular matrix
because H is singular. For this matrix, Z = null(H) and C = colspace(H) are
nontrivial.
Z =
[
1]
[ –4]
[10/3]
C =
[ 0,
1]
[ 1,
0]
[6/5, –3/10]
It should be pointed out that even though H is singular, vpa(H) is not. For any
integer value d, setting
digits(d)
and then computing
det(vpa(H))
inv(vpa(H))
results in a determinant of size 10^(–d) and an inverse with elements on the
order of 10^d.
Eigenvalues
The symbolic eigenvalues of a square matrix A or the symbolic eigenvalues and
eigenvectors of A are computed, respectively, using the commands
E = eig(A)
[V,E] = eig(A)
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The variable-precision counterparts are
E = eig(vpa(A))
[V,E] = eig(vpa(A))
The eigenvalues of A are the zeros of the characteristic polynomial of A,
det(A–x*I), which is computed by
poly(A)
The matrix H from the last section provides our first example.
H =
[8/9, 1/2, 1/3]
[1/2, 1/3, 1/4]
[1/3, 1/4, 1/5]
The matrix is singular, so one of its eigenvalues must be zero. The statement
[T,E] = eig(H)
produces the matrices T and E. The columns of T are the eigenvectors of H.
T =
[
1, 28/153+2/153*12589^(1/2), 28/153–2/153*12589^(12)]
[
–4,
1,
1]
[ 10/3, 92/255–1/255*12589^(1/2), 292/255+1/255*12589^(12)]
Similarly, the diagonal elements of E are the eigenvalues of H.
E =
[0,
0,
0]
[0, 32/45+1/180*12589^(1/2),
0]
[0,
0, 32/45–1/180*12589^(1/2)]
It may be easier to understand the structure of the matrices of eigenvectors, T,
and eigenvalues, E, if we convert T and E to decimal notation. We proceed as
follows. The commands
Td = double(T)
Ed = double(E)
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return
Td =
1.0000
1.6497
-1.2837
-4.0000
1.0000
1.0000
3.3333
0.7051
1.5851
Ed =
0
0
0
0
1.3344
0
0
0
0.0878
The first eigenvalue is zero. The corresponding eigenvector (the first column of
Ts) is the same as the basis for the null space found in the last section. The
other two eigenvalues are the result of applying the quadratic formula to
x^2–64/45*x+253/2160
which is the quadratic factor in factor(poly(H)).
syms x
g = simple(factor(poly(H))/x);
solve(g)
Closed form symbolic expressions for the eigenvalues are possible only when
the characteristic polynomial can be expressed as a product of rational
polynomials of degree four or less. The Rosser matrix is a classic numerical
analysis test matrix that happens to illustrate this requirement. The
statement
R = sym(gallery('rosser'))
generates
R =
[ 611
[ 196
[–192
[ 407
[ –8
[ –52
[ –49
[ 29
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196
899
113
–192
–71
–43
–8
–44
–192
113
899
196
61
49
8
52
407
–192
196
611
8
44
59
–23
–8
–71
61
8
411
–599
208
208
–52
–43
49
44
–599
411
208
208
–49
–8
8
59
208
208
99
–911
29]
–44]
52]
–23]
208]
208]
–911]
99]
Linear Algebra
The commands
p = poly(R);
pretty(factor(p))
produce
2
2
2
[x (x – 1020) (x – 1020 x + 100)(x – 1040500) (x – 1000) ]
The characteristic polynomial (of degree 8) factors nicely into the product of two
linear terms and three quadratic terms. We can see immediately that four of
the eigenvalues are 0, 1020, and a double root at 1000. The other four roots are
obtained from the remaining quadratics. Use
eig(R)
to find all these values
[
0]
[
1020]
[510+100*26^(1/2)]
[510–100*26^(1/2)]
[ 10*10405^(1/2)]
[ –10*10405^(1/2)]
[
1000]
[
1000]
The Rosser matrix is not a typical example; it is rare for a full 8-by-8 matrix to
have a characteristic polynomial that factors into such simple form. If we
change the two “corner” elements of R from 29 to 30 with the commands
S = R;
S(1,8) = 30;
S(8,1) = 30;
and then try
p = poly(S)
we find
p =
40250968213600000+51264008540948000*x–
1082699388411166000*x^2+4287832912719760*x^–3–
5327831918568*x^4+82706090*x^5+5079941*x^6–
4040*x^7+x^8
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We also find that factor(p) is p itself. That is, the characteristic polynomial
cannot be factored over the rationals.
For this modified Rosser matrix
F = eig(S)
returns
F =
[ –1020.0532142558915165931894252600]
[ –.17053529728768998575200874607757]
[ .21803980548301606860857564424981]
[ 999.94691786044276755320289228602]
[ 1000.1206982933841335712817075454]
[ 1019.5243552632016358324933278291]
[ 1019.9935501291629257348091808173]
[ 1020.4201882015047278185457498840]
Notice that these values are close to the eigenvalues of the original Rosser
matrix. Further, the numerical values of F are a result of Maple’s floating-point
arithmetic. Consequently, different settings of digits do not alter the number
of integers to the right of the decimal place.
It is also possible to try to compute eigenvalues of symbolic matrices, but closed
form solutions are rare. The Givens transformation is generated as the matrix
exponential of the elementary matrix
A =
0 1
–1 0
The Symbolic Math Toolbox commands
syms t
A = sym([0 1; –1 0]);
G = expm(t*A)
return
[ cos(t),
[ –sin(t),
1-78
sin(t)]
cos(t)]
Linear Algebra
Next, the command
g = eig(G)
produces
g =
[ cos(t)+(cos(t)^2–1)^(1/2)]
[ cos(t)–(cos(t)^2–1)^(1/2)]
We can use simple to simplify this form of g. Indeed, repeated application of
simple
for j = 1:4
[g,how] = simple(g)
end
produces the best result
g =
[ cos(t)+(–sin(t)^2)^(1/2)]
[ cos(t)–(–sin(t)^2)^(1/2)]
how =
simplify
g =
[ cos(t)+i*sin(t)]
[ cos(t)–i*sin(t)]
how =
radsimp
g =
[
exp(i*t)]
[ 1/exp(i*t)]
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how =
convert(exp)
g =
[ exp(i*t)]
[ exp(–i*t)]
how =
simplify
Notice the first application of simple uses simplify to produce a sum of sines
and cosines. Next, simple invokes radsimp to produce cos(t) + i*sin(t) for
the first eigenvector. The third application of simple uses convert(exp) to
change the sines and cosines to complex exponentials. The last application of
simple uses simplify to obtain the final form.
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Linear Algebra
Jordan Canonical Form
The Jordan canonical form results from attempts to diagonalize a matrix by a
similarity transformation. For a given matrix A, find a nonsingular matrix V,
so that inv(V)*A*V, or, more succinctly, J = V\A*V, is “as close to diagonal as
possible.” For almost all matrices, the Jordan canonical form is the diagonal
matrix of eigenvalues and the columns of the transformation matrix are the
eigenvectors. This always happens if the matrix is symmetric or if it has
distinct eigenvalues. Some nonsymmetric matrices with multiple eigenvalues
cannot be diagonalized. The Jordan form has the eigenvalues on its diagonal,
but some of the superdiagonal elements are one, instead of zero. The statement
J = jordan(A)
computes the Jordan canonical form of A. The statement
[V,J] = jordan(A)
also computes the similarity transformation. The columns of V are the
generalized eigenvectors of A.
The Jordan form is extremely sensitive to perturbations. Almost any change in
A causes its Jordan form to be diagonal. This makes it very difficult to compute
the Jordan form reliably with floating-point arithmetic. It also implies that A
must be known exactly (i.e., without round-off error, etc.). Its elements must be
integers, or ratios of small integers. In particular, the variable-precision
calculation, jordan(vpa(A)), is not allowed.
For example, let
A = sym([12,32,66,116;–25,–76,–164,–294;
21,66,143,256;–6,–19,–41,–73])
A =
[
12,
32,
66, 116]
[ –25, –76, –164, –294]
[
21,
66, 143, 256]
[
–6, –19, –41, –73]
Then
[V,J] = jordan(A)
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produces
V =
[
4,
[ –6,
[
4,
[ –1,
–2,
4,
8, –11,
–7, 10,
2, –3,
J
[
[
[
[
0,
0,
2,
0,
=
1,
0,
0,
0,
1,
1,
0,
0,
3]
–8]
7]
–2]
0]
0]
1]
2]
Therefore A has a double eigenvalue at 1, with a single Jordan block, and a
double eigenvalue at 2, also with a single Jordan block. The matrix has only
two eigenvectors, V(:,1) and V(:,3). They satisfy
A*V(:,1) = 1*V(:,1)
A*V(:,3) = 2*V(:,3)
The other two columns of V are generalized eigenvectors of grade 2. They
satisfy
A*V(:,2) = 1*V(:,2) + V(:,1)
A*V(:,4) = 2*V(:,4) + V(:,3)
In mathematical notation, with vj = v(:,j), the columns of V and eigenvalues
satisfy the relationships
( A – λ 2 I )v 4 = v 3
( A – λ 1 I )v 2 = v 1
Singular Value Decomposition
Only the variable-precision numeric computation of the singular value
decomposition is available in the toolbox. One reason for this is that the
formulas that result from symbolic computation are usually too long and
complicated to be of much use. If A is a symbolic matrix of floating-point or
variable-precision numbers, then
S = svd(A)
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Linear Algebra
computes the singular values of A to an accuracy determined by the current
setting of digits. And
[U,S,V] = svd(A);
produces two orthogonal matrices, U and V, and a diagonal matrix, S, so that
A = U*S*V';
Let’s look at the n-by-n matrix A with elements defined by
A(i,j) = 1/(i–j+1/2)
There are several interesting ways to generate this matrix A. They are
described below. For n = 5, the matrix is
[ 2
[2/3
[2/5
[2/7
[2/9
–2
2
2/3
2/5
2/7
–2/3
–2
2
2/3
2/5
–2/5
–2/3
–2
2
2/3
–2/7]
–2/5]
–2/3]
–2]
2]
It turns out many of the singular values of these matrices are close to π. When
n = 16, the MATLAB floating-point computation svd(A) results in
3.14159265358979
3.14159265358979
3.14159265358979
3.14159265358979
3.14159265358976
3.14159265358767
3.14159265349961
3.14159265052655
3.14159256925492
3.14159075458606
3.14155754359918
3.14106044663470
3.13504054399745
3.07790297231120
2.69162158686066
1.20968137605669
The first four singular values appear equal to π to the available precision.
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The most obvious way of generating the 5-by-5 matrix A whose i-, j-th element
1/(i–j+1/2) is
for i=1:n
for j=1:n
A(i,j) = sym(1/(i–j+1/2));
end
end
MATLAB’s matrix-based computational paradigm, however, permits other,
more efficient, approaches. Consider the famous (among MATLAB
afficianados) “Tony’s Trick.”
m = ones(n,1); i=(1:n)'; j=1:n;
A = sym(1./(i(:,m)–j(m,:)+1/2));
The most efficient way to generate this matrix is with the purely numeric
statements
[J,I] = meshgrid(1:n);
A = sym(1./(I – J+1/2));
Since the elements of A are the ratios of small integers, vpa(A) produces a
variable-precision representation, which is accurate to digits precision. Hence
S = svd(vpa(A))
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Linear Algebra
computes the desired singular values to full accuracy. With n = 16 and
digits(30), the result is
S
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
[
=
1.20968137605668985332455685357
2.69162158686066606774782763594
3.07790297231119748658424727354
3.13504054399744654843898901261
3.14106044663470063805218371924
3.14155754359918083691050658260
3.14159075458605848728982577119
3.14159256925492306470284863102
3.14159265052654880815569479613
3.14159265349961053143856838564
3.14159265358767361712392612384
3.14159265358975439206849907220
3.14159265358979270342635559051
3.14159265358979323325290142781
3.14159265358979323843066846712
3.14159265358979323846255035974
]
]
]
]
]
]
]
]
]
]
]
]
]
]
]
]
There are two ways to compare S with pi, the floating-point representation of
π. In the vector below, the first element is computed by subtraction with
variable-precision arithmetic and then converted to a double. The second
element is computed with floating-point arithmetic.
[double(pi*ones(16,1)–S)
pi–double(S)]
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The results are
1.9319e+00
4.4997e–01
6.3690e–02
6.5521e–03
5.3221e–04
3.5110e–05
1.8990e–06
8.4335e–08
3.0632e–09
9.0183e–11
2.1196e–12
3.8846e–14
5.3504e–16
5.2097e–18
3.1975e–20
9.3024e–23
1.9319e+00
4.4997e–01
6.3690e–02
6.5521e–03
5.3221e–04
3.5110e–05
1.8990e–06
8.4335e–08
3.0632e–09
9.0183e–11
2.1196e–12
3.8636e–14
4.4409e–16
0
0
0
Since the relative accuracy of pi is pi*eps, which is 6.9757e–16, either column
confirms our suspicion that four of the singular values of the 16-by-16 example
equal π to floating-point accuracy.
Eigenvalue Trajectories
This example applies several numeric, symbolic, and graphic techniques to
study the behavior of matrix eigenvalues as a parameter in the matrix is
varied. This particular setting involves numerical analysis and perturbation
theory, but the techniques illustrated are more widely applicable.
In this example, we consider a 3-by-3 matrix A whose eigenvalues are 1, 2, 3.
First, we perturb A by another matrix E and parameter t: A → A + tE. As t
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Linear Algebra
increases from 0 to 10–6, the eigenvalues λ1 = 1, λ2 = 2, λ3 = 3 change to
λ1' ≈ 1.5596 + 0.2726i, λ2' ≈ 1.5596 – 0.2726i, λ3' ≈ 2.8808.
λ’(1)
0.3
0.2
0.1
λ(1)
λ(2)
λ(3)
0
λ’(3)
−0.1
−0.2
λ’(2)
−0.3
0
0.5
1
1.5
2
2.5
3
3.5
This, in turn, means that for some value of t = τ, 0 < τ < 10–6, the perturbed
matrix A(t) = A + tE has a double eigenvalue λ1 = λ2.
Let’s find the value of t, called τ, where this happens.
The starting point is a MATLAB test example, known as gallery(3).
A = gallery(3)
A =
–149
–50
537
180
–27
–9
–154
546
–25
This is an example of a matrix whose eigenvalues are sensitive to the effects of
roundoff errors introduced during their computation. The actual computed
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eigenvalues may vary from one machine to another, but on a typical
workstation, the statements
format long
e = eig(A)
produce
e =
0.99999999999642
2.00000000000579
2.99999999999780
Of course, the example was created so that its eigenvalues are actually 1, 2, and
3. Note that three or four digits have been lost to roundoff. This can be easily
verified with the toolbox. The statements
B
e
p
f
=
=
=
=
sym(A);
eig(B)'
poly(B)
factor(p)
produce
e =
[1,
2,
3]
p =
x^3–6*x^2+11*x–6
f =
(x–1)*(x–2)*(x–3)
Are the eigenvalues sensitive to the perturbations caused by roundoff error
because they are “close together”? Ordinarily, the values 1, 2, and 3 would be
regarded as “well separated.” But, in this case, the separation should be viewed
on the scale of the original matrix. If A were replaced by A/1000, the
eigenvalues, which would be .001, .002, .003, would “seem” to be closer
together.
But eigenvalue sensitivity is more subtle than just “closeness.” With a carefully
chosen perturbation of the matrix, it is possible to make two of its eigenvalues
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Linear Algebra
coalesce into an actual double root that is extremely sensitive to roundoff and
other errors.
One good perturbation direction can be obtained from the outer product of the
left and right eigenvectors associated with the most sensitive eigenvalue. The
following statement creates
E = [130,–390,0;43,–129,0;133,–399,0]
the perturbation matrix
E =
130
43
133
–390
–129
–399
0
0
0
The perturbation can now be expressed in terms of a single, scalar parameter
t. The statements
syms x t
A = A+t*E
replace A with the symbolic representation of its perturbation.
A =
[–149+130*t, –50–390*t, –154]
[ 537+43*t, 180–129*t, 546]
[ –27+133*t, –9–399*t, –25]
Computing the characteristic polynomial of this new A
p = poly(A)
gives
p =
x^3+(–t–6)*x^2+(492512*t+11)*x–6–1221271*t
Prettyprinting
pretty(collect(p,x))
shows more clearly that p is a cubic in x whose coefficients vary linearly with t
3
[ x
2
+ (– t – 6) x
+ (492512 t + 11) x – 6 – 1221271 t ]
1-89
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Tutorial
It turns out that when t is varied over a very small interval, from 0 to 1.0e–6,
the desired double root appears. This can best be seen graphically. The first
figure shows plots of p, considered as a function of x, for three different values
of t: t = 0, t = 0.5e–6, and t = 1.0e–6. For each value, the eigenvalues are
computed numerically and also plotted.
x = .8:.01:3.2;
for k = 0:2
c = sym2poly(subs(p,t,k*0.5e–6));
y = polyval(c,x);
lambda = eig(double(subs(A,t,k*0.5e–6)));
subplot(3,1,3–k)
plot(x,y,'–',x,0*x,':',lambda,0*lambda,'o')
axis([.8 3.2 –.5 .5])
text(2.25,.35,['t = ' num2str( k*0.5e–6 )]);
end
0.5
t = 1e−06
0
−0.5
1
1.5
2
2.5
3
0.5
t = 5e−07
0
−0.5
1
1.5
2
2.5
3
2.5
3
0.5
t=0
0
−0.5
1
1.5
2
The bottom subplot shows the unperturbed polynomial, with its three roots at
1, 2, and 3. The middle subplot shows the first two roots approaching each
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Linear Algebra
other. In the top subplot, these two roots have become complex and only one
real root remains.
The next statements compute and display the actual eigenvalues
e = eig(A);
pretty(e)
showing that e(2) and e(3) form a complex conjugate pair.
[
1/3
]
[
1/3 %1
– 3 %2 + 2 + 1/3 t
]
[
]
[
1/3
1/2
1/3
]
[– 1/6 %1
+ 3/2 %2 + 2 + 1/3 t + 1/2 i 3
(1/3 %1
+ 3 %2)]
[
]
[
1/3
1/2
1/3
]
[– 1/6 %1
+ 3/2 %2 + 2 + 1/3 t – 1/2 i 3
(1/3 %1
+ 3 %2)]
2
3
%1 := 3189393 t – 2216286 t + t + 3 (–3 + 4432572 t
2
3
– 1052829647418 t + 358392752910068940 t
4 1/2
– 181922388795 t )
2
– 1/3 + 492508/3 t – 1/9 t
%2 := --------------------------1/3
%1
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Next, the symbolic representations of the three eigenvalues are evaluated at
many values of t
tvals = (2:–.02:0)' * 1.e–6;
r = size(tvals,1);
c = size(e,1);
lambda = zeros(r,c);
for k = 1:c
lambda(:,k) = double(subs(e(k),t,tvals));
end
plot(lambda,tvals)
xlabel('\lambda'); ylabel('t');
title('Eigenvalue Transition')
to produce a plot of their trajectories:
−6
2
Eigenvalue Transition
x 10
1.8
1.6
1.4
t
1.2
1
0.8
0.6
0.4
0.2
0
1
1.2
1.4
1.6
1.8
2
λ
2.2
2.4
2.6
2.8
3
Above t = 0.8e–6, the graphs of two of the eigenvalues intersect, while below
t = 0.8e–6, two real roots become a complex conjugate pair. What is the precise
value of t that marks this transition? Let τ denote this value of t.
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Linear Algebra
One way to find the exact value of τ involves polynomial discriminants. The
discriminant of a quadratic polynomial is the familiar quantity under the
square root sign in the quadratic formula. When it is negative, the two roots
are complex.
There is no discrim function in the toolbox, but there is one in Maple and it can
be accessed through the toolbox’s maple function. The statement
mhelp discrim
provides a brief explanation. Use these commands
syms a b c x
maple('discrim', a*x^2+b*x+c, x)
to show the generic quadratic’s discriminant, b2 – 4ac
ans =
–4*a*c+b^2
The discriminant for the perturbed cubic characteristic polynomial is obtained,
using
discrim = maple('discrim',p,x)
which produces
[discrim =
4–5910096*t+1403772863224*t^2–
477857003880091920*t^3+242563185060*t^4]
The quantity τ is one of the four roots of this quartic. Let’s find a numeric value
for τ
digits(24)
s = solve(discrim);
tau = vpa(s)
tau =
[
1970031.04061804553618913]
[
.783792490602e–6]
[ .1076924816049e–5+.318896441018863170083895e–5*i]
[ .1076924816049e–5–.318896441018863170083895e–5*i]
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Of the four solutions, we know that
tau = tau(2)
is the transition point
tau =
.783792490602e–6
because it is closest to our previous estimate.
A more generally applicable method for finding τ is based on the fact that, at a
double root, both the function and its derivative must vanish. This results in
two polynomial equations to be solved for two unknowns. The statement
sol = solve(p,diff(p,x))
solves the pair of algebraic equations p = 0 and dp/dx = 0 and produces
sol =
t: [4x1 sym]
x: [4x1 sym]
Find τ now by
tau = double(sol.t(2))
which reveals that the second element of sol.t is the desired value of τ
format short
tau =
7.8379e–07
Therefore, the second element of sol.x
sigma = double(sol.x(2))
is the double eigenvalue
sigma =
1.5476
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Linear Algebra
Let’s verify that this value of τ = 7.8379e – 07 does indeed produce a double
eigenvalue at σ = 1.5476. To achieve this, substitute τ for t in the perturbed
matrix A(t) = A + tE and find the eigenvalues of A(t). That is,
e = eig(double(subs(A,t,tau)))
e =
1.5476
1.5476
2.9047
confirms that σ = 1.5476 is a double eigenvalue of A(t) for t = 7.8379e – 07.
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Solving Equations
Solving Algebraic Equations
If S is a symbolic expression,
solve(S)
attempts to find values of the symbolic variable in S (as determined by
findsym) for which S is zero. For example
syms a b c x
S = a*x^2 + b*x + c;
solve(S)
uses the familiar quadratic formula to produce
ans =
[1/2/a*(–b+(b^2–4*a*c)^(1/2))]
[1/2/a*(–b–(b^2–4*a*c)^(1/2))]
This is a symbolic vector whose elements are the two solutions.
If you want to solve for a specific variable, you must specify that variable as an
additional argument. For example, if you want to solve S for b, use the
command
b = solve(S,b)
which returns
b =
–(a*x^2+c)/x
Note that these examples assume equations of the form f(x) = 0. If you need to
solve equations of the form f(x) = q(x), you must use quoted strings. In
particular, the command
s = solve('cos(2*x)+sin(x)=1')
1-96
Solving Equations
returns a vector with four solutions.
s =
[
0]
[
pi]
[ 1/6*pi]
[ 5/6*pi]
The equation x3 – 2x2 = x – 1 helps us understand the output of solve. Type
s = solve('x^3–2*x^2 = x–1')
s =
[ 1/6*(28+84*i*3^(1/2))^(1/3)+14/3/(28+84*i*3^(1/2))^(1/3)+2/3]
[ –1/12*(28+84*i*3^(1/2))^(1/3)–7/3/(28+84*i*3^(1/2))^(1/3)
+2/3+1/2*i*3^(1/2)*(1/6*(28+84*i*3^(1/2))^(1/3)
–14/3/(28+84*i*3^(1/2))^(1/3))]
[ –1/12*(28+84*i*3^(1/2))^(1/3)–7/3/(28+84*i*3^(1/2))^(1/3)
+2/3–1/2*i*3^(1/2)*(1/6*(28+84*i*3^(1/2))^(1/3)
–14/3/(28+84*i*3^(1/2))^(1/3))]
To gain more insight into this solution, let’s see the numeric results
double(s)
ans =
2.24697960371747 + 0.00000000000000i
–0.80193773580484 + 0.00000000000000i
0.55495813208737 – 0.00000000000000i
It appears that the roots of x3 – 2x2 = x – 1 are all real. This is misleading.
Applying the vpa command
vpa(s, 10)
yields
ans =
[ 2.246979604+.1e–9*i]
[ –.8019377357+.3e–9*i]
[ .5549581323–.5e–9*i]
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This shows that the imaginary parts of s are small, but still nonzero.
As another example, consider the commands
syms x
s = solve(tan(x)+sin(x)–2);
This results in a lengthy 4-by-1 symbolic vector whose qualitative features are,
at best, cryptic. As above, use the double command
X = double(s)
which produces
X =
0.8863
–1.8979
2.0766– 1.5151i
2.0766+ 1.5151i
Are these the only two real solutions of tan(x) + sin(x) – 2 for x ∈ [-2π, 2π]? To
answer this question, plot the function against the real axis
ezplot(tan(x)+sin(x)–2)
hold on
w = –2*pi:pi/2:2*pi;
plot(w,0*w,'r-.');
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Solving Equations
The resulting plot
tan(x)+sin(x)−2
5
0
−5
−10
−6
−4
−2
0
x
2
4
6
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Tutorial
shows that tan(x) + sin(x) – 2 has four intersections with the real axis for x ∈ [–
2π, 2π]. Since the first two elements of the solution vector X are real, we can plot
these on our current figure.
RX = [X(1), X(2)]
plot(RX, 0*RX,'gO')
text(–1.8,–0.4,'X(2)')
text(1.0,–0.4,'X(1)')
tan(x)+sin(x)−2
5
0
X(2)
X(1)
−5
−10
−6
1-100
−4
−2
0
x
2
4
6
Solving Equations
To display the remaining two roots in the interval [–2π, 2π], subtract 2π from
X(1) = 0.8863, add 2π to X(2) = –1.8970, and plot the points
PX = [X(1)–2*pi,X(2)+2*pi]
plot(PX, 0*PX,'kO')
hold off
tan(x)+sin(x)−2
5
0
X(1)−2*pi
X(2)
X(1)
X(2)+2*pi
−5
−10
−6
−4
−2
0
x
2
4
6
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Tutorial
Again for
f = cos(2*x) + sin(x) – 1;
s = solve(f);
plotting s and f, using
ezplot(f)
hold on;
plot(w,0*w,'r-.')
plot(double(s),0*double(s),'gO')
cos(2*x)+sin(x)−1
0
−0.5
−1
−1.5
−2
−2.5
−3
−6
1-102
−4
−2
0
x
2
4
6
Solving Equations
makes it clear that solve does not return all solutions. Plotting the points
–2*pi, –11/6*pi, –(7/6)*pi, –p along with f shows that these points are
also solutions.
s2 = [–2*pi, –11/6*pi, –(7/6)*pi, –pi]
plot(s2, 0*s2, 'kX')
(
)
( )
0
−0.5
−1
−1.5
−2
−2.5
−3
−6
−4
−2
0
x
2
4
6
If we cannot find a symbolic solution, we can compute a variable-precision one.
For example
digits(6)
syms x
X = solve(log(x)–sin(x)) % Equivalently, solve('log(x)=sin(x)')
results in
X =
.996957e-1-1.19186*i
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Several Algebraic Equations
Now let's look at systems of equations. Suppose we have the system
2 2
x y = 0
y
x – --- = α
2
and we want to solve for x and y. First create the necessary symbolic objects
syms x y alpha
There are several ways to address the output of solve. One is to use a
two-output call
[x,y] = solve(x^2*y^2; x–(y/2)–alpha)
which returns
x =
[
0]
[
0]
[ alpha]
[ alpha]
y =
[ –2*alpha]
[ –2*alpha]
[
0]
[
0]
Consequently, the solution vector
v = [x, y]
appears to have redundant components. This is due to the first equation
x2 y2 = 0, which has two solutions in x and y: x = ±0, y = ±0. A perturbation to
the equations in the form of
eqs1 = 'x^2*y^2=1, x–1/2*y–alpha'
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Solving Equations
produces four distinct solutions.
x
[
[
[
[
=
1/2*alpha+1/2*(alpha^2+2)^(1/2)]
1/2*alpha–1/2*(alpha^2+2)^(1/2)]
1/2*alpha+1/2*(alpha^2–2)^(1/2)]
1/2*alpha–1/2*(alpha^2–2)^(1/2)]
y
[
[
[
[
=
–alpha+(alpha^2+2)^(1/2)]
–alpha–(alpha^2+2)^(1/2)]
–alpha+(alpha^2–2)^(1/2)]
–alpha–(alpha^2–2)^(1/2)]
Since we did not specify the dependent variables, solve uses findsym to
determine the variables.
This way of assigning output from solve is quite successful for “small” systems.
Plainly, if we had, say, a 10-by-10 system of equations, typing
[x1,x2,x3,x4,x5,x6,x7,x8,x9,x10] = solve(...)
is both awkward and time consuming. To circumvent this difficulty, solve can
return a structure whose fields are the solutions. In particular, consider the
system u^2–v^2 = a^2, u + v = 1, a^2–2*a = 3. The command
S = solve('u^2–v^2 = a^2','u + v = 1','a^2–2*a = 3')
returns
S =
a: [2x1 sym]
u: [2x1 sym]
v: [2x1 sym]
The solutions for a reside in the “a-field” of S. That is,
S.a
produces
ans =
[ –1]
[ 3]
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Similar comments apply to the solutions for u and v. The structure S can now
be manipulated by field and index to access a particular portion of the solution.
For example, if we want to examine the second solution, we can use the
following statement
s2 = [S.a(2), S.u(2), S.v(2)]
to extract the second component of each field
s2 =
[ 3,
5, –4]
The following statement
M = [S.a, S.u, S.v]
creates the solution matrix M
M =
[ –1,
[ 3,
1, 0]
5, –4]
whose rows comprise the distinct solutions of the system.
Linear systems of simultaneous equations can also be solved using matrix
division. For example,
clear u v x y
syms u v x y
S = solve(x+2*y–u, 4*x+5*y–v);
sol = [S.x;S.y]
and
A = [1 2; 4 5];
b = [u; v];
z = A\b
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Solving Equations
result in
sol =
[ –5/3*u+2/3*v]
[ 4/3*u–1/3*v]
z =
[ –5/3*u+2/3*v]
[ 4/3*u–1/3*v]
Thus s and z produce the same solution, although the results are assigned to
different variables.
Single Differential Equation
The function dsolve computes symbolic solutions to ordinary differential
equations. The equations are specified by symbolic expressions containing the
letter D to denote differentiation. The symbols D2, D3, ... DN, correspond to the
second, third, ..., Nth derivative, respectively. Thus, D2y is the Symbolic Math
Toolbox equivalent of d2y/dt2. The dependent variables are those preceded by D
and the default independent variable is t. Note that names of symbolic
variables should not contain D. The independent variable can be changed from
t to some other symbolic variable by including that variable as the last input
argument.
Initial conditions can be specified by additional equations. If initial conditions
are not specified, the solutions contain constants of integration, C1, C2, etc.
The output from dsolve parallels the output from solve. That is, you can call
dsolve with the number of output variables equal to the number of dependent
variables or place the output in a structure whose fields contain the solutions
of the differential equations. The table below details the output syntax.
Syntax
Scope
y = dsolve('Dyt= y0*y')
One equation, one output
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Syntax
Scope
[u,v] = dsolve('Du = v', 'Dv = u')
Two equations, two output
S = dsolve('Df=g','Dg=h','Dh=–f')
Three equations, output
structure
S.f, S.g, S.h
Solutions
Example 1
The following call to dsolve
dsolve('Dy=1+y^2')
uses y as the dependent variable and t as the default independent variable.
The output of this command is
ans =
tan(t–C1)
To specify an initial condition, use
y = dsolve('Dy=1+y^2','y(0)=1')
This produces
y =
tan(t+1/4*pi)
Notice that y is in the MATLAB workspace, but the independent variable t is
not. Thus, the command diff(y,t) returns an error. To place t in the
workspace, type syms t.
Example 2
Nonlinear equations may have multiple solutions, even when initial conditions
are given.
x = dsolve('(Dx)^2+x^2=1','x(0)=0')
results in
x =
[–sin(t)]
[ sin(t)]
1-108
Solving Equations
Example 3
Here is a second order differential equation with two initial conditions. The
command
y = dsolve('D2y=cos(2*x)–y','y(0)=1','Dy(0)=0', 'x')
produces
y =
–2/3*cos(x)^2+1/3+4/3*cos(x)
The key issues in this example are the order of the equation and the initial
conditions. To solve the ordinary differential equation
3
d u
dx
3
= u
u(0) = 1, u'(0) = –1, u''(0) = π
simply type
u = dsolve('D3u=u','u(0)=1','Du(0)=–1','D2u(0) = pi','x')
Use D3u to represent d3u/dx3 and D2u(0) for u''(0).
Several Differential Equations
The function dsolve can also handle several ordinary differential equations in
several variables, with or without initial conditions. For example, here is a pair
of linear, first order equations.
S = dsolve('Df = 3*f+4*g', 'Dg = –4*f+3*g')
The computed solutions are returned in the structure S. You can determine the
values of f and g by typing
f = S.f
f =
C2*exp(3*t)*sin(4*t)–C1*exp(3*t)*cos(4*t)
g = S.g
g =
C1*exp(3*t)*sin(4*t)+C2*exp(3*t)*cos(4*t)
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If you prefer to recover f and g directly as well as include initial conditions,
type
[f,g] = dsolve('Df=3*f+4*g, Dg =–4*f+3*g', 'f(0) = 0, g(0) = 1')
f =
exp(3*t)*sin(4*t)
g =
exp(3*t)*cos(4*t)
This table details some examples and Symbolic Math Toolbox syntax. Note that
the final entry in the table is the Airy differential equation whose solution is
referred to as the Airy function.
Differential Equation
MATLAB Command
y = dsolve('Dy+4*y = exp(–t)',
'y(0) = 1')
dy
–t
+ 4y ( t ) = e
dt
y(0) = 1
y = dsolve('D2y+4*y = exp(–2*x)',
'y(0)=0', 'y(pi) = 0', 'x')
2
d y
dx
2
+ 4y ( x ) = e
– 2x
y ( 0 ) = 0, y ( π ) = 0
y = dsolve('D2y = x*y','y(0) = 0',
'y(3) = besselk(1/3, 2*sqrt(3))/pi',
'x')
2
d y
dx
2
= xy ( x )
1
y ( 0 ) = 0, y ( 3 ) = --- K 1 ( 2 3 )
π --3
(The Airy Equation)
The Airy function plays an important role in the mathematical modeling of the
dispersion of water waves. It is a nontrivial exercise to show that the Fourier
transform of the Airy function is exp(iw3/3). With this in mind, we proceed to
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Solving Equations
the section “Integral Transforms”—the next stop on our tour of the Symbolic
Math Toolbox.
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Integral Transforms
Integral transforms and their discrete counterparts are powerful and
important computational tools in engineering, applied mathematics, and
science. The Laplace transform acts on continuous systems (differential
equations); its discrete analog, the z-transform, operates on discrete systems
(difference equations). Similarly, the Fourier transform operates on continuous
functions; the discrete Fourier transform (DFT), on finite data samples. The
most famous and efficient DFT is the fast Fourier transform (FFT).
The Symbolic Math Toolbox provides commands that enable you to compute
the analytic Fourier, Laplace, and z-transforms and their inverses. If you want
to take the FFT of a data set, use the MATLAB command fft. The following
sections describe the Symbolic Math Toolbox syntax for these three
fundamental transforms and their inverses.
The Fourier and Inverse Fourier Transforms
The Fourier transform of a function f(x) is defined as
∞
F[ f ]( w ) =
∫ f ( x )e
– iwx
dx
–∞
and the inverse Fourier transform (IFT) as
∞
1
F [ f ] ( x ) = -----2π
–1
∫ f ( w )e
iwx
dw
–∞
We refer to this formulation as the Fourier transform of f with respect to x as
a function of w. Or, more concisely, the Fourier transform of f with respect to x
at w. Mathematicians often use the notation F[f] to denote the Fourier
transform of f. In this setting, the transform is taken with respect to the
independent variable of f (if f = f(t), then t is the independent variable; f = f(x)
implies that x is the independent variable, etc.) at the default variable w. We
refer to F[f] as the Fourier transform of f at w and F–1[f] is the IFT of f at x. See
fourier and ifourier in the reference pages for tables that show the Symbolic
Math Toolbox commands equivalent to various mathematical representations
of the Fourier and inverse Fourier transforms.
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Integral Transforms
In the section on differential equations, we mentioned the Airy differential
equation
2
d y
dx
= xy ( x )
2
whose solution, A(x), has a Fourier transform of
iw ⁄ 3
3
e
Let’s verify this claim with the Symbolic Math Toolbox. Indeed,
F[ A] = e
iw ⁄ 3
3
if and only if
–1
A = F [e
iw ⁄ 3
3
]
The statements
syms w x
A = ifourier(exp(i*w^3/3),w,x);
A = simple(A);
pretty(A)
return
1/3
1/2
1/2
1/2
1/3 3/2
3
x
besselk(1/3, 2/9 3
(x 3
)
)
-----------------------------------------------------pi
A( x ) =
2 3⁄2
K 1 ⁄ 3  --- x 


3
x
--- ---------------------------------π
3
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where K1/3(z) is a Bessel function of the second kind of order 1/3. Solving the
Airy differential equation
Y = dsolve('D2y = x*y','x');
pretty(Y)
returns
1/2
3/2
1/2
3/2
C1 x
besseli(1/3, 2/3 x
) + C2 x
besselk(1/3, 2/3 x
)
and setting
1
C1 = 0, C2 = ----------3π
yields A(x).
An application of the Fourier transform is the solution of ordinary and partial
differential equations over the real line. Consider the deformation of an
infinitely long beam resting on an elastic foundation with a shock applied to it
at a point. A “real world” analogy to this phenomenon is a set of railroad tracks
atop a road bed.
δ(x)
shock
.......
x
road
bed
k
bed rock
stiffness
constant
y(x)
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Integral Transforms
The shock could be induced by a pneumatic hammer blow (or a swing from the
mighty John Henry!).
The differential equation idealizing this physical setting is
4
k
1
+ ------- y = ------- δ ( x ), – ∞ < x < ∞
EI
EI
dx
d y
4
Here, E represents elasticity of the beam (rail road track), I is the “beam
constant,” and k is the spring (road bed) stiffness. The shock force on the right
hand side of the differential equation is modeled by the Dirac Delta function
δ(x). If you are unfamiliar with δ(x), you may be surprised to learn that (despite
its name), it is not a function at all. Rather, δ(x) is an example of what
mathematicians call a distribution. The Dirac Delta function (named after the
physicist Paul Dirac) has the following important property
∞
∫ f ( x – y )δ ( x ) dx
= f(x)
–x
A definition of the Dirac Delta function is
δ ( x ) = lim nχ ( – 1 ⁄ 2n, 1 ⁄ 2n ) ( x )
n→∞
where

 1
χ ( – 1 ⁄ 2n, 1 ⁄ 2n ) ( x ) = 
 0

1
1
for – ------- < x < ------2n
2n
otherwise
You can evaluate the Dirac Delta function at a point (say) x = 3, using the
commands
syms x
del = sym('Dirac(x)');
vpa(subs(del,x,3))
which return
ans =
0
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Tutorial
Returning to the differential equation, let Y(w) = F[y(x)](w) and
∆(w) = F[δ(x)](w). Indeed, try the command fourier(del,x,w). The Fourier
transform turns differentiation into exponentiation, and, in particular,
4
F
d y
dx
4
4
(w) = w Y(w)
To see a demonstration of this property, try this
syms w x
fourier(diff(sym('y(x)'),x,4),x,w)
which returns
ans =
w^4*fourier(y(x),x,w)
Note that you can call the fourier command with one, two, or three inputs (see
the reference pages for fourier). With a single input argument, fourier(f)
returns a function of the default variable w. If the input argument is a function
of w, fourier(f) returns a function of t. All inputs to fourier must be symbolic
objects.
We now see that applying the Fourier transform to the differential equation
above yields the algebraic equation
k
w 4 + ------ Y(w) = ∆(w)

EI 
or
Y(w) = ∆(w) G(w)
where
1
G ( w ) = --------------------- = F [ g ( x ) ] ( w )
4
k
w + ------EI
for some function g(x). That is, g is the inverse Fourier transform of G
g(x) = F–1[G(w)](x)
1-116
Integral Transforms
The Symbolic Math Toolbox counterpart to the IFT is ifourier. This behavior
of ifourier parallels fourier with one, two, or three input arguments (see the
reference pages for ifourier).
Continuing with the solution of our differential equation, we observe that the
ratio
K
------EI
is a relatively “large” number since the road bed has a high stiffness constant
k and a rail road track has a low elasticity E and beam constant I. We make the
simplifying assumption that
K
------- = 1024
EI
This is done to ease the computation of F–1[G(w)](x). Proceeding, we type
G = 1/(w^4 + 1024);
g = ifourier(G,w,x);
g = simple(expand(g));
pretty(g)
and see
(–1/1024 + 1/1024i) (exp((4 – 4i)x) Heaviside(x) – exp((4 – 4 i)x)
+ i exp((4 + 4i) x) Heaviside(x) – i Heaviside(x) exp((–4 – 4i)x)
– Heaviside(x) exp((–4 + 4 i) x) – i exp((4 + 4 i) x))
Notice that g contains the Heaviside distribution
for x > 0
 1

H(x ) =  0
for x < 0

 singular for x = 0
We can factor out the Heaviside distribution.
H = sym('Heaviside(x)');
g = collect(g,H);
pretty(g)
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Tutorial
returns
(– 1/1024 + 1/1024 i) (exp((4 – 4 i) x) + i exp((4 + 4 i) x)
– i exp((–4 – 4 i) x) – exp((–4 + 4 i) x)) Heaviside(x)
+ (– 1/1024 + 1/1024 i) (–exp((4 – 4 i) x) – i exp((4 + 4 i) x))
Since Y is the product of Fourier transforms, then y is the convolution of the
transformed functions. That is, F[y] = Y(w) = ∆(w) G(w) = F[δ] F[g] implies
∞
y( x ) = (δ ⋅ g)( x) =
∫ g ( x – y )δ ( x ) dx
= g(x)
–∞
by the special property of the Dirac Delta function. To plot this function, we
must substitute the domain of x into y(x), using the subs command.
XX = –3:0.05:3;
YY = double(subs(g,x,XX));
plot(XX,YY)
title('Beam Deflection for a Point Shock')
xlabel('x'); ylabel('y(x)');
1-118
Integral Transforms
The resulting graph
−4
20
Beam Deflection for a Point Shock
x 10
15
y(x)
10
5
0
−5
−3
−2
−1
0
x
1
2
3
shows that the impact of a blow on a beam is highly localized; the greatest
deflection occurs at the point of impact and falls off sharply from there. This is
the behavior we expect from experience.
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Tutorial
The Laplace and Inverse Laplace Transforms
The Laplace transform of a function f(t) is defined as
∞
L[ f ]( s ) =
∫ f ( t )e
– ts
dt
0
while the inverse Laplace transform (ILT) of f(s) is
c + i∞
1
L [ f ] ( t ) = --------2πi
∫
–1
st
f ( s )e ds
c – i∞
where c is a real number selected so that all singularities of f(s) are to the left
of the line s = c. The notation L[f] denotes the Laplace transform of f at s.
Similarly, L–1[f] is the ILT of f at t.
The Laplace transform has many applications including the solution of
ordinary differential equations/initial value problems. Consider the
resistance-inductor-capacitor (RLC) circuit below..
N1
E(t)
I2
I1
R1
I3
R2
R3
+
L
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N2
C
Integral Transforms
Let Rj and Ij, j = 1, 2, 3 be resistances (measured in ohms) and currents (amps),
respectively; L be inductance (henrys), and C be capacitance (farads); E(t) be
the electromotive force, and Q(t) be the charge
By applying Kirchhoff’s voltage and current laws, Ohm’s Law, Faraday’s Law,
and Henry’s Law, you can arrive at the following system of simultaneous
ordinary differential equations.
dI 1
dt
R 2 dQ
R1 – R2
= --------------------I 1, I 1 ( 0 ) = I 10
+ ------L
L dt
R2
1
1
dQ
= --------------------- E ( t ) – ---- Q ( t ) + --------------------- I 1, Q ( 0 ) = Q 0
C
R3 + R2
R3 + R2
dt
Let’s solve this system of differential equations using laplace. We will first
treat the Rj, L, and C as (unknown) real constants and then supply values later
on in the computation.
syms R1 R2 R3 L C real
dI1 = sym('diff(I1(t),t)'); dQ = sym('diff(Q(t),t)');
I1 = sym('I1(t)'); Q = sym('Q(t)');
syms t s
E = sin(t); % Voltage
eq1 = dI1 + R2*dQ/L – (R2 – R1)*I1/L;
eq2 = dQ – (E – Q/C)/(R2 + R3) – R2*I1/(R2 + R3);
At this point, we have constructed the equations in the MATLAB workspace.
An approach to solving the differential equations is to apply the Laplace
transform, which we will apply to eq1 and eq2. Transforming eq1 and eq2
L1 = laplace(eq1,t,s)
L2 = laplace(eq2,t,s)
returns
L1 =
[s*laplace(I1(t),t,s) – I1(0) + R2/L*(s*laplace(Q(t),t,s) – Q(0))
– (R2 – R1)/L*laplace(I1(t),t,s)]
L2 =
[s*laplace(Q(t),t,s) – Q(0) – 1/(R2 + R3)/C*(C/(s^2 + 1) –
laplace(Q(t),t,s)) – R2/(R2 + R3)*laplace(I1(t),t,s)]
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Tutorial
Now we need to solve the system of equations L1 = 0, L2 = 0 for
laplace(I1(t),t,s) and laplace(Q(t),t,s), the Laplace transforms of I1
and Q, respectively. To do this, we need to make a series of substitutions. For
the purposes of this example, use the quantities R1 = 4 Ω (ohms), R2 = 2 Ω,
R3 = 3 Ω, C = 1/4 farads, L = 1.6 H (henrys), I1(0) = 15 amps, and Q(0) = 2
amps/sec. Substituting these values in L1
syms LI1 LQ
NI1 = subs(L1,{R1,R2,R3,L,C,'I1(0)','Q(0)'}, ...
{4,2,3,1.6,1/4,15,2})
returns
NI1 =
s*laplace(I1(t),t,s) – 35/2 + 5/4*s*laplace(Q(t),t,s)
+ 5/4*laplace(I1(t),t,s)
The substitution
NQ = subs(L2,{R1,R2,R3,L,C,'I1(0)','Q(0)'},{4,2,3,1.6,1/4,15,2})
returns
NQ =
s*laplace(Q(t),t,s) – 2 – 1/5/(s^2+1) + 4/5*laplace(Q(t),t,s)
– 2/5*laplace(I1(t),t,s)
To solve for laplace(I1(t),t,s) and laplace(Q(t),t,s), we make a final
pair of substitutions. First, replace the strings 'laplace(I1(t),t,s)' and
'laplace(Q(t),t,s)' by the syms LI1 and LQ, using
NI1 =
subs(NI1,{'laplace(I1(t),t,s)','laplace(Q(t),t,s)'},{LI1,LQ})
to obtain
NI1 =
s*LI1 – 35/2 + 5/4*s*LQ + 5/4*LI1
Collecting terms
NI1 = collect(NI1,LI1)
1-122
Integral Transforms
gives
NI1 =
(s + 5/4)*LI1 – 35/2 + 5/4*s*LQ
A similar string substitution
NQ =
subs(NQ,{'laplace(I1(t),t,s)','laplace(Q(t),t,s)'},{LI1,LQ})
yields
NQ =
s*LQ – 2 – 1/5/(s^2+1) + 4/5*LQ – 2/5*LI1
which, after collecting terms,
NQ = collect(NQ,LQ)
gives
NQ =
(4/5 + s)*LQ – 2/5*LI1 – 2 – 1/5/(s^2+1)
Now, solving for LI1 and LQ
[LI1,LQ] = solve(NI1,NQ,LI1,LQ)
we obtain
LI1 =
[5*(59*s + 56 + 56*s^2 + 60*s^3)/(51*s^3 + 40*s^2 + 51*s + 20
+ 20*s^4)]
LQ =
[(44*s + 195 + 190*s^2 + 40*s^3)/(51*s^3 + 40*s^2 + 51*s + 20
+ 20*s^4)]
To recover I1 and Q we need to compute the inverse Laplace transform of LI1
and LQ. Inverting LI1
I1 = ilaplace(LI1,s,t)
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Tutorial
produces
I1 =
–5/51*sin(t)+15*exp(–51/40*t)*cosh(1/40*1001^(1/2)*t)
– 1465/7293*exp(–51/40*t)*1001^(1/2)*sinh(1/40*1001^(1/2)*t)
– 5/51*sin(t)
Inverting LQ
Q = ilaplace(LQ,s,t)
yields
Q =
–5/51*cos(t) + 4/51*sin(t)
+ 107/51*exp(–51/40*t)*cosh(1/40*1001^(1/2)*t)
+ 2039/7293*exp(–51/40*t)*1001^(1/2)*sinh(1/40*1001^(1/2)*t)
Now let’s plot the current I1(t) and charge Q(t) in two different time domains,
0 ≤ t ≤ 10 and 5 ≤ t ≤ 25. The statements
subplot(2,2,1); ezplot(I1,[0,10]);
title('Current'); ylabel('I1(t)'); grid
subplot(2,2,2); ezplot(Q,[0,10]);
title('Charge'); ylabel('Q(t)'); grid
subplot(2,2,3); ezplot(I1,[5,25]);
title('Current'); ylabel('I1(t)'); grid
text(7,0.25,'Transient'); text(16,0.125,'Steady State');
subplot(2,2,4); ezplot(Q,[5,25]);
title('Charge'); ylabel('Q(t)'); grid
text(7,0.25,'Transient'); text(15,0.16,'Steady State');
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Integral Transforms
generate the desired plots
Current
Charge
3
5
2.5
4
Q(t)
I1(t)
2
3
2
1.5
1
1
0.5
0
0
0
2
4
6
8
10
0
2
4
t
6
8
10
t
Current
Charge
0.3
0.4
Transient
0.3
Transient
0.2
Steady State
0.1
Q(t)
I1(t)
0.2
0
Steady State
0.1
0
−0.1
−0.1
5
10
15
t
20
25
5
10
15
t
20
25
Note that the circuit’s behavior, which appears to be exponential decay in the
short term, turns out to be oscillatory in the long term. The apparent
discrepancy arises because the circuit’s behavior actually has two components:
an exponential part that decays rapidly (the “transient” component) and an
oscillatory part that persists (the “steady-state” component).
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Tutorial
The Z– and Inverse Z–transforms
The (one-sided) z-transform of a function f(n) is defined as
∞
Z[ f ]( z) =
∑
f ( n )z
–n
n=0
The notation Z[f] refers to the z-transform of f at z. Let R be a positive number
so that the function g(z) is analytic on and outside the circle |z| = R. Then the
inverse z-transform (IZT) of g at n is defined as
1
–1
Z [ g ] ( n ) = --------2πi
°∫
g ( z )z
n–1
dz, n = 1, 2, …
z =R
The notation Z–1[f] means the IZT of f at n. The Symbolic Math Toolbox
commands ztrans and iztrans apply the z-transform and IZT to symbolic
expressions, respectively. See ztrans and iztrans for tables showing various
mathematical representations of the z-transform and inverse z-transform and
their Symbolic Math Toolbox counterparts.
The z-transform is often used to solve difference equations. In particular,
consider the famous “Rabbit Problem.” That is, suppose that rabbits reproduce
only on odd birthdays (1, 3, 5, 7, …). If p(n) is the rabbit population at year n,
then p obeys the difference equation
p(n+2) = p(n+1) + p(n), p(0) = 1, p(1) = 2.
We can use ztrans to find the population each year p(n). First, we apply ztrans
to the equations
pn = sym('p(n)');
pn1 = sym('p(n+1)');
pn2 = sym('p(n+2)');
syms n z
eq = pn2 – pn1 – pn;
Zeq = ztrans(eq, n, z)
to obtain
Zeq =
z^2*ztrans(p(n),n,z)–p(0)*z^2–p(1)*z–z*ztrans(p(n),n,z)+p(0)*z
–ztrans(p(n),n,z)
1-126
Integral Transforms
Next, replace 'ztrans(p(n),n,z)' with Pz and insert the initial conditions for
p(0) and p(1).
syms Pz
Zeq = subs(Zeq,{'ztrans(p(n),n,z)','p(0)','p(1)'},{Pz,1,2})
to obtain
Zeq =
z^2*Pz–1*z^2–2*z–z*Pz+1*z–Pz
Collecting terms
eq = collect(Zeq,Pz)
yields
eq =
(z^2–z–1)*Pz–z^2–z
Now solve for Pz
P = solve(eq,Pz)
to obtain
P =
z*(z + 1)/(z^2 – z – 1)
To recover p(n), we take the inverse z-transform of P.
p = iztrans(P,z,n);
p = simple(p);
pretty(p)
n
1/2
n
n
1/2
n
2
5
2
(–2)
5
(–2)
3/10 --------- + 1/2 -------- – 3/10 ---------- + 1/2 ---------1/2
n
1/2
n
1/2
n
1/2
n
(5 – 1)
(5 – 1)
(5
+ 1)
(5
+ 1)
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Tutorial
Finally, let’s plot p
m = 1:10;
y = double(subs(f,n,m));
plot(m,y,'rO')
title('Rabbit Population');
xlabel('years'); ylabel('f(n)'); grid on
to show the growth in rabbit population over time.
Rabbit Population
150
p
100
50
0
1
2
3
4
5
6
7
8
9
10
years
References
Andrews, L.C., B.K. Shivamoggi, Integral Transforms for Engineers and
Applied Mathematicians, Macmillan Publishing Company, New York, 1986
Crandall, R.E., Projects in Scientific Computation, Springer-Verlag Publishers,
New York, 1994
1-128
Integral Transforms
Strang, G., Introduction to Applied Mathematics, Wellesley-Cambridge Press,
Wellesley, MA, 1986
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Tutorial
Special Mathematical Functions
Over fifty of the special functions of classical applied mathematics are
available in the toolbox. These functions are accessed with the mfun function,
which numerically evaluates a special function for the specified parameters.
This allows you to evaluate functions that are not available in standard
MATLAB, such as the Fresnel cosine integral. In addition, you can evaluate
several MATLAB special functions in the complex plane, such as the error
function.
For example, suppose you want to evaluate the hyperbolic cosine integral at
the points 2+i, 0, and 4.5. First type
help mfunlist
to see the list of functions available for mfun. This list provides a brief
mathematical description of each function, its Maple name, and the
parameters it needs. From the list, you can see that the hyperbolic cosine
integral is called Chi, and it takes one complex argument. For additional
information, you can access Maple help on the hyperbolic cosine integral using
mhelp Chi
Now type
z = [2+i 0 4.5];
w = mfun('Chi',z)
which returns
w =
2.0303 + 1.7227i
NaN
13.9658
mfun returns NaNs where the function has a singularity. The hyperbolic cosine
integral has a singularity at z = 0.
These special functions can be used with the mfun function:
• Airy Functions
• Binomial Coefficients
• Riemann Zeta Functions
• Bernoulli Numbers and Polynomials
• Euler Numbers and Polynomials
1-130
Special Mathematical Functions
• Harmonic Function
• Exponential Integrals
• Logarithmic Integral
• Sine and Cosine Integrals
• Hyperbolic Sine and Cosine Integrals
• Shifted Sine Integral
• Fresnel Sine and Cosine Integral
• Dawson’s Integral
• Error Function
• Complementary Error Function and its Iterated Integrals
• Gamma Function
• Logarithm of the Gamma Function
• Incomplete Gamma Function
• Digamma Function
• Polygamma Function
• Generalized Hypergeometric Function
• Bessel Functions
• Incomplete Elliptic Integrals
• Complete Elliptic Integrals
• Complete Elliptic Integrals with Complementary Modulus
• Beta Function
• Dilogarithm Integral
• Lambert’s W Function
• Dirac Delta Function (distribution)
• Heaviside Function (distribution)
The orthogonal polynomials listed below are available with the Extended
Symbolic Math Toolbox:
• Gegenbauer
• Hermite
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Tutorial
• Laguerre
• Generalized Laguerre
• Legendre
• Jacobi
• Chebyshev of the First and Second Kind
Diffraction
This example is from diffraction theory in classical electrodynamics. (J.D.
Jackson, Classical Electrodynamics, John Wiley & Sons, 1962.)
Suppose we have a plane wave of intensity I0 and wave number k. We assume
that the plane wave is parallel to the xy-plane and travels along the z-axis as
shown below. This plane wave is called the incident wave. A perfectly
conducting flat diffraction screen occupies half of the xy-plane, that is x < 0. The
plane wave strikes the diffraction screen, and we observe the diffracted wave
from the line whose coordinates are
(x, 0, z0), where z0 > 0.
x
y
z
incident plane wave
diffraction screen
line of observation
(x0,0,z0)
The intensity of the diffracted wave is given by
I0
1 2
1 2
I = ----- C ( ζ ) + ---  + S ( ζ ) + --- 

2 
2
2
1-132
Special Mathematical Functions
where
ζ =
k
--------- ⋅ x
2z 0
and C(ξ) and S(ξ) are the Fresnel cosine and sine integrals:
ζ
π
2
π
2
C(ζ ) =
∫0 cos --2- – t
S( ζ) =
∫0 sin --2- – t
ζ

dt
 dt
How does the intensity of the diffracted wave behave along the line of
observation? Since k and z0 are constants independent of x, we set
k
--------- = 1
2z 0
and assume an initial intensity of I0 = 1 for simplicity.
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Tutorial
The following code generates a plot of intensity as a function of x.
x = –50:50;
C = mfun('FresnelC',x);
S = mfun('FresnelS',x);
I0 = 1;
T = (C+1/2).^2 + (S+1/2).^2;
I = (I0/2)*T;
plot(x,I);
xlabel('x');
ylabel('I(x)');
title('Intensity of Diffracted Wave');;
Intensity of Diffracted Wave
1.4
1.2
1
I(x)
0.8
0.6
0.4
0.2
0
-50
-40
-30
-20
-10
0
x
10
20
30
40
50
We see from the graph that the diffraction effect is most prominent near the
edge of the diffraction screen (x = 0), as we expect.
Note that values of x that are large and positive correspond to observation
points far away from the screen. Here, we would expect the screen to have no
effect on the incident wave. That is, the intensity of the diffracted wave should
be the same as that of the incident wave. Similarly, x values that are large and
negative correspond to observation points under the screen that are far away
from the screen edge. Here, we would expect the diffracted wave to have zero
intensity. These results can be verified by setting
x = [Inf –Inf]
1-134
Special Mathematical Functions
in the code to calculate I.
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Tutorial
Using Maple Functions
The maple function lets you access Maple functions directly. This function
takes sym objects, strings, and doubles as inputs. It returns a symbolic object,
character string, or double corresponding to the class of the input. You can also
use the maple function to debug symbolic math programs that you develop.
Simple Example
Suppose we want to write an M-file that takes two polynomials or two integers
and returns their greatest common divisor. For example, the greatest common
divisor of 14 and 21 is 7. The greatest common divisor of x^2–y^2 and x^3–y^3
is x – y.
The first thing we need to know is how to call the greatest common divisor
function in Maple. We use the mhelp function to bring up the Maple online help
for the greatest common divisor (gcd):
Let’s try the gcd function:
mhelp gcd
which returns
Function: gcd - greatest common divisor of polynomials
Function: lcm - least common multiple of polynomials
Calling Sequence:
gcd(a,b,'cofa','cofb')
lcm(a,b,...)
Parameters:
a, b
- multivariate polynomials over the rationals
cofa,cofb - (optional) unevaluated names
1-136
Using Maple Functions
Description:
- The gcd function computes the greatest common divisor of two
polynomials a and b with rational coefficients.
- The lcm function computes the least common multiple of an
arbitrary number of polynomials with rational coefficients.
- The optional third argument cofa is assigned the cofactor
a/gcd(a,b).
- The optional fourth argument cofb is assigned the cofactor
b/gcd(a,b).
Examples:
> gcd(x^2–y^2,x^3–y^3);
–x + y
> lcm(x^2–y^2,x^3–y^3);
3
4
4
3
–y x + y – x + x y
> gcd(6,8,a,b);
2
> a;
3
> b;
4
See Also: gcdex, igcd, ilcm, Gcd, numtheory[GIgcd]
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Since we now know the Maple calling syntax for gcd, we can write a simple
M-file to calculate the greatest common divisor. First, create the M-file gcd in
the @sym directory and include the commands below.
function g = gcd(a, b)
g = maple('gcd',a, b);
If we run this file
syms x y
z = gcd(x^2–y^2,x^3–y^3)
w = gcd(6, 24)
we get
z =
–y+x
w =
6
Now let’s extend our function so that we can take the gcd of two matrices in a
pointwise fashion.
function g = gcd(a,b)
if any(size(a) ~= size(b))
error('Inputs must have the same size.')
end
for k = 1: prod(size(a))
g(k) = maple('gcd',a(k), b(k));
end
g = reshape(g,size(a));
Running this on some test data
A = sym([2 4 6; 3 5 6; 3 6 4]);
B = sym([40 30 8; 17 60 20; 6 3 20]);
gcd(A,B)
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Using Maple Functions
we get the result
ans =
[ 2, 2, 2 ]
[ 1, 5, 2 ]
[ 3, 3, 4 ]
Vectorized Example
Suppose we want to calculate the sine of a symbolic matrix. One way to do this
is:
function y = sin1(x)
for k = 1: prod(size(x))
y(k) = maple('sin',x(k));
end
y = reshape(y,size(x));
So the statements
syms x y
A = [0 x; y pi/4]
sin1(A)
return
A =
[
[
0,
x ]
y, pi/4 ]
ans =
[
[
0,
sin(x) ]
sin(y), 1/2*2^(1/2) ]
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A more efficient way to do this is to call Maple just once, using the Maple map
function. The map function applies a Maple function to each element of an
array. In our sine calculation example, this looks like:
function y = sin2(x)
if prod(size(x)) == 1
% scalar case
y = maple('sin',x);
else
% array case
y = maple('map','sin',x);
end
Note that our sin2 function treats scalar and array cases differently. It applies
the map function to arrays but not to scalars. This is because map applies a
function to each operand of a scalar.
Because our sin2 function calls Maple only once, it is considerably faster than
our sin1 function, which calls Maple prod(size(A)) number of times. For
example, using A = x*ones(10,10) on a Sun-4, sin1(A) takes more than six
times longer to compute than sin2(A). On a Macintosh 7500/100 PowerPC, the
ratio is four-to-one.
The map function can also be used for Maple functions that require multiple
input arguments. In this case, the syntax is
maple('map', Maple function, sym array, arg2, arg3, ..., argn)
For example, one way to call the collect M-file is collect(S,x). In this case,
the collect function collects all the coefficients with the same power of x for
each element in S. The core section of the implementation is shown below:
r = maple('map','collect',sym(s),sym(x));
For additional information on the Maple map function, type
mhelp map
1-140
Using Maple Functions
Debugging
The maple command provides two debugging facilities: trace mode and a status
output argument.
Trace Mode
The command maple traceon causes all subsequent Maple statements and
results to be printed to the screen. For example
maple traceon
a = sym('a');
exp(2*a)
prints all calls made to the Maple kernel for calculating exp(2*a).
statement =
(2)*(a);
result =
2*a
statement =
exp(2*a);
result =
exp(2*a)
ans =
exp(2*a)
To revert back to suppressed printing, use maple traceoff.
Status Output Argument
The maple function optionally returns two output arguments, result and
status. If the maple call succeeds, Maple returns the result in the result
argument and zero in the status argument. If the call fails, Maple returns an
error code (a positive integer) in the status argument and a corresponding
warning/error message in the result argument.
For example, the Maple discrim function calculates the discriminant of a
polynomial and has the syntax discrim(p,x), where p is a polynomial in x.
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Tutorial
Suppose we forget to supply the second argument when calling the discrim
function
syms a b c x
[result, status] = maple('discrim', a*x^2+b*x+c)
Maple returns
result =
Error, (in discrim) invalid arguments
status =
2
If we then include x
[result, status] = maple('discrim', a*x^2+b*x+c, x)
we get the following
result =
–4*a*c+b^2
status =
0
1-142
Extended Symbolic Math Toolbox
Extended Symbolic Math Toolbox
The Extended Symbolic Math Toolbox allows you to access all nongraphics
Maple packages, Maple programming features, and Maple procedures. The
Extended Toolbox thus provides access to a large body of mathematical
software written in the Maple language.
Maple programming features include looping (for ... do ... od, while ...
do ... od) and conditionals (if ... elif ... else ... fi). Please see The
Maple Handbook for information on how to use these and other features.
This section explains how to load Maple packages and how to use Maple
procedures. For additional information, please consult these references.
Char, B.W., K.O. Geddes, G.H. Gonnet, B.L. Leong, M.B. Monagan, and S.M.
Watt, First Leaves: A Tutorial Introduction to Maple V, Springer-Verlag, NY,
1991.
Char, B.W., K.O. Geddes, G.H. Gonnet, B.L. Leong, M.B. Monagan, and S.M.
Watt, Maple V Language Reference Manual, Springer-Verlag, NY, 1991.
Char, B.W., K.O. Geddes, G.H. Gonnet, B.L. Leong, M.B. Monagan, and S.M.
Watt, Maple V Library Reference Manual, Springer-Verlag, NY, 1991.
Heck, A., Introduction to Maple, Springer-Verlag, NY, 1996.
Nicolaides, R. and N. Walkington, Maple: A Comprehensive Introduction,
Cambridge University Press, Cambridge, 1996.
Packages of Library Functions
Specialized libraries, or “packages,” can be used through the Extended Toolbox.
These packages include:
• Combinatorial Functions
• Differential Equation Tools
• Differential Forms
• Domains of Computation
• Euclidean Geometry
• Gaussian Integers
• Gröbner Bases
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• Permutation and Finitely Presented Groups
• Lie Symmetries
• Boolean Logic
• Graph Networks
• Newman-Penrose Formalism
• Number Theory
• Numerical Approximation
• Orthogonal Polynomials
• p-adic Numbers
• Formal Power Series
• Projective Geometry
• Simplex Linear Optimization
• Statistics
• Total Orders on Names
• Galois Fields
• Linear Recurrence Relation Tools
• Financial Mathematics
• Rational Generating Functions
• Tensor Computations
You can use the Maple with command to load these packages. Say, for example,
that you want to use the orthogonal polynomials package. First get the Maple
name of this package, using the statement
mhelp index[packages]
which returns
HELP FOR: Index of descriptions for packages of library functions
SYNOPSIS:
- The following packages are available:
...
orthopoly:
orthogonal polynomials
...
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Extended Symbolic Math Toolbox
You can then can access information about the package
mhelp orthopoly
To load the package, type
maple('with(orthopoly);')
This returns
ans =
[G, H, L, P, T, U]
which is a listing of function names in the orthopoly package. These functions
are now loaded in the Maple workspace, and you can use them as you would
any regular Maple function.
Procedure Example
The following example shows how you can access a Maple procedure through
the Extended Symbolic Math Toolbox. The example computes either symbolic
or variable-precision numeric approximations to π, using a method derived by
1-145
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Tutorial
Richard Brent based from the arithmetic-geometric mean algorithm of Gauss.
Here is the Maple source code.
pie
#
#
#
#
#
:= proc(n)
pie(n) takes n steps of an arithmetic–geometric mean
algorithm for computing pi. The result is a symbolic
expression whose length roughly doubles with each step.
The number of correct digits in the evaluated string also
roughly doubles with each step.
# Example: pie(5) is a symbolic expression with 1167
# characters which, when evaluated, agrees with pi to 84
# decimal digits.
local a,b,c,d,k,t;
a
b
c
t
:=
:=
:=
:=
1:
sqrt(1/2):
1/4:
1:
for k from 1 to n do
d := (b–a)/2:
b := sqrt(a*b):
a := a+d:
c := c–t*d^2:
t := 2*t:
od;
(a+b)^2/(4*c):
end;
Assume the source code for this Maple procedure is stored in the file pie.src.
Using the Extended Symbolic Math Toolbox, the MATLAB statement
procread('pie.src')
reads the specified file, deletes comments and newline characters, and sends
the resulting string to Maple. (The MATLAB ans variable then contains a
string representation of the pie.src file.)
1-146
Extended Symbolic Math Toolbox
You can use the pie function, using the maple function. The statement
p = maple('pie',5)
returns a symbolic object p that begins and ends with
p =
1/4*(1/32+1/64*2^(1/2)+1/32*2^(3/4)+ ...
... *2^(1/2))*2^(3/4))^(1/2))^(1/2))^2)
It is interesting to change the computation from symbolic to numeric. The
assignment to the variable b in the second executable line is key. If the
assignment statement is simply
b := sqrt(1/2)
the entire computation is done symbolically. But if the assignment statement
is modified to include decimal points
b := sqrt(1./2.)
the entire computation uses variable-precision arithmetic at the current
setting of digits. If this change is made, then
digits(100)
procread('pie.src')
p = maple('pie',5)
produces a 100-digit result
p =
3.14159265358979323 ... 5628703211672038
The last 16 digits differ from those of π because, with five iterations, the
algorithm gives only 84 digits.
Note that you can define your own MATLAB M-file that accesses a Maple
procedure
function p = pie1(n)
p = maple('pie',n)
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Precompiled Maple Procedures
When Maple loads a source (ASCII text) procedure into its workspace, it
compiles (translates) the procedure into an internal format. You can
subsequently use the maple function to save the procedures in the internal
format. The advantage is you avoid recompiling the procedure the next time
you load it, thereby speeding up the process.
For example, you can convert the pie.src procedure developed in the
preceding example to a precompiled Maple procedure, using the commands
clear maplemex
procread('pie.src')
maple('save(`pi.m`)');
The clear maplemex command resets the Maple workspace to its initial state.
Since the Maple save command saves all variables in the current session, we
want to remove extraneous variables. Note that you must use back quotes
around the function name.
To read the precompiled procedure into a subsequent MATLAB session, type
maple('read','`pie.m`');
Again, as with the ASCII text form, you can access the function using maple
p = maple('pie',5)
Note that precompiled Maple procedures have .m extensions. Hence, you must
take care to avoid confusing them with MATLAB M-files, which also have .m
extensions.
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Reference
2
Reference
This chapter provides detailed descriptions of all Symbolic Math Toolbox
functions. It begins with tables of these functions and continues with the
reference entries in alphabetical order.
Calculus
diff
Differentiate.
int
Integrate.
jacobian
Jacobian matrix.
limit
Limit of an expression.
symsum
Summation of series.
taylor
Taylor series expansion.
Linear Algebra
2-2
colspace
Basis for column space.
det
Determinant.
diag
Create or extract diagonals.
eig
Eigenvalues and eigenvectors.
expm
Matrix exponential.
inv
Matrix inverse.
jordan
Jordan canonical form.
null
Basis for null space.
poly
Characteristic polynomial.
rank
Matrix rank.
rref
Reduced row echelon form.
Linear Algebra
svd
Singular value decomposition.
tril
Lower triangle.
triu
Upper triangle.
Simplification
collect
Collect common terms.
expand
Expand polynomials and elementary functions.
factor
Factor.
horner
Nested polynomial representation.
numden
Numerator and denominator.
simple
Search for shortest form.
simplify
Simplification.
subexpr
Rewrite in terms of subexpressions.
Solution of Equations
compose
Functional composition.
dsolve
Solution of differential equations.
finverse
Functional inverse.
solve
Solution of algebraic equations.
2-3
2
Reference
Variable Precision Arithmetic
digits
Set variable precision accuracy.
vpa
Variable precision arithmetic.
Arithmetic Operations
2-4
+
Addition.
–
Subtraction.
*
Multiplication.
.*
Array multiplication.
/
Right division.
./
Array right division.
\
Left division.
.\
Array left division.
^
Matrix or scalar raised to a power.
.^
Array raised to a power.
'
Complex conjugate transpose.
.'
Real transpose.
Special Functions
cosint
Cosine integral, Ci(x).
hypergeom
Generalized hypergeometric function.
lambertw
Solution of λ ( x )e
sinint
Sine integral, Si(x).
zeta
Riemann zeta function.
λ(x)
= x.
Access To Maple
maple
Access Maple kernel.
mapleinit
Initialize Maple.
mfun
Numeric evaluation of Maple functions.
mhelp
Maple help.
mfunlist
List of functions for mfun.
procread
Install a Maple procedure.
Pedagogical and Graphical Applications
ezcontour
Contour plotter.
ezcontourf
Filled contour plotter.
ezmesh
Mesh plotter.
ezmeshc
Combined mesh and contour plotter.
2-5
2
Reference
Pedagogical and Graphical Applications
ezplot
Function plotter.
ezplot3
3-D curve plotter.
ezpolar
Polar coordinate plotter.
ezsurf
Surface plotter.
ezsurfc
Combined surface and contour plotter.
funtool
Function calculator.
rsums
Riemann sums.
taylortool
Taylor series calculator.
Conversions
char
Convert sym object to string.
double
Convert symbolic matrix to double.
poly2sym
Function calculator.
sym2poly
Symbolic polynomial to coefficient vector.
Basic Operations
2-6
ccode
C code representation of a symbolic expression.
conj
Complex conjugate.
findsym
Determine symbolic variables.
fortran
Fortran representation of a symbolic expression.
imag
Imaginary part of a complex number.
Basic Operations
latex
LaTeX representation of a symbolic expression.
pretty
Pretty print a symbolic expression.
real
Real part of an imaginary number.
sym
Create symbolic object.
syms
Shortcut for creating multiple symbolic objects.
Integral Transforms
fourier
Fourier transform.
ifourier
Inverse Fourier transform.
ilaplace
Inverse Laplace transform.
iztrans
Inverse z-transform.
laplace
Laplace transform.
ztrans
z-transform.
2-7
Arithmetic Operations
Purpose
Perform arithmetic operations on symbols.
Syntax
A+B
A–B
A*B A.*B
A\B A.\B
A/B A./B
A^B A.^B
A' A.'
Description
+
Matrix addition. A + B adds A and B. A and B must have the same
dimensions, unless one is scalar.
–
Subtraction. A – B subtracts B from A. A and B must have the same
dimensions, unless one is scalar.
*
Matrix multiplication. A*B is the linear algebraic product of A and B. The
number of columns of A must equal the number of rows of B, unless one
is a scalar.
.*
Array multiplication. A.*B is the entry-by-entry product of A and B. A
and B must have the same dimensions, unless one is scalar.
\
Matrix left division. X = A\B solves the symbolic linear equations A*X=B.
Note that A\B is roughly equivalent to inv(A)*B. Warning messages are
produced if X does not exist or is not unique. Rectangular matrices A are
allowed, but the equations must be consistent; a least squares solution
is not computed.
.\
Array left division. A.\B is the matrix with entries B(i,j)/A(i,j). A
and B must have the same dimensions, unless one is scalar.
/
Matrix right division. X=B/A solves the symbolic linear equation X*A=B.
Note that B/A is the same as (A.'\B.'). Warning messages are
produced if X does not exist or is not unique. Rectangular matrices A are
allowed, but the equations must be consistent; a least squares solution
is not computed.
./
Array right division. A./B is the matrix with entries A(i,j)/B(i,j). A
and B must have the same dimensions, unless one is scalar.
2
Arithmetic Operations
2-8
Arithmetic Operations
Examples
^
Matrix power. X^P raises the square matrix X to the integer power P. If
X is a scalar and P is a square matrix, X^P raises X to the matrix power
P, using eigenvalues and eigenvectors. X^P, where X and P are both
matrices, is an error.
.^
Array power. A.^B is the matrix with entries A(i,j)^B(i,j). A and B
must have the same dimensions, unless one is scalar.
'
Matrix Hermition transpose. If A is complex, A' is the complex
conjugate transpose.
.'
Array transpose. A.' is the real transpose of A. A.' does not conjugate
complex entries.
The following statements
syms a b c d;
A = [a b; c d];
A*A/A
A*A–A^2
return
[ a, b]
[ c, d]
[ 0, 0]
[ 0, 0]
The following statements
syms a11 a12 a21 a22 b1 b2;
A = [a11 a12; a21 a22];
B = [b1 b2];
X = B/A;
x1 = X(1)
x2 = X(2)
2-9
Arithmetic Operations
return
x1 =
(a21*b2–b1*a22)/(–a11*a22+a12*a21)
x2 =
(–a11*b2+a12*b1)/(–a11*a22+a12*a21)
See Also
2-10
null, solve
ccode
Purpose
ccode
C code representation of a symbolic expression.
Syntax
ccode(s)
Description
ccode(s) returns a fragment of C that evaluates the symbolic expression s.
Examples
The statements
syms x
f = taylor(log(1+x));
ccode(f)
return
t0 = x–x*x/2+x*x*x/3–pow(x,4.0)/4+pow(x,5.0)/5;
The statements
H = sym(hilb(3));
ccode(H)
return
H[0][0] = 1.0;
H[1][0] = 1.0/2.0;
H[2][0] = 1.0/3.0;
See Also
H[0][1] = 1.0/2.0;
H[1][1] = 1.0/3.0;
H[2][1] = 1.0/4.0;
H[0][2] = 1.0/3.0;
H[1][2] = 1.0/4.0;
H[2][2] = 1.0/5.0;
fortran, latex, pretty
2-11
collect
Purpose
collect
Collect coefficients.
Syntax
R = collect(S)
R = collect(S,v)
Description
For each polynomial in the array S of polynomials, collect(S) collects terms
containing the variable v (or x, if v is not specified). The result is an array
containing the collected polynomials.
Examples
The following statements
syms
R1 =
R2 =
R3 =
x y;
collect((exp(x)+x)*(x+2))
collect((x+y)*(x^2+y^2+1), y)
collect([(x+1)*(y+1),x+y])
return
R1 =
x^2+(exp(x)+2)*x+2*exp(x)
R2 =
y^3+x*y^2+(x^2+1)*y+x*(x^2+1)
R3 =
[(y+1)*x+y+1, x+y]
See Also
2-12
expand, factor, simple, simplify, syms
colspace
Purpose
colspace
Basis for column space.
Syntax
B = colspace(A)
Description
colspace(A) returns a matrix whose columns form a basis for the column
space of A. A is a symbolic or numeric matrix. Note that size(colspace(A),2)
returns the rank of A.
Examples
The statements
A = sym([2,0;3,4;0,5])
B = colspace(A)
return
A =
[2,0]
[3,4]
[0,5]
B =
[
1,
0]
[
0,
1]
[–15/8, 5/4]
See Also
null
orth in the online MATLAB Function Reference
2-13
compose
Purpose
compose
Functional composition.
Syntax
compose(f,g)
compose(f,g,z)
compose(f,g,x,z)
compose(f,g,x,y,z)
Description
compose(f,g) returns f(g(y)) where f = f(x) and g = g(y). Here x is the
symbolic variable of f as defined by findsym and y is the symbolic variable of g
as defined by findsym.
compose(f,g,z) returns f(g(z)) where f = f(x), g = g(y), and x and y are
the symbolic variables of f and g as defined by findsym.
compose(f,g,x,z) returns f(g(z)) and makes x the independent variable for
f. That is, if f = cos(x/t), then compose(f,g,x,z) returns cos(g(z)/t)
whereas compose(f,g,t,z) returns cos(x/g(z)).
compose(f,g,x,y,z) returns f(g(z)) and makes x the independent variable
for f and y the independent variable for g. For f = cos(x/t) and
g = sin(y/u), compose(f,g,x,y,z) returns cos(sin(z/u)/t) whereas
compose(f,g,x,u,z) returns cos(sin(y/z)/t).
Examples
Suppose
syms x y z t u;
f = 1/(1 + x^2); g = sin(y); h = x^t; p = exp(–y/u);
Then
compose(f,g)
->
compose(f,g,t) ->
compose(h,g,x,z)
compose(h,g,t,z)
compose(h,p,x,y,z)
compose(h,p,t,u,z)
See Also
2-14
finverse, subs, syms
1/(1+sin(x)^2)
1/(1+sin(t)^2)
-> sin(z)^t
-> x^sin(z)
-> exp(–z/u)^t
-> x^exp(–y/z)
conj
Purpose
conj
Symbolic conjugate.
Syntax
conj(X)
Description
conj(X) is the complex conjugate of X.
For a complex X, conj(X) = real(X) - i*imag(X).
See Also
real, imag
2-15
cosint
Purpose
cosint
Cosine integral function.
Syntax
Y = cosint(X)
Description
cosint(X) evaluates the cosine integral function at the elements of X, a
numeric matrix, or a symbolic matrix. The cosine integral function is defined
by:
x
cos t – 1
Ci ( x ) = γ + ln ( x ) + -------------------- dt
t
∫
0
where γ is Euler’s constant 0.577215664...
Examples
cosint(7.2) returns 0.0960.
cosint([0:0.1:1]) returns
Columns 1 through 7
Inf
–1.7279
–1.0422
–0.6492
–0.3788
Columns 8 through 11
0.1005
0.1983
The statements
syms x;
f = cosint(x);
diff(x)
return
cos(x)/x
See Also
2-16
sinint
0.2761
0.3374
–0.1778
–0.0223
det
Purpose
det
Matrix determinant.
Syntax
r = det(A)
Description
det(A) computes the determinant of A, where A is a symbolic or numeric
matrix. det(A) returns a symbolic expression, if A is symbolic; a numeric value,
if A is numeric.
Examples
The statements
syms a b c d;
det([a, b; c, d])
return
a*d – b*c
The statements
A = sym([2/3 1/3;1 1])
r = det(A)
return
A =
[ 2/3, 1/3]
[
1,
1]
r = 1/3
2-17
diag
Purpose
diag
Create or extract symbolic diagonals.
Syntax
diag(A,k)
diag(A)
Description
diag(A,k), where A is a row or column vector with n components, returns a
square symbolic matrix of order n+abs(k), with the elements of A on the k-th
diagonal. k = 0 signifies the main diagonal; k > 0, the k-th diagonal above the
main diagonal; k < 0, the k-th diagonal below the main diagonal.
diag(A,k), where A is a square symbolic matrix, returns a column vector
formed from the elements of the k-th diagonal of A.
diag(A), where A is a vector with n components, returns an n-by-n diagonal
matrix having A as its main diagonal.
diag(A), where A is a square symbolic matrix, returns the main diagonal of A.
Examples
Suppose
v = [a b c]
Then both diag(v) and diag(v,0) return
[ a, 0, 0 ]
[ 0, b, 0 ]
[ 0, 0, c ]
diag(v,–2) returns
[
[
[
[
[
0,
0,
a,
0,
0,
0,
0,
0,
b,
0,
0,
0,
0,
0,
c,
0,
0,
0,
0,
0,
0]
0]
0]
0]
0]
Suppose
A =
[ a, b, c ]
[ 1, 2, 3 ]
[ x, y, z ]
2-18
diag
diag(A) returns
[ a ]
[ 2 ]
[ z ]
diag(A,1) returns
[ b ]
[ 3 ]
See Also
tril, triu
2-19
diff
Purpose
diff
Differentiate.
Syntax
diff(S,'v')
diff(S,n)
diff(S,'v',n)
Description
diff(S) differentiates a symbolic expression S with respect to its free variable
as determined by findsym.
diff(S,'v') or diff(S,sym('v')) differentiates S with respect to v.
diff(S,n), for a positive integer n, differentiates S n times.
diff(S,'v',n) and diff(S,n,'v') are also acceptable.
Examples
Assume
syms x t
Then
diff(sin(x^2))
returns
2*cos(x^2)*x
and
diff(t^6,6)
returns
720
See also
2-20
int, jacobian, findsym
digits
Purpose
digits
Set variable precision accuracy.
Syntax
digits(d)
d = digits
digits
Description
digits specifies the number of significant decimal digits that Maple uses to do
variable precision arithmetic (VPA). The default value is 32 digits.
digits(d) sets the current VPA accuracy to d digits.
digits returns the current VPA accuracy.
Examples
If
z = 1.0e–16
x = 1.0e+2
digits(14)
then
y = vpa(x*z+1)
uses 14-digit decimal arithmetic and returns
y =
1.0000000000000
Whereas
digits(15)
y = vpa(x*z+1)
used 15-digit decimal arithmetic and returns
y =
1.00000000000001
See Also
double, vpa
2-21
double
Purpose
double
Convert symbolic matrix to MATLAB numeric form.
Syntax
R = double(S)
Description
double(S) converts the symbolic object S to a numeric object. If S is a symbolic
constant or constant expression, double returns a double-precision
floating-point number representing the value of S. If S is a symbolic matrix
whose entries are constants or constant expressions, double returns a matrix
of double precision floating-point numbers representing the values of S’s
entries.
Examples
double(sym('(1+sqrt(5))/2')) returns 1.6180.
The following statements
a = sym(2*sqrt(2));
b = sym((1–sqrt(3))^2);
T = [a, b]
double(T)
return
ans =
2.8284
See Also
2-22
sym, vpa
0.5359
dsolve
Purpose
dsolve
Symbolic solution of ordinary differential equations.
Syntax
r = dsolve('eq1,eq2,...', 'cond1,cond2,...', 'v')
r = dsolve('eq1','eq2',...,'cond1','cond2',...,'v')
Description
dsolve('eq1,eq2,...', 'cond1,cond2,...', 'v') symbolically solves the
ordinary differential equation(s) specified by eq1, eq2,... using v as the
independent variable and the boundary and/or initial condition(s) specified by
cond1,cond2,....
The default independent variable is t.
The letter D denotes differentiation with respect to the independent variable;
with the primary default, this is d/dx. A D followed by a digit denotes repeated
differentiation. For example, D2 is d2/dx2. Any character immediately
following a differentiation operator is a dependent variable. For example, D3y
denotes the third derivative of y(x) or y(t).
Initial/boundary conditions are specified with equations like y(a) = b or
Dy(a) = b, where y is a dependent variable and a and b are constants. If the
number of initial conditions specified is less than the number of dependent
variables, the resulting solutions will contain the arbitrary constants C1,
C2,....
You can also input each equation and/or initial condition as a separate
symbolic equation. dsolve accepts up to 12 input arguments.
With no output arguments, dsolve returns a list of solutions.
dsolve returns a warning message, if it cannot find an analytic solution for an
equation. In such a case, you can find a numeric solution, using MATLAB’s
ode23 or ode45 function.
Examples
dsolve('Dy = a*y') returns
exp(a*t)*C1
dsolve('Df = f + sin(t)') returns
–1/2*cos(t)–1/2*sin(t)+exp(t)*C1
2-23
dsolve
dsolve('(Dy)^2 + y^2 = 1','s') returns
–sin(–s+C1)
dsolve('Dy = a*y', 'y(0) = b') returns
exp(a*t)*b
dsolve('D2y = –a^2*y', 'y(0) = 1', 'Dy(pi/a) = 0') returns
cos(a*t)
dsolve('Dx = y', 'Dy = –x') returns
x=
cos(t)*C1+sin(t)*C2
y =
–sin(t)*C1+cos(t)*C2
Diagnostics
If dsolve cannot find an analytic solution for an equation, it prints the warning
Warning: explicit solution could not be found
and return an empty sym object.
See Also
2-24
syms
eig
Purpose
eig
Symbolic matrix eigenvalues and eigenvectors.
Syntax
lambda = eig(A)
[V,D] = eig(A)
[V,D,P] = eig(A)
lambda = eig(vpa(A))
[V,D] = eig(vpa(A))
Description
lambda=eig(A) returns a symbolic vector containing the eigenvalues of the
square symbolic matrix A.
[V,D] = eig(A) returns a matrix V whose columns are eigenvectors and a
diagonal matrix D containing eigenvalues. If the resulting V is the same size as
A, then A has a full set of linearly independent eigenvectors that satisfy
A*V = V*D.
[V,D,P]=eig(A) also returns P, a vector of indices whose length is the total
number of linearly independent eigenvectors, so that A*V = V*D(P,P).
lambda = eig(VPA(A)) and [V,D] = eig(VPA(A)) compute numeric
eigenvalues and eigenvectors, respectively, using variable precision
arithmetic. If A does not have a full set of eigenvectors, the columns of V will not
be linearly independent.
Examples
The statements
R = sym(gallery('rosser'));
eig(R)
return
ans =
[
0]
[
1020]
[ 510+100*26^(1/2)]
[ 510–100*26^(1/2)]
[
10*10405^(1/2)]
[ –10*10405^(1/2)]
[
1000]
[
1000]
2-25
eig
eig(vpa(R)) returns
ans =
[
–1020.0490184299968238463137913055]
[ .56512999999999999999999999999800e–28]
[ .98048640721516997177589097485157e–1]
[
1000.0000000000000000000000000002]
[
1000.0000000000000000000000000003]
[
1019.9019513592784830028224109024]
[
1020.0000000000000000000000000003]
[
1020.0490184299968238463137913055]
The statements
A = sym(gallery(5));
[v,lambda] = eig(A)
return
v =
[
0]
[ 21/256]
[ –71/128]
[ 973/256]
[
1]
lambda =
[
[
[
[
[
See Also
2-26
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0]
0]
0]
0]
0]
jordan, poly, svd, vpa
expm
Purpose
expm
Symbolic matrix exponential.
Syntax
expm(A)
Description
expm(A) is the matrix exponential of the symbolic matrix A.
Examples
The statements
syms t;
A = [0 1; –1 0];
expm(t*A)
return
[ cos(t),
[ –sin(t),
sin(t)]
cos(t)]
2-27
expand
Purpose
expand
Symbolic expansion.
Syntax
R = expand(S)
Description
expand(S) writes each element of a symbolic expression S as a product of its
factors. expand is most often used only with polynomials, but also expands
trigonometric, exponential, and logarithmic functions.
Examples
expand((x–2)*(x–4)) returns
x^2–6*x+8
expand(cos(x+y)) returns
cos(x)*cos(y)–sin(x)*sin(y)
expand(exp((a+b)^2)) returns
exp(a^2)*exp(a*b)^2*exp(b^2)
expand(log(a*b/sqrt(c))) returns
log(a)+log(b)–1/2*log(c)
expand([sin(2*t), cos(2*t)]) returns
[2*sin(t)*cos(t), 2*cos(t)^2–1]
See Also
2-28
collect, factor, horner, simple, simplify, syms
ezcontour
Purpose
ezcontour
Contour plotter.
Syntax
ezcontour(f)
ezcontour(f,domain)
ezcontour(...,n)
Description
ezcontour(f) plots the contour lines of f(x,y), where f is a symbolic expression
that represents a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain -2π < x < 2π, -2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezcontour(f,domain) plots f(x,y) over the specified domain. domain can be
either a 4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max]
(where, min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezcontour(u^2 − v^3,[0,1],[3,6]) plots the contour lines for u2 − v3 over
0 < u < 1, 3 < v < 6.
ezcontour(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezcontour automatically adds a title and axis labels.
Examples
The following mathematical expression defines a function of two variables, x
and y:
f ( x, y ) = 3 ( 1 – x ) 2 e – x
2
– ( y + 1 )2
1
x
2
2
2
2
– 10  --- – x 3 – y 5 e – x – y – --- e – ( x + 1 ) – y
5

3
ezcontour requires a sym argument that expresses this function using
MATLAB syntax to represent exponents, natural logs, etc. This function is
represented by the symbolic expression:
syms x y
f = 3*(1−x)^2*exp(−(x^2)−(y+1)^2) ...
− 10*(x/5 − x^3 − y^5)*exp(−x^2−y^2) ...
− 1/3*exp(−(x+1)^2 − y^2);
2-29
ezcontour
For convenience, this expression is written on three lines.
Pass the sym f to ezcontour along with a domain ranging from -3 to 3 and
specify a computational grid of 49-by-49:
ezcontour(f,[-3,3],49)
3 (1−x)2 exp(−(x2) − (y+1)2)− ~~~ x2−y2)− 1/3 exp(−(x+1)2 − y2)
3
2
y
1
0
−1
−2
−3
−3
−2
−1
0
x
1
2
3
In this particular case, the title is too long to fit at the top of the graph so
MATLAB abbreviates the string.
See Also
2-30
contour, ezcontourf, ezmesh, ezmeshc, ezplot, ezplot3, ezpolar, ezsurf,
ezsurfc
ezcontourf
Purpose
ezcontourf
Filled contour plotter.
Syntax
ezcontourf(f)
ezcontourf(f,domain)
ezcontourf(...,n)
Description
ezcontour(f) plots the contour lines of f(x,y), where f is a sym that represents
a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain -2π < x < 2π, -2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezcontour(f,domain) plots f(x,y) over the specified domain. domain can be
either a 4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max]
(where, min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezcontourf(u^2 − v^3,[0,1],[3,6]) plots the contour lines for u2 − v3 over
0 < u < 1, 3 < v < 6.
ezcontourf(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezcontourf automatically adds a title and axis labels.
Examples
The following mathematical expression defines a function of two variables, x
and y:
f ( x, y ) = 3 ( 1 – x ) 2 e – x
2
– ( y + 1 )2
1
x
2
2
2
2
– 10  --- – x 3 – y 5 e – x – y – --- e – ( x + 1 ) – y
5

3
ezcontourf requires a sym argument that expresses this function using
MATLAB syntax to represent exponents, natural logs, etc. This function is
represented by the symbolic expression:
syms x y
f = 3*(1−x)^2*exp(−(x^2)−(y+1)^2) ...
− 10*(x/5 − x^3 − y^5)*exp(−x^2−y^2) ...
− 1/3*exp(−(x+1)^2 − y^2);
2-31
ezcontourf
For convenience, this expression is written on three lines.
Pass the sym f to ezcontourf along with a domain ranging from −3 to 3 and
specify a grid of 49-by-49:
ezcontourf(f,[−3,3],49)
2
2
2
2
2
2
2
3 (1−x) exp(−(x ) − (y+1) )− ~~~ x −y )− 1/3 exp(−(x+1) − y )
3
2
y
1
0
−1
−2
−3
−3
−2
−1
0
x
1
2
3
In this particular case, the title is too long to fit at the top of the graph so
MATLAB abbreviates the string.
See Also
2-32
contourf, ezcontour, ezmesh, ezmeshc, ezplot, ezplot3, ezpolar, ezsurf,
ezsurfc
ezmesh
Purpose
2ezmesh
3-D mesh plotter.
Syntax
ezmesh(f)
ezmesh(f,domain)
ezmesh(x,y,z)
ezmesh(x,y,z,[smin,smax,tmin,tmax]) or ezmesh(x,y,z,[min,max])
ezmesh(...,n)
ezmesh(...,'circ')
Description
ezmesh(f) creates a graph of f(x,y), where f is a symbolic expression that
represents a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain −2π < x < 2π, −2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezmesh(f,domain) plots f over the specified domain. domain can be either a
4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max] (where,
min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezmesh(u^2 − v^3,[0,1],[3,6]) plots u2 − v3 over 0 < u < 1, 3 < v < 6.
ezmesh(x,y,z) plots the parametric surface x = x(s,t), y = y(s,t), and z = z(s,t)
over the square: −2π < s < 2π, −2π < t < 2π.
ezmesh(x,y,z,[smin,smax,tmin,tmax]) or ezmesh(x,y,z,[min,max]) plots
the parametric surface using the specified domain.
ezmesh(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezmesh(...,'circ') plots f over a disk centered on the domain
Remarks
rotate3d is always on. To rotate the graph, click and drag with the mouse.
Examples
This example visualizes the function,
f ( x, y ) = xe
2
–x – y
2
2-33
ezmesh
with a mesh plot drawn on a 40-by-40 grid. The mesh lines are set to a uniform
blue color by setting the colormap to a single color:
syms x y
ezmesh(x*exp(−x^2−y^2),40)
colormap [0 0 1]
x exp(−x2 − y2)
0.5
0
−0.5
2
1
2
0
0
−1
y
See Also
2-34
−2
−2
x
ezcontour, ezcontourf, ezmeshc, ezplot, ezplot3, ezpolar, ezsurf, ezsurfc,
mesh
ezmeshc
Purpose
2ezmeshc
Combined mesh/contour plotter.
Syntax
ezmeshc(f)
ezmeshc(f,domain)
ezmeshc(x,y,z)
ezmeshc(x,y,z,[smin,smax,tmin,tmax]) or ezmeshc(x,y,z,[min,max])
ezmeshc(...,n)
ezmeshc(...,'circ')
Description
ezmeshc(f) creates a graph of f(x,y), where f is a symbolic expression that
represents a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain −2π < x < 2π, −2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezmeshc(f,domain) plots f over the specified domain. domain can be either a
4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max] (where,
min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezmeshc(u^2 − v^3,[0,1],[3,6]) plots u2 − v3 over 0 < u < 1, 3 < v < 6.
ezmeshc(x,y,z) plots the parametric surface x = x(s,t), y = y(s,t), and z = z(s,t)
over the square: −2π < s < 2π, −2π < t < 2π.
ezmeshc(x,y,z,[smin,smax,tmin,tmax]) or ezmeshc(x,y,z,[min,max])
plots the parametric surface using the specified domain.
ezmeshc(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezmeshc(...,'circ') plots f over a disk centered on the domain
Remarks
rotate3d is always on. To rotate the graph, click and drag with the mouse.
Examples
Create a mesh/contour graph of the expression,
2-35
ezmeshc
y
f ( x, y ) = --------------------------2
2
1+x +y
over the domain −5 < x < 5, −2*pi < y < 2*pi:
syms x y
ezmeshc(y/(1 + x^2 + y^2),[−5,5,−2*pi,2*pi])
Use the mouse to rotate the axes to better observe the contour lines (this
picture uses a view of azimuth = −65.5 and elevation = 26).
y/(1 + x2 + y2)
0.5
0
5
−0.5
0
5
0
−5
y
See Also
2-36
−5
x
ezcontour, ezcontourf, ezmesh, ezplot, ezplot3, ezpolar, ezsurf, ezsurfc,
meshc
ezplot
Purpose
2ezplot
Function plotter.
Syntax
ezplot(f)
ezplot(f,[min,max])
ezplot(f,[xmin,xmax,ymin,ymax])
ezplot(x,y)
ezplot(x,y,[tmin,tmax])
ezplot(...,figure)
Description
ezplot(f) plots the expression f = f(x) over the default domain −2π < x < 2π.
ezplot(f,[xmin xmax]) plots f = f(x) over the specified domain. It opens and
displays the result in a window labeled Figure No. 1. If any plot windows are
already open, ezplot displays the result in the highest numbered window.
ezplot(f,[xmin xmax],fign) opens (if necessary) and displays the plot in the
window labeled fign.
For implicitly defined functions, f = f(x,y):
ezplot(f) plots f(x,y) = 0 over the default domain −2π < x < 2π, −2π < y < 2π.
ezplot(f,[xmin,xmax,ymin,ymax]) plots f(x,y) = 0 over xmin < x < xmax and
ymin < y < ymax.
ezplot(f,[min,max])plots f(x,y) = 0 over min < x < max and min < y < max.
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezplot(u^2 − v^2 − 1,[−3,2,−2,3]) plots u2 − v2 − 1 = 0 over −3 < u < 2,
−2 < v < 3.
ezplot(x,y) plots the parametrically defined planar curve x = x(t) and y = y(t)
over the default domain 0 < t < 2π.
ezplot(x,y,[tmin,tmax]) plots x = x(t) and y = y(t) over tmin < t < tmax.
ezplot(...,figure) plots the given function over the specified domain in the
Figure window identified by the handle figure.
Algorithm
If you do not specify a plot range, ezplot samples the function between –2*pi
and 2*pi and selects a subinterval where the variation is significant as the plot
2-37
ezplot
domain. For the range, ezplot omits extreme values associated with
singularities.
Examples
This example plots the implicitly defined function,
x2 − y4 = 0
over the domain [−2π, 2π]:
syms x y
ezplot(x^2−y^4)
x2−y4 = 0
6
4
y
2
0
−2
−4
−6
−6
2-38
−4
−2
0
x
2
4
6
ezplot
The following statements
syms x
ezplot(erf(x))
grid
plot a graph of the error function:
erf(x)
1
0.5
0
0.5
-1
-2
See Also
-1.5
-1
-0.5
0
x
0.5
1
1.5
2
ezcontour, ezcontourf, ezmesh, ezmeshc, ezplot3, ezpolar, ezsurf, ezsurfc,
plot
2-39
ezplot3
Purpose
2ezplot3
3-D parametric curve plotter.
Syntax
ezplot3(x,y,z)
ezplot3(x,y,z,[tmin,tmax])
ezplot3(...,'animate')
Description
ezplot3(x,y,z) plots the spatial curve x = x(t), y = y(t), and z = z(t) over the
default domain 0 < t < 2π.
ezplot3(x,y,z,[tmin,tmax]) plots the curve x = x(t), y = y(t), and z = z(t) over
the domain tmin < t < tmax.
ezplot3(...,'animate') produces an animated trace of the spatial curve.
Examples
This example plots the parametric curve,
over the domain [0,6π]:
syms t; ezplot3(sin(t), cos(t), t,[0,6*pi])
x = sin(t), y = cos(t), z = t
20
z
15
10
5
0
1
1
0.5
0.5
0
0
−0.5
y
2-40
−0.5
−1
−1
x
ezplot3
See Also
ezcontour, ezcontourf, ezmesh, ezmeshc, ezplot, ezpolar, ezsurf, ezsurfc,
plot3
2-41
ezpolar
Purpose
2ezpolar
Polar coordinate plotter.
Syntax
ezpolar(f)
ezpolar(f,[a,b])
Description
ezpolar(f) plots the polar curve rho = f(theta) over the default domain
0 < theta < 2π.
ezpolar(f,[a,b]) plots f for a < theta < b.
Example
This example creates a polar plot of the function,
1 + cos(t)
over the domain [0, 2π]:
syms t
ezpolar(1+cos(t))
90
2
120
60
1.5
150
30
1
0.5
180
0
330
210
240
300
270
r = 1+cos(t)
2-42
ezsurf
Purpose
2ezsurf
3-D colored surface plotter.
Syntax
ezsurf(f)
ezsurf(f,domain)
ezsurf(x,y,z)
ezsurf(x,y,z,[smin,smax,tmin,tmax]) or ezsurf(x,y,z,[min,max])
ezsurf(...,n)
ezsurf(...,'circ')
Purpose
ezsurf(f) creates a graph of f(x,y), where f is a symbolic expression that
represents a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain −2π < x < 2π, −2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezsurf(f,domain) plots f over the specified domain. domain can be either a
4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max] (where,
min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezsurf(u^2 − v^3,[0,1],[3,6]) plots u2 − v3 over 0 < u < 1, 3 < v < 6.
ezsurf(x,y,z) plots the parametric surface x = x(s,t), y = y(s,t), and z = z(s,t)
over the square: −2π < s < 2π, −2π < t < 2π.
ezsurf(x,y,z,[smin,smax,tmin,tmax]) or ezsurf(x,y,z,[min,max]) plots
the parametric surface using the specified domain.
ezsurf(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezsurf(...,'circ') plots f over a disk centered on the domain
Remarks
rotate3d is always on. To rotate the graph, click and drag with the mouse.
Examples
ezsurf does not graph points where the mathematical function is not defined
(these data points are set to NaNs, which MATLAB does not plot). This example
2-43
ezsurf
illustrates this filtering of singularities/discontinuous points by graphing the
function,
f(x, y) = real ( atan ( x + iy ) )
over the default domain −2π < x < 2π, −2π < y < 2π:
syms x y
ezsurf(real(atan(x+i*y)))
real(atan(x+i y))
2
1
0
−1
−2
5
5
0
y
0
−5
−5
x
Note also that ezsurf creates graphs that have axis labels, a title, and extend
to the axis limits.
See Also
2-44
ezcontour, ezcontourf, ezmesh, ezmeshc, ezplot, ezpolar, ezsurfc, surf
ezsurfc
Purpose
2ezsurfc
Combined surface/contour plotter.
Syntax
ezsurfc(f)
ezsurfc(f,domain)
ezsurfc(x,y,z)
ezsurfc(x,y,z,[smin,smax,tmin,tmax]) or ezsurfc(x,y,z,[min,max])
ezsurfc(...,n)
ezsurfc(...,'circ')
Description
ezsurfc(f) creates a graph of f(x,y), where f is a symbolic expression that
represents a mathematical function of two variables, such as x and y.
The function f is plotted over the default domain −2π < x < 2π, −2π < y < 2π.
MATLAB chooses the computational grid according to the amount of variation
that occurs; if the function f is not defined (singular) for points on the grid, then
these points are not plotted.
ezsurfc(f,domain) plots f over the specified domain. domain can be either a
4-by-1 vector [xmin, xmax, ymin, ymax] or a 2-by-1 vector [min, max] (where,
min < x < max, min < y < max).
If f is a function of the variables u and v (rather than x and y), then the domain
endpoints umin, umax, vmin, and vmax are sorted alphabetically. Thus,
ezsurfc(u^2 − v^3,[0,1],[3,6]) plots u2 − v3 over 0 < u < 1, 3 < v < 6.
ezsurfc(x,y,z) plots the parametric surface x = x(s,t), y = y(s,t), and z = z(s,t)
over the square: −2π < s < 2π, −2π < t < 2π.
ezsurfc(x,y,z,[smin,smax,tmin,tmax]) or ezsurfc(x,y,z,[min,max])
plots the parametric surface using the specified domain.
ezsurfc(...,n) plots f over the default domain using an n-by-n grid. The
default value for n is 60.
ezsurfc(...,'circ') plots f over a disk centered on the domain.
Remarks
rotate3d is always on. To rotate the graph, click and drag with the mouse.
2-45
ezsurfc
Examples
Create a surface/contour plot of the expression,
y
f ( x, y ) = --------------------------2
2
1+x +y
over the domain −5 < x < 5, −2*pi < y < 2*pi, with a computational grid of size
35-by-35:
syms x y
ezsurfc(y/(1 + x^2 + y^2),[−5,5,−2*pi,2*pi],35)
Use the mouse to rotate the axes to better observe the contour lines (this
picture uses a view of azimuth = −65.5 and elevation = 26)
y/(1 + x2 + y2)
0.5
0
5
−0.5
0
5
0
−5
y
See Also
2-46
−5
x
ezcontour, ezcontourf, ezmesh, ezmeshc, ezplot, ezpolar, ezsurf, surfc
factor
Purpose
factor
Factorization.
Syntax
factor(X)
Description
factor can take a positive integer, an array of symbolic expressions, or an
array of symbolic integers as an argument. If N is a positive integer, factor(N)
returns the prime factorization of N.
If S is a matrix of polynomials or integers, factor(S) factors each element. If
any element of an integer array has more than 16 digits, you must use sym to
create that element, for example, sym('N').
Examples
factor(x^3–y^3) returns
(x–y)*(x^2+x*y+y^2)
factor([a^2–b^2, a^3+b^3]) returns
[(a–b)*(a+b), (a+b)*(a^2–a*b+b^2)]
factor(sym('12345678901234567890')) returns
(2)*(3)^2*(5)*(101)*(3803)*(3607)*(27961)*(3541)
See Also
collect, expand, horner, simplify, simple
2-47
findsym
Purpose
findsym
Finds the variables in a symbolic expression or matrix.
Syntax
r = findsym(S)
r = findsym(S,n)
Description
findsym(S) returns all symbolic variables in S in alphabetical order, separated
by commas. If S does not contain any variables, findsym returns an empty
string.
findsym(S,n) returns the n variables alphabetically closest to x.
Note A symbolic variable is an alphanumeric name, other than i or j, that
begins with an alphabetic character.
Examples
syms a x y z t
findsym(sin(pi*t)) returns pi, t.
findsym(x+i*y–j*z) returns x, y, z.
findsym(a+y,1) returns y.
See Also
2-48
compose, diff, int, limit, taylor
finverse
Purpose
finverse
Functional inverse.
Syntax
g = finverse(f)
g = finverse(f,u)
Description
g = finverse(f) returns the functional inverse of f. f is a scalar sym
representing a function of one symbolic variable, say x. Then g is a scalar sym
that satisfies g(f(x)) = x. That is, finverse(f) returns f–1, provided f–1
exists.
g = finverse(f,v) uses the symbolic variable v, where v is a sym, as the
independent variable. Then g is a scalar sym that satisfies g(f(v)) = v. Use
this form when f contains more than one symbolic variable.
Examples
finverse(1/tan(x)) returns
atan(1/x)
finverse(exp(u–2*v),u) returns
2*v+log(u)
See Also
compose, syms
2-49
fortran
Purpose
fortran
Fortran representation of a symbolic expression.
Syntax
fortran(S)
Description
fortran(S)returns the Fortran code equivalent to the expression S.
Examples
The statements
syms x
f = taylor(log(1+x));
fortran(f)
return
t0 = x–x**2/2+x**3/3–x**4/4+x**5/5
The statements
H = sym(hilb(3));
fortran(H)
return
H(1,1) = 1
H(2,1) = 1.E0/2.E0
H(3,1) = 1.E0/3.E0
See Also
2-50
ccode, latex, pretty
H(1,2) = 1.E0/2.E0
H(2,2) = 1.E0/3.E0
H(3,2) = 1.E0/4.E0
H(1,3) = 1.E0/3.E0
H(2,3) = 1.E0/4.E0
H(3,3) = 1.E0/5.E0
fourier
Purpose
fourier
Fourier integral transform.
Syntax
F = fourier(f)
F = fourier(f,v)
F = fourier(f,u,v)
Description
F = fourier(f) is the Fourier transform of the symbolic scalar f with default
independent variable x. The default return is a function of w. The Fourier
transform is applied to a function of x and returns a function of w.
f = f( x) ⇒ F = F( w )
If f = f(w), fourier returns a function of t.
F = F(t)
By definition
∞
F(w) =
∫ f ( x )e
– iwx
dx
–∞
where x is the symbolic variable in f as determined by findsym.
F = fourier(f,v) makes F a function of the symbol v instead of the default w.
∞
F(v) =
∫ f ( x )e
– ivx
dx
–∞
F = fourier(f,u,v) makes f a function of u and F a function of v instead of
the default variables x and w, respectively.
∞
F(v) =
∫ f ( u )e
– ivu
du
–∞
2-51
fourier
Examples
Fourier Transform
f( x ) = e
–x
MATLAB Command
f = exp(–x^2)
2
∞
∫ f ( x )e
F[ f]( w ) =
– ixw
dx
–∞
=
πe
returns
–w ⁄ 4
g( w ) = e
fourier(f)
2
pi^(1/2)*exp(–1/4*w^2)
g = exp(–abs(w))
–w
∞
F[ g]( t) =
∫ g ( w )e
–itw
dw
–∞
returns
2
= --------------2
1+t
f ( x ) = xe
fourier(g)
2/(1+t^2)
f = x*exp(–abs(x))
–x
∞
F[ f]( u ) =
∫ f ( x )e
–∞
4i
= – -------------------------2 2u
(1 + u )
2-52
– ixu
dx
fourier(f,u)
returns
–4*i/(1+u^2)^2*u
fourier
Fourier Transform
f ( x, v ) = e
2
–x v
MATLAB Command
syms x real
f = exp(–x^2*abs(v))*sin(v)/v
sin v
------------, x real
v
∞
F[ f( v ) ]( u ) =
∫ f ( x, v )e
– ivu
–∞
u+1
u–1
- + atan -----------= – atan -----------2
2
x
x
See Also
dv
fourier(f,v,u)
returns
–atan((u–1)/x^2)+atan((u+1)/x^2)
ifourier, laplace, ztrans
2-53
funtool
Purpose
funtool
Function calculator.
Syntax
funtool
Description
funtool is a visual function calculator that manipulates and displays functions
of one variable. At the click of a button, for example, funtool draws a graph
representing the sum, product, difference, or ratio of two functions that you
specify. funtool includes a function memory that allows you to store functions
for later retrieval.
At startup, funtool displays graphs of a pair of functions, f(x) = x and
g(x) = 1. The graphs plot the functions over the domain [–2*pi, 2*pi].
funtool also displays a control panel that lets you save, retrieve, redefine,
combine, and transform f and g.
Text Fields. The top of the control panel contains a group of editable text fields.
2-54
f=
Displays a symbolic expression representing f. Edit this field to
redefine f.
g=
Displays a symbolic expression representing g. Edit this field to
redefine g.
x=
Displays the domain used to plot f and g. Edit this field to
specify a different domain.
funtool
a=
Displays a constant factor used to modify f (see button
descriptions in the next section). Edit this field to change the
value of the constant factor.
funtool redraws f and g to reflect any changes you make to the contents of the
control panel’s text fields.
Control Buttons. The bottom part of the control panel contains an array of
buttons that transform f and perform other operations.
The first row of control buttons replaces f with various transformations of f.
df/dx
Derivative of f
int f
Integral of f
simple f
Simplified form of f, if possible
num f
Numerator of f
den f
Denominator of f
1/f
Reciprocal of f
finv
Inverse of f
The operators intf and finv may fail if the corresponding symbolic expressions
do not exist in closed form.
The second row of buttons translates and scales f and the domain of f by a
constant factor. To specify the factor, enter its value in the field labeled a= on
the calculator control panel. The operations are
f+a
Replaces f(x) by f(x) + a.
f–a
Replaces f(x) by f(x) – a.
f*a
Replaces f(x) by f(x) * a.
f/a
Replaces f(x) by f(x) / a.
f^a
Replaces f(x) by f(x) ^ a.
f(x+a)
Replaces f(x) by f(x + a ).
f(x*a)
Replaces f(x) by f(x * a).
2-55
funtool
The first four buttons of the third row replace f with a combination of f and g.
f+g
Replaces f(x) by f(x) + g(x).
f–g
Replaces f(x) by f(x)–g(x).
f*g
Replaces f(x) by f(x) * g(x).
f/g
Replaces f(x) by f(x) / g(x).
The remaining buttons on the third row interchange f and g.
g=f
Replaces g with f.
swap
Replaces f with g and g with f.
The first three buttons in the fourth row allow you to store and retrieve
functions from the calculator’s function memory.
Insert
Adds f to the end of the list of stored functions.
Cycle
Replaces f with the next item on the function list.
Delete
Deletes f from the list of stored functions.
The other four buttons on the fourth row perform miscellaneous functions:
See Also
2-56
Reset
Resets the calculator to its initial state.
Help
Displays the online help for the calculator.
Demo
Runs a short demo of the calculator.
Close
Closes the calculator’s windows.
ezplot, syms
horner
Purpose
horner
Horner polynomial representation.
Syntax
R = horner(P)
Description
Suppose P is a matrix of symbolic polynomials. horner(P) transforms each
element of P into its Horner, or nested, representation.
Examples
horner(x^3–6*x^2+11*x–6) returns
–6+(11+(–6+x)*x)*x
horner([x^2+x;y^3–2*y]) returns
[
(1+x)∗x]
[(–2+y^2)*y]
See Also
expand, factor, simple, simplify, syms
2-57
hypergeom
Purpose
hypergeom
Generalized hypergeometric function.
Syntax
hypergeom(n, d, z)
Description
hypergeom(n, d, z) is the generalized hypergeometric function F(n, d, z), also
known as the Barnes extended hypergeometric function and denoted by jFk
where j = length(n) and k = length(d). For scalar a, b, and c,
hypergeom([a,b],c,z) is the Gauss hypergeometric function 2F1(a,b;c;z).
The definition by a formal power series is
∞
F ( n, d, z ) =
∑
k=0
C n, k z k
------------ ⋅ ----C d, k k!
where
v
C
v, k
=
Γ ( vj + k )
∏ ---------------------Γ( vj )
j=1
Either of the first two arguments may be a vector providing the coefficient
parameters for a single function evaluation. If the third argument is a vector,
the function is evaluated pointwise. The result is numeric if all the arguments
are numeric and symbolic if any of the arguments is symbolic.
See Abramowitz and Stegun, Handbook of Mathematical Functions, chapter
15.
Examples
syms a z
hypergeom([],[],z) returns exp(z)
hypergeom(1,[],z) returns −1/(−1+z)
hypergeom(1,2,'z') returns (exp(z)−1)/z
hypergeom([1,2],[2,3],'z') returns −2*(−exp(z)+1+z)/z^2
hypergeom(a,[],z) returns (1−z)^(−a)
hypergeom([],1,−z^2/4) returns besselj(0,z)
2-58
hypergeom
hypergeom([−n, n],1/2,(1−z)/2) returns
expand(cos(n*acos(z)))
which is T(n, z), the n-th Chebyshev polynomial.
2-59
ifourier
Purpose
ifourier
Inverse Fourier integral transform.
Syntax
f = ifourier(F)
f = ifourier(F,u)
f = ifourier(F,v,u)
Description
f = ifourier(F) is the inverse Fourier transform of the scalar symbolic object
F with default independent variable w. The default return is a function of x. The
inverse Fourier transform is applied to a function of w and returns a function
of x.
F = F( w) ⇒ f = f(x )
If F = F(x), ifourier returns a function of t.
f = f( t )
By definition
∞
∫ F ( w )e iwx dw
f ( x ) = 1 ⁄ ( 2π )
–∞
f = ifourier(F,u) makes f a function of u instead of the default x.
∞
f ( u ) = 1 ⁄ ( 2π )
∫ F ( w )e iwu dw
–∞
Here u is a scalar symbolic object.
f = ifourier(F,v,u) takes F to be a function of v and f to be a function of u
instead of the default w and x, respectively.
∞
f ( u ) = 1 ⁄ ( 2π )
∫ F ( v )e ivu dv
–∞
2-60
ifourier
Examples
Inverse Fourier Transform
f( w ) = e
syms a real
f = exp(–w^2/(4*a^2))
w ⁄ ( 4a )
2
2
∞
∫ f ( w )e
–1
F [f](x) =
ixw
dw
–∞
a – ( ax )
= ------- e
π
g( x) = e
MATLAB Command
F = ifourier(f)
F = simple(F)
returns
2
a*exp(–x^2*a^2)/pi^(1/2)
g = exp(–abs(x))
–x
∞
∫ g ( x )e
–1
F [g](t) =
itx
dx
–∞
returns
π
= -------------21+t
f ( w ) = 2e
1/(1+t^2)/pi
–w
f = 2*exp(–abs(w)) – 1
–1
∞
–1
ifourier(g)
F [f](t) =
∫ f ( w )e
–∞
2
2 – πδ ( t ) ( 1 – t )
= -----------------------------------------2
π( 1 + t )
itw
simple(ifourier(f,t))
dw
returns
(2–pi*Dirac(t)–pi*Dirac(t)*t^2)/
(pi+pi*t^2)
2-61
ifourier
Inverse Fourier Transform
f ( w, v ) = e
2
–w v
MATLAB Command
sin v
------------, w real
v
syms w real
f = exp(–w^2*abs(v))*sin(v)/v
∞
–1
F [ f( v ) ](t ) =
∫ f ( w, v )e
ivt
dv
–∞
t – 1
1
t+1
= ------  atan ----------– atan ---------2
2
2π 
w 
w
See Also
2-62
fourier, ilaplace, iztrans
ifourier(f,v,t)
returns
1/2*(atan((t+1)/w^2)–
atan((–1+t)/w^2))/pi
ilaplace
Purpose
ilaplace
Inverse Laplace transform.
Syntax
F = ilaplace(L)
F = ilaplace(L,y)
F = ilaplace(L,y,x)
Description
F = ilaplace(L) is the inverse Laplace transform of the scalar symbolic object
L with default independent variable s. The default return is a function of t. The
inverse Laplace transform is applied to a function of s and returns a function
of t.
L = L(s ) ⇒ F = F( t)
If L = L(t), ilaplace returns a function of x.
F = F(x)
By definition
c + i∞
F(t) =
∫
st
L ( s )e ds
c – i∞
where c is a real number selected so that all singularities of L(s) are to the left
of the line s = c, i.
F = ilaplace(L,y) makes F a function of y instead of the default t.
c + i∞
F(y) =
∫
sy
L ( y )e ds
c – i∞
Here y is a scalar symbolic object.
F = ilaplace(L,y,x) takes F to be a function of x and L a function of y instead
of the default variables t and s, respectively.
c + i∞
F(x) =
∫
xy
L ( y )e dy
c – i∞
2-63
ilaplace
Examples
Inverse Laplace Transform
f = 1/s^2
1
f ( s ) = ----2s
c + i∞
1
L [ f ] = --------2πi
∫
–1
ilaplace(f)
st
f ( s )e ds
returns
c – i∞
= t
t
1
g ( t ) = ------------------2(t – a)
g = 1/(t–a)^2
c + i∞
1
L [ g ] = --------2πi
∫
–1
ilaplace(g)
xt
g ( t )e dt
returns
c – i∞
= xe
ax
x*exp(a*x)
f = 1/(u^2–a^2)
1
f ( u ) = -----------------2
2
u –a
c + i∞
1
L [ f ] = --------2πi
–1
∫
c – i∞
1
1
= --------------– ----------------ax
– ax
2ae
2ae
2-64
MATLAB Command
ilaplace(f,x)
g ( u )e
xu
du
returns
1/2/a*exp(a*x)–1/2/a*exp(–a*x)
ilaplace
Inverse Laplace Transform
MATLAB Command
f = s^3*v/(s^2+v^2)
3
s v
f ( s, v ) = ----------------2
2
s +v
c + i∞
1
L [ f ] = --------2πi
–1
∫
xv
f ( s, v )e dv
c – i∞
laplace(f,v,x)
returns
3
= s cos sx
See Also
s^3*cos(s*x)
ifourier, iztrans, laplace
2-65
imag
Purpose
imag
Symbolic imaginary part.
Syntax
imag(Z)
Description
imag(Z) is the imaginary part of a symbolic Z.
See Also
conj, real
2-66
int
Purpose
int
Integrate.
Syntax
R
R
R
R
Description
int(S) returns the indefinite integral of S with respect to its symbolic variable
as defined by findsym.
=
=
=
=
int(S)
int(S,v)
int(S,a,b)
int(S,v,a,b)
int(S,v) returns the indefinite integral of S with respect to the symbolic scalar
variable v.
int(S,a,b) returns the definite integral from a to b of each element of S with
respect to each element’s default symbolic variable. a and b are symbolic or
double scalars.
int(S,v,a,b) returns the definite integral of S with respect to v from a to b.
Examples
int(–2*x/(1+x^2)^2) returns
1/(1+x^2)
int(x/(1+z^2),z) returns
atan(z)*x
int(x*log(1+x),0,1) returns
1/4
int(2*x, sin(t), 1) returns
1–sin(t)^2
int([exp(t),exp(alpha*t)]) returns
[exp(t), 1/alpha*exp(alpha*t)]
See Also
diff, symsum
2-67
inv
Purpose
inv
Matrix inverse.
Syntax
R = inv(A)
Description
inv(A) returns inverse of the symbolic matrix A.
Examples
The statements
A = sym([2,–1,0;–1,2,–1;0,–1,2]);
inv(A)
return
[ 3/4, 1/2, 1/4]
[ 1/2,
1, 1/2]
[ 1/4, 1/2, 3/4]
The statements
syms a b c d
A = [a b; c d]
inv(A)
return
[ d/(a*d–b*c), –b/(a*d–b*c)]
[ –c/(a*d–b*c), a/(a*d–b*c)]
Suppose you have created the following M-file:
%% Generate a symbolic N-by-N Hilbert matrix.
function A = genhilb(N)
syms t;
for i = 1:N
for j = 1:N
A(i,j) = 1/(i + j – t);
end
end
Then, the following statement
inv(genhilb(2))
2-68
inv
returns
[
–(–3+t)^2*(–2+t), (–3+t)*(–2+t)*(–4+t)]
[(–3+t)*(–2+t)*(–4+t),
–(–3+t)^2*(–4+t)]
the symbolic inverse of the 2-by-2 Hilbert matrix.
See Also
vpa
Arithmetic Operations page
2-69
iztrans
Purpose
iztrans
Inverse z-transform.
Syntax
f = iztrans(F)
f = iztrans(F,k)
f = iztrans(F,w,k)
Description
f = iztrans(F) is the inverse z-transform of the scalar symbolic object F with
default independent variable z. The default return is a function of n:
1
f ( n ) = --------2πi
°∫
F ( z )z
n–1
dz, n = 1, 2, …
z =R
where R is a positive number chosen so that the function F(z) is analytic on and
outside the circle |z| = R
If F = F(n), iztrans returns a function of k:
f = f( k )
f = iztrans(F,k) makes f a function of k instead of the default n. Here k is a
scalar symbolic object.
f = iztrans(F,w,k) takes F to be a function of w instead of the default
findsym(F) and returns a function of k:
F = F( w) ⇒ f = f(k )
Examples
Inverse Z-Transform
MATLAB Operation
f = 2*z/(z–2)^2
2z
f ( z ) = -------------------2
(z – 2)
1
–1
Z [ f ] = --------2πi
°∫
z =R
= n2
2-70
f ( s )z
n–1
dz
iztrans(f)
returns
n
2^n*n
iztrans
Inverse Z-Transform
MATLAB Operation
n(n + 1)
g ( n ) = ----------------------------2
n + 2n + 1
1
–1
Z [ g ] = --------2πi
°∫
g = n*(n+1)/(n^2+2*n+1)
g ( n )n
k–1
dn
n =R
= –1
iztrans(g)
returns
k
(–1)^k
f = z/(z–a)
z
f ( z ) = -----------z–a
1
–1
Z [ f ] = --------2πi
°∫
f ( z )z
k–1
dz
z =R
=a
iztrans(f,k)
returns
k
a^k
f = x*(x–exp(z))/
(x^2–2*x*exp(z)+exp(2*z))
z
x( x – e )
f ( x, z ) = ------------------------------------2
z
2z
x – 2xe + e
1
–1
Z [ f ] = --------2πi
°∫
f ( x, z )x
k–1
x =R
=e
See Also
kz
dx
iztrans(f,x,k)
returns
exp(z)^k
ifourier, ilaplace, ztrans
2-71
jacobian
Purpose
jacobian
Jacobian matrix.
Syntax
R = jacobian(w,v)
Description
jacobian(w,v) computes the Jacobian of w with respect to v. w is a symbolic
scalar expression or a symbolic column vector. v is a symbolic row vector. The
(i,j)-th entry of the result is ∂w(i)/∂v(j).
Examples
The statements
w
v
R
b
=
=
=
=
[x*y*z; y; x+z];
[x,y,z];
jacobian(w,v)
jacobian(x+z, v)
return
R =
[y*z, x*z, x*y]
[ 0,
1,
0]
[ 1,
0,
1]
b =
[1, 0, 1]
See Also
2-72
diff
jordan
Purpose
jordan
Jordan canonical form.
Syntax
J = jordan(A)
[V,J] = jordan(A)
Description
jordan(A) computes the Jordan canonical (normal) form of A, where A is a
symbolic or numeric matrix. The matrix must be known exactly. Thus, its
elements must be integers or ratios of small integers. Any errors in the input
matrix may completely change the Jordan canonical form.
[V,J] = jordan(A) computes both J, the Jordan canonical form, and the
similarity transform, V, whose columns are the generalized eigenvectors.
Moreover, V\A*V=J.
Examples
The statements
A = [1 –3 –2; –1 1 –1; 2 4 5]
[V,J] = jordan(A)
return
A =
1
–1
2
–3
1
4
–2
–1
5
V =
[–1, –2, 2]
[ 0, –2, 0]
[ 0, 4, 0]
J =
[3, 0, 0]
[0, 2, 1]
[0, 0, 2]
Then the statements
V = double(V)
V\A*V
2-73
jordan
return
V =
–1
0
1
ans =
3
0
0
See Also
2-74
eig, poly
–2
–2
4
0
2
0
2
0
0
0
1
2
lambertw
Purpose
lambertw
Lambert’s W function.
Syntax
Y = lambertw(X)
Description
lambertw(X) evaluates Lambert’s W function at the elements of X, a numeric
matrix or a symbolic matrix. Lambert’s W solves the equation
w
we = x
for w as a function of x.
Examples
lambertw([0 –exp(–1); pi 1]) returns
0
1.0737
–1.0000
0.5671
The statements
syms x y
lambertw([0 x;1 y])
return
[
0, lambertw(x)]
[ lambertw(1), lambertw(y)]
References
[1] Corless, R.M, Gonnet, G.H., Hare, D.E.G., and Jeffrey, D.J., Lambert's W
Function in Maple, Technical Report, Dept. of Applied Math., Univ. of Western
Ontario, London, Ontario, Canada.
[2] Corless, R.M, Gonnet, G.H., Hare, D.E.G., and Jeffrey, D.J., On Lambert's
W Function, Technical Report, Dept. of Applied Math., Univ. of Western
Ontario, London, Ontario, Canada.
Both papers are available by anonymous FTP from
cs–archive.uwaterloo.ca
2-75
laplace
Purpose
laplace
Laplace transform.
Syntax
laplace(F)
laplace(F,t)
fourier(F,w,z)
Description
L = laplace(F) is the Laplace transform of the scalar symbol F with default
independent variable t. The default return is a function of s. The Laplace
transform is applied to a function of t and returns a function of s.
F = F( t) ⇒ L = L( s )
If F = F(s), laplace returns a function of t.
L = L( t)
By definition
∞
L( s ) =
∫ F ( t )e
– st
dt
0
where t is the symbolic variable in F as determined by findsym.
L = laplace(F,t) makes L a function of t instead of the default s.
∞
L( t) =
∫ F ( x )e
– tx
dx
0
Here L is returned as a scalar symbol.
L = laplace(F,w,z) makes L a function of z and F a function of w instead of
the default variables s and t, respectively.
∞
L( z) =
∫ F ( w )e
0
2-76
– zw
dw
laplace
Examples
Laplace Transform
f( t ) = t
MATLAB Command
f = t^4
4
∞
L[ f] =
∫ f ( t )e
–ts
laplace(f)
dt
0
returns
24
= -----5s
24/s^5
g = 1/sqrt(s)
1
g ( s ) = -----s
∞
∫
L[ g]( t) =
g ( s )e
– st
laplace(g)
ds
returns
0
=
π
--s
f( t ) = e
1/s^(1/2)*pi^(1/2)
f = exp(–a*t)
– at
∞
L[ f]( x) =
∫ f ( t )e
0
1
= -----------x+a
– tx
dt
laplace(f,x)
returns
1/(x + a)
2-77
laplace
Laplace Transform
MATLAB Command
f ( t, v ) = 1 – cos tv
f = 1 – cos(t*v)
∞
L[ f ]( x) =
∫ f ( t, v )e
–vx
dv
laplace(f,v,x)
0
2
t
1
x
= --- – ---------------- = -------------------x x 2 + t 2 x 3 + xt 2
See Also
2-78
fourier, ilaplace, ztrans
returns
1/x–x/(x^2+t^2)
latex
Purpose
latex
Syntax
latex(S)
Description
latex(S) returns the LaTeX representation of the symbolic expression S.
Examples
The statements
LaTeX representation of a symbolic expression.
syms x
f = taylor(log(1+x));
latex(f)
return
x–1/2\,{x}^{2}+1/3\,{x}^{3}–1/4\,{x}^{4}+1/5\,{x}^{5}
The statements
H = sym(hilb(3));
latex(H)
return
\left [\begin {array}{ccc} 1&1/2&1/3\\\noalign{\medskip}1/2&1/
3&1/4
\\\noalign{\medskip}1/3&1/4&1/5\end {array}\right ]
The statements
syms alpha t
A = [alpha t alpha*t];
latex(A)
return
\left [\begin {array}{ccc} \alpha&t&\alpha\,t\end {array}\right ]
See Also
pretty, ccode, fortran
2-79
limit
Purpose
Syntax
limit
Limit of a symbolic expression.
limit(F,x,a)
limit(F,a)
limit(F)
limit(F,x,a,'right')
limit(F,x,a,'left')
Description
limit(F,x,a) takes the limit of the symbolic expression F as x -> a.
limit(F,a) uses findsym(F) as the independent variable.
limit(F) uses a = 0 as the limit point.
limit(F,x,a,'right') or limit(F,x,a,'left') specify the direction of a
one-sided limit.
Examples
Assume
syms x a t h;
Then
limit(sin(x)/x)
limit(1/x,x,0,'right')
limit(1/x,x,0,'left')
limit((sin(x+h)–sin(x))/h,h,0)
v = [(1 + a/x)^x, exp(–x)];
limit(v,x,inf,'left')
See Also
2-80
pretty, ccode, fortran
=>
=>
=>
=>
1
inf
–inf
cos(x)
=> [exp(a),
0]
maple
Purpose
maple
Access Maple kernel.
Syntax
r = maple('statement')
r = maple('function',arg1,arg2,...)
[r, status] = maple(...)
maple('traceon') or maple trace on
maple('traceoff') or maple trace off
Description
maple('statement') sends statement to the Maple kernel and returns the
result. A semicolon for the Maple syntax is appended to statement if necessary.
maple('function',arg1,arg2,...) accepts the quoted name of any Maple
function and associated input arguments. The arguments are converted to
symbolic expressions if necessary, and function is then called with the given
arguments. If the input arguments are syms, then maple returns a sym.
Otherwise, it returns a result of class char.
[r,status] = maple(...) is an option that returns the warning/error status.
When the statement execution is successful, r is the result and status is 0. If
the execution fails, r is the corresponding warning/error message, and status
is a positive integer.
maple('traceon') (or maple trace on) causes all subsequent Maple
statements and results to be printed. maple('traceoff') (or maple trace
off) turns this feature off.
Examples
Each of the following statements evaluate π to 100 digits:
maple('evalf(Pi,100)')
maple evalf Pi 100
maple('evalf','Pi',100)
The statement
[result,status] = maple('BesselK',4.3)
2-81
maple
returns the following output because Maple’s BesselK function needs two input
arguments:
result =
Error, (in BesselK) invalid arguments
status =
2
The traceon command shows how Symbolic Math Toolbox commands interact
with Maple. For example, the statements
syms x
v = [x^2–1;x^2–4]
maple traceon % or maple trace on
w = factor(v)
return
v =
[x^2–1]
[x^2–4]
statement =
map(ifactor,array([[x^2–1],[x^2–4]]));
result =
Error, (in ifactor) invalid arguments
statement =
map(factor,array([[x^2–1],[x^2–4]]));
result =
MATRIX([[(x–1)*(x+1)], [(x–2)*(x+2)]])
w =
[(x–1)*(x+1)]
[(x–2)*(x+2)]
This example reveals that the factor statement first invokes Maple’s integer
factor (ifactor) statement to determine whether the argument is a factorable
integer. If Maple’s integer factor statement returns an error, the Symbolic
2-82
maple
Math Toolbox factor statement then invokes Maple’s expression factoring
statement.
See Also
mhelp, procread
2-83
mapleinit
Purpose
mapleinit
Initialize the Maple kernel.
Syntax
mapleinit
Description
mapleinit determines the path to the directory containing the Maple Library,
loads the Maple linear algebra and integral transform packages, initializes
digits, and establishes several aliases. mapleinit is called by the MEX-file
interface to Maple.
You can edit the mapleinit M-file to change the pathname to the Maple
library. You do this by changing the initstring variable in mapleinit.m to the
full pathname of the Maple library, as described below.
UNIX. Suppose you already have a copy of the Library for Maple V, Release 4
in the UNIX directory /usr/local/Maple/lib. You can edit mapleinit.m to
contain
maplelib = '/usr/local/Maple/lib'
and then delete the copy of the Maple Library that is distributed with
MATLAB.
MS-Windows. Suppose you already have a copy of the Library for Maple V,
Release 4 in the directory C:\MAPLE\LIB. You can edit mapleinit.m to contain
maplelib = 'C:\MAPLE\LIB'
and then delete the copy of the Maple Library that is distributed with
MATLAB.
Macintosh. Suppose you already have a copy of the Library for Maple V,
Release 4 in the directory MyDisk:Maple:Lib. You can edit mapleinit.m to
contain
maplelib = 'MyDisk:Maple:Lib'
and then delete the copy of the Maple Library that is distributed with
MATLAB.
2-84
mfun
Purpose
mfun
Numeric evaluation of Maple mathematical function.
Syntax
Y = mfun('function',par1,par2,par3,par4)
Description
mfun('function',par1,par2,par3,par4) numerically evaluates one of the
special mathematical functions known to Maple. Each par argument is a
numeric quantity corresponding to a Maple parameter for function. You can
use up to four parameters. The last parameter specified can be a matrix,
usually corresponding to X. The dimensions of all other parameters depend on
the Maple specifications for function. You can access parameter information
for Maple functions using one of the following commands:
help mfunlist
mhelp function
Maple evaluates function using 16 digit accuracy. Each element of the result
is a MATLAB numeric quantity. Any singularity in function is returned as
NaN.
Examples
mfun('FresnelC',0:5) returns
0
0.7799
0.4883
0.6057
0.4984
0.5636
mfun('Chi',[3*i 0]) returns
0.1196 + 1.5708i
See Also
NaN
mfunlist, mhelp
2-85
mfunlist
Purpose
mfunlist
List special functions for use with mfun.
Syntax
mfunlist % help mfunlist on the Macintosh
Description
mfunlist lists the special mathematical functions for use with the mfun
function. The following tables describe these special functions.
You can access more detailed descriptions by typing
mhelp function
Limitations
In general, the accuracy of a function will be lower near its roots and when its
arguments are relatively large.
Runtime depends on the specific function and its parameters. In general,
calculations are slower than standard MATLAB calculations.
See Also
mfun, mhelp
References
[1] Abramowitz, M. and Stegun, I.A., Handbook of Mathematical Functions ,
Dover Publications, 1965.
Table
Conventions
The following conventions are used in Table 2-1: MFUN Special Functions,
unless otherwise indicated in the Arguments column:
2-86
x, y
real argument
z , z1, z2
complex argument
m, n
integer argument
mfunlist
Table 2-1: MFUN Special Functions
Function Name
Definition
mfun Name
Arguments
Bernoulli Numbers
and Polynomials
Generating functions:
bernoulli(n)
bernoulli(n,t)
n ≥ 0
xt
e
=
------------t
e –1
Bessel Functions
∞
∑
0 < t < 2π
n–1
t
B n ( x ) ⋅ -----------n!
n=0
BesselI, BesselJ – Bessel functions BesselJ(v,x)
BesselY(v,x)
BesselK, BesselY – Bessel functions BesselI(v,x)
BesselK(v,x)
v is real.
of the first kind.
of the second kind.
Beta Function
Beta(x,y)
Γ(x ) ⋅ Γ(y)
B ( x, y ) = ---------------------------Γ(x + y)
Binomial Coefficients
binomial(m,n)
m!
 m = -------------------------- n
n! ( m – n )!
Γ(m + 1)
= -------------------------------------------------------Γ ( n + 1 )Γ ( m – n + 1 )
Complete Elliptic
Integrals
Legendre ’s complete elliptic integrals LegendreKc(k)
LegendreEc(k)
of the first, second, and third kind.
a is real
–Inf < a < Inf
LegendrePic(a,k)
k is real
0 < k < 1
2-87
mfunlist
Table 2-1: MFUN Special Functions (Continued)
Function Name
Definition
Complete Elliptic
Integrals with
Complementary
Modulus
Associated complete elliptic integrals LegendreKc1(k)
a is real
LegendreEc1(k)
of the first, second, and third kind
–Inf < a < Inf
LegendrePic1(a,k)
using complementary modulus.
k is real
0 < k < 1
Complementary Error
Function and Its
Iterated Integrals
mfun Name
erfc(z)
erfc(n,z)
∞
2
2
–t
erfc ( z ) = ------- ⋅ e dt = 1 – erf ( z )
π
∫
z
2
2
–z
erfc ( – 1, z ) = ------- ⋅ e
π
∞
erfc ( n, z ) =
∫ erfc ( n – 1, z ) dt
z
Dawson’s Integral
dawson(x)
x
F( x ) = e
–x
2
∫
⋅ e
–t
2
dt
0
Digamma Function
Psi(x)
ψ(x) =
2-88
d
Γ′ ( x )
ln ( Γ ( x ) ) = ------------dx
Γ( x)
Arguments
n > 0
mfunlist
Table 2-1: MFUN Special Functions (Continued)
Function Name
Definition
Dilogarithm Integral
x
f( x ) =
mfun Name
Arguments
dilog(x)
x > 1
ln ( t )
- dt
∫ -----------1–t
1
Error Function
erf(z)
z
2
2
–t
erf ( z ) = ------- e dt
π
∫
0
Euler Numbers and
Polynomials
Generating function for Euler
numbers:
∞
1
-------------- =
ch ( t )
euler(n)
euler(n,z)
n≥0
Ei(n,z)
Ei(x)
n ≥ 0
Real(z) > 0
π
t < --2
n
t
E n ----n!
∑
n=0
Exponential Integrals
∞
Ei ( n, z ) =
∫
1
– zt
e
-------- dt
n
t
x
Ei ( x ) = PV –
e
t
∫ ---t–∞
2-89
mfunlist
Table 2-1: MFUN Special Functions (Continued)
Function Name
Definition
mfun Name
Fresnel Sine and
Cosine Integrals
FresnelC(x)
FresnelS(x)
x
C(x) =
Arguments
π
2
dt
π
2
dt
∫ cos --2- ⋅ t

0
x
S( x ) =
∫ sin --2- ⋅ t

0
Gamma Function
GAMMA(z)
∞
Γ(z) =
∫t
z – 1 –t
e dt
0
Harmonic Function
harmonic(n)
n
h(n) =
∑
1
--- = ψ ( n + 1 ) + γ
k
k=1
Hyperbolic Sine and
Cosine Integrals
z
Shi ( z ) =
Shi(z)
Chi(z)
sinh ( t )
dt
∫ ------------------t
0
z
cosh ( t ) – 1
Chi ( z ) = γ + ln ( z ) + ----------------------------- dt
t
∫
0
2-90
n > 0
mfunlist
Table 2-1: MFUN Special Functions (Continued)
Function Name
(Generalized)
Hypergeometric
Function
Definition
j
∏
∞
F ( n, d, z ) =
∑
Γ ( ni + k ) k
------------------------ ⋅ z
Γ ( ni )
i=1
-------------------------------------------m
Γ
(
d
+
k
)
i
k=0
- ⋅ k!
∏ ----------------------Γ ( di )
mfun Name
Arguments
hypergeom(n,d,x)
n1,n2,... are
where
d1,d2,... are
n = [n1,n2,...]
d = [d1,d2,...]
real and
non-negative.
real.
i=1
where j and m are the number of
terms in n and d, respectively.
Incomplete Elliptic
Integrals
Legendre's incomplete elliptic
integrals of the first, second, and
third kind.
LegendreF(x,k)
0 < x ≤ Inf
LegendreE(x,k)
LegendrePi(x,a,k) a is real
–Inf < a < Inf
k is real
0 < k < 1
Incomplete Gamma
Function
GAMMA(z1,z2)
∞
Γ ( a, z ) =
∫e
–t
⋅t
a–1
dt
z
Logarithm of the
Gamma Function
lnGAMMA(z)
ln Γ ( z ) = ln ( Γ ( z ) )
2-91
mfunlist
Table 2-1: MFUN Special Functions (Continued)
Function Name
Definition
Logarithmic Integral
mfun Name
Arguments
Li(x)
x > 1
Psi(n,z)
n ≥ 0
x

 dt 
Li ( x ) = PV  --------  = Ei ( ln x )
 ln t 
0

∫
Polygamma Function
ψ
(n)
n
(z) =
d
ψ(z)
dz
where ψ ( z ) is the Digamma function.
Shifted Sine Integral
Ssi(z)
π
Ssi ( z ) = Si ( z ) – --2
2-92
mfunlist
Orthogonal
Polynomials
The following functions require the Maple Orthogonal Polynomial Package.
They are available only with the Extended Symbolic Math Toolbox. Before
using these functions, you must first initialize the Orthogonal Polynomial
Package by typing
maple('with','orthopoly')
Note that in all cases, n is a non-negative integer and x is real.
Table 2-2: Orthogonal Polynomials
Polynomial
Maple Name
Arguments
Gegenbauer
G(n,a,x)
a is a nonrational algebraic
expression or a rational number
greater than –1/2.
Hermite
H(n,x)
Laguerre
L(n,x)
Generalized
Laguerre
L(n,a,x)
Legendre
P(n,x)
Jacobi
P(n,a,b,x)
Chebyshev of
the First and
Second Kind
T(n,x)
U(n,x)
a is a nonrational algebraic
expression or a rational number
greater than –1.
a, b are nonrational algebraic
expressions or rational numbers
greater than –1.
2-93
mhelp
Purpose
mhelp
Maple help.
Syntax
mhelp topic
mhelp('topic')
Description
mhelp topic and mhelp('topic') both return Maple’s online documentation
for the specified Maple topic.
Examples
mhelp BesselI and mhelp('BesselI') both return Maple’s online
documentation for the Maple BesselI function.
See Also
maple
2-94
null
Purpose
null
Basis for null space.
Syntax
Z = null(A)
Description
The columns of Z = null(A) form a basis for the null space of A.
size(Z,2) is the nullity of A.
A*Z is zero.
If A has full rank, Z is empty.
Examples
The statements
A = sym(magic(4));
Z = null(A)
A*Z
return
[ –1]
[ –3]
[ 3]
[ 1]
[
[
[
[
See Also
0]
0]
0]
0]
arithmetic operators, colspace, rank, rref, svd
null in the online MATLAB Function Reference.
2-95
numden
Purpose
numden
Numerator and denominator.
Syntax
[N,D] = numden(A)
Description
[N,D] = numden(A) converts each element of A to a rational form where the
numerator and denominator are relatively prime polynomials with integer
coefficients. A is a symbolic or a numeric matrix. N is the symbolic matrix of
numerators, and D is the symbolic matrix of denominators.
Examples
[n,d] = numden(4/5) returns n = 4 and d = 5.
[n,d] = numden(x/y + y/x) returns
n =
x^2+y^2
d =
y*x
The statements
A = [a, 1/b]
[n,d] = numden(A)
return
A =
[a, 1/b]
n =
[a, 1]
d =
[1, b]
2-96
poly
Purpose
poly
Characteristic polynomial of a matrix.
Syntax
p = poly(A)
p = poly(A, v)
Description
If A is a numeric array, poly(A) returns the coefficients of the characteristic
polynomial of A. If A is symbolic, poly(A) returns the characteristic polynomial
of A in terms of the default variable x. The variable v can be specified in the
second input argument.
Note that if A is numeric, poly(sym(A)) approximately equals
poly2sym(poly(A)). The approximation is due to roundoff error.
Examples
The statements
A
p
q
s
=
=
=
=
gallery(3)
poly(A)
poly(sym(A))
poly(sym(A),z)
return
A =
–149
537
–27
–50
180
–9
p =
1.0000
–6.0000
–154
546
–25
11.0000
–6.0000
q=
x^3–6*x^2+11*x–6
s =
z^3–6*z^2+11*z–6
See Also
poly2sym, jordan, eig, solve
2-97
poly2sym
Purpose
poly2sym
Polynomial coefficient vector to symbolic polynomial.
Syntax
r = poly2sym(c)
r = poly2sym(c, v)
Description
r = poly2sym(c) returns a symbolic representation of the polynomial whose
coefficients are in the numeric vector c. The default symbolic variable is x. The
variable v can be specified as a second input argument. If c = [c1 c2 ... cn],
r=poly2sym(c) has the form:
c1 x
n–1
+ c2 x
n–2
+ … + cn
poly2sym uses sym’s default (rational) conversion mode to convert the numeric
coefficients to symbolic constants. This mode expresses the symbolic coefficient
approximately as a ratio of integers, if sym can find a simple ratio that
approximates the numeric value, otherwise as an integer multiplied by a power
of 2.
If x has a numeric value and sym expresses the elements of c exactly,
eval(poly2sym(c)) returns the same value as polyval(c,x).
Examples
poly2sym([1 3 2]) returns
x^2 + 3*x + 2
poly2sym([.694228, .333, 6.2832]) returns
6253049924220329/9007199254740992*x^2+333/1000*x+3927/625
poly2sym([1 0 1 –1 2], y) returns
y^4+y^2–y+2
See Also
2-98
sym, sym2poly
polyval in the online MATLAB Function Reference
pretty
Purpose
pretty
Prettyprint symbolic expressions.
Syntax
pretty(S)
pretty(S,n)
Description
The pretty function prints symbolic output in a format that resembles typeset
mathematics.
pretty(S) prettyprints the symbolic matrix S using the default line width of
79.
pretty(S,n) prettyprints S using line width n instead of 79.
Examples
The following statements:
A = sym(pascal(2))
B = eig(A)
pretty(B)
return
A =
[1, 1]
[1, 2]
B =
[3/2+1/2*5^(1/2)]
[3/2–1/2*5^(1/2)]
[
1/2
[ 3/2 + 1/2 5
[
[
1/2
[ 3/2 – 1/2 5
]
]
]
]
]
2-99
procread
Purpose
procread
Install a Maple procedure.
Syntax
procread('filename')
Description
procread('filename') reads the specified file, which should contain the source
text for a Maple procedure. It deletes any comments and newline characters,
then sends the resulting string to Maple.
The Extended Symbolic Math Toolbox is required.
Examples
Suppose the file ident.src contains the following source text for a Maple
procedure.
ident := proc(A)
# ident(A) computes A*inverse(A)
local X;
X := inverse(A);
evalm(A &* X);
end;
Then the statement
procread('ident.src')
installs the procedure. It can be accessed with
maple('ident',magic(3))
or
maple('ident',vpa(magic(3)))
See Also
2-100
maple
rank
Purpose
rank
Symbolic matrix rank.
Syntax
rank(A)
Description
rank(A) is the rank of the symbolic matrix A.
Examples
rank([a b;c d]) is 2.
rank(sym(magic(4))) is 3.
2-101
real
Purpose
real
Symbolic real part.
Syntax
real(Z)
Description
real(Z) is the real part of a symbolic Z.
See Also
conj, imag
2-102
rref
Purpose
rref
Reduced row echelon form.
Syntax
rref(A)
Description
rref(A) is the reduced row echelon form of the symbolic matrix A.
Examples
rref(sym(magic(4))) returns
[
[
[
[
1,
0,
0,
0,
0,
1,
0,
0,
0, 1]
0, 3]
1, –3]
0, 0]
2-103
rsums
Purpose
rsums
Interactive evaluation of Riemann sums.
Syntax
rsums(f)
Description
rsums(f) interactively approximates the integral of f(x) by Riemann sums.
rsums(f) displays a graph of f(x). You can then adjust the number of terms
taken in the Riemann sum by using the slider below the graph. The number of
terms available ranges from 2 to 256.
Examples
2-104
rsums exp(–5*x^2) creates the following plot
simple
Purpose
simple
Search for a symbolic expression’s simplest form.
Syntax
r = simple(S)
[r,how] = simple(S)
Description
simple(S) tries several different algebraic simplifications of the symbolic
expression S, displays any that shorten the length of S’s representation, and
returns the shortest. S is a sym. If S is a matrix, the result represents the
shortest representation of the entire matrix, which is not necessarily the
shortest representation of each individual element. If no return output is given,
simple(S) displays all possible representations and returns the shortest.
[r,how] = simple(S) does not display intermediate simplifications, but
returns the shortest found, as well as a string describing the particular
simplification. r is a sym. how is a string.
Examples
See Also
Expression
Simplification
Simplification Method
cos(x)^2+sin(x)^2
1
simplify
2*cos(x)^2–sin(x)^2
3*cos(x)^2–1
simplify
cos(x)^2–sin(x)^2
cos(2*x)
combine(trig)
cos(x)+
(–sin(x)^2)^(1/2)
cos(x)+i*sin(x)
radsimp
cos(x)+i*sin(x)
exp(i*x)
convert(exp)
(x+1)*x*(x–1)
x^3–x
collect(x)
x^3+3*x^2+3*x+1
(x+1)^3
factor
cos(3*acos(x))
4*x^3–3*x
expand
collect, expand, factor, horner, simplify
2-105
simplify
Purpose
simplify
Symbolic simplification.
Syntax
R = simplify(S)
Description
simplify(S) simplifies each element of the symbolic matrix S using Maple
simplification rules.
Examples
simplify(sin(x)^2 + cos(x)^2) returns
1
simplify(exp(c*log(sqrt(a+b)))) returns
(a+b)^(1/2*c)
The statements
S = [(x^2+5*x+6)/(x+2),sqrt(16)];
R = simplify(S)
return
R = [x+3,4]
See Also
2-106
collect, expand, factor, horner, simple
sinint
Purpose
sinint
Sine integral function.
Syntax
Y = sinint(X)
Description
sinint(X) evaluates the sine integral function at the elements of X, a numeric
matrix, or a symbolic matrix. The result is a numeric matrix. The sine integral
function is defined by
Si ( x ) =
Examples
sin t
- dt
∫0 ---------t
x
sinint([pi 0;–2.2 exp(3)]) returns
1.8519
–1.6876
0
1.5522
sinint(1.2) returns 1.1080.
diff(sinint(x)) returns sin(x)/x.
See Also
cosint
2-107
size
Purpose
size
Symbolic matrix dimensions.
Syntax
d = size(A)
[m,n] = size(A)
d= size(A, n)
Description
Suppose A is an m-by-n symbolic or numeric matrix. The statement
d = size(A) returns a numeric vector with two integer components, d = [m,n].
The multiple assignment statement [m,n] = size(A) returns the two integers
in two separate variables.
The statement d = size(A,n) returns the length of the dimension specified by
the scalar n. For example, size(A,1) is the number of rows of A and size(A,2)
is the number of columns of A.
Examples
The statements
syms a b c d
A = [a b c ; a b d; d c b; c b a];
d = size(A)
r = size(A, 2)
return
d =
4
3
r =
3
See Also
2-108
length, ndims in the online MATLAB Function Reference
solve
Purpose
solve
Symbolic solution of algebraic equations.
Syntax
g
g
g
g
Description
Single Equation/Expression. The input to solve can be either symbolic
expressions or strings. If eq is a symbolic expression (x^2–2*x+1) or a string
that does not contain an equal sign ('x^2–2*x+1'), then solve(eq) solves the
equation eq=0 for its default variable (as determined by findsym).
=
=
=
=
solve(eq)
solve(eq,var)
solve(eq1,eq2,...,eqn)
solve(eq1,eq2,...,eqn,var1,var2,...,varn)
solve(eq,var) solves the equation eq (or eq=0 in the two cases cited above) for
the variable var.
System of Equations. The inputs are either symbolic expressions or strings
specifying equations. solve(eq1,eq2,...,eqn) solves the system of equations
implied by eq1,eq2,...,eqn in the n variables determined by applying
findsym to the system.
Three different types of output are possible. For one equation and one output,
the resulting solution is returned with multiple solutions for a nonlinear
equation. For a system of equations and an equal number of outputs, the
results are sorted alphabetically and assigned to the outputs. For a system of
equations and a single output, a structure containing the solutions is returned.
For both a single equation and a system of equations, numeric solutions are
returned if symbolic solutions cannot be determined.
Examples
solve('a*x^2 + b*x + c') returns
[ 1/2/a*(–b+(b^2–4*a*c)^(1/2)),
1/2/a*(–b–(b^2–4*a*c)^(1/2))]
solve('a*x^2 + b*x + c','b') returns
–(a*x^2+c)/x
solve('x + y
= 1','x – 11*y = 5') returns
y = –1/3, x = 4/3
2-109
solve
A = solve('a*u^2 + v^2', 'u – v = 1', 'a^2 – 5*a + 6')
returns
A =
a: [1x4 sym]
u: [1x4 sym]
v: [1x4 sym]
where
A.a =
[ 2, 2, 3, 3]
A.u =
[ 1/3+1/3*i*2^(1/2), 1/3–1/3*i*2^(1/2),
1/4+1/4*i*3^(1/2), 1/4–1/4*i*3^(1/2)]
A.v =
[ –2/3+1/3*i*2^(1/2), –2/3–1/3*i*2^(1/2),
–3/4+1/4*i*3^(1/2), –3/4–1/4*i*3^(1/2)]
See Also
2-110
arithmetic operators, dsolve, findsym
subexpr
Purpose
subexpr
Rewrite a symbolic expression in terms of common subexpressions.
Syntax
[Y,SIGMA] = subexpr(X,SIGMA)
[Y,SIGMA] = subexpr(X,'SIGMA')
Description
[Y,SIGMA] = subexpr(X,SIGMA) or [Y,SIGMA] = subexpr(X,'SIGMA')
rewrites the symbolic expression X in terms of its common subexpressions.
These are the subexpressions that are written as %1, %2, etc. by pretty(S).
Examples
The statements
t = solve('a*x^3+b*x^2+c*x+d = 0');
[r,s] = subexpr(t,'s');
return the rewritten expression for t in r in terms of a common subexpression,
which is returned in s.
See Also
pretty, simple, subs
2-111
subs
Purpose
subs
Symbolic substitution in a symbolic expression or matrix.
Syntax
R = subs(S)
R = subs(S,old,new)
Description
subs(S) replaces all occurrences of variables in the symbolic expression S with
values obtained from the calling function, or the MATLAB workspace.
subs(S,old,new) replaces old with new in the symbolic expression S. old is a
symbolic variable or a string representing a variable name. new is a symbolic
or numeric variable or expression.
If old and new are cell arrays of the same size, each element of old is replaced
by the corresponding element of new. If S and old are scalars and new is an
array or cell array, the scalars are expanded to produce an array result. If new
is a cell array of numeric matrices, the substitutions are performed
elementwise (i.e., subs(x*y,{x,y},{A,B}) returns A.*B when A and B are
numeric).
If subs(s,old,new) does not change s, subs(s,new,old)is tried. This provides
backwards compatibility with previous versions and eliminates the need to
remember the order of the arguments. subs(s,old,new) does not switch the
arguments if s does not change.
Examples
Single input:
Suppose a = 980 and C1 = 3 exist in the workspace.
The statement
y = dsolve('Dy = -a*y')
produces
y = exp(-a*t)*C1
Then the statement
subs(y)
produces
ans = 3*exp(-980*t)
2-112
subs
Single Substitution:
subs(a+b,a,4) returns 4+b.
Multiple Substitutions:
subs(cos(a)+sin(b),{a,b},{sym('alpha'),2}) returns
cos(alpha)+sin(2)
Scalar Expansion Case:
subs(exp(a*t),'a',-magic(2)) returns
[
exp(-t), exp(-3*t)]
[ exp(-4*t), exp(-2*t)]
Multiple Scalar Expansion:
subs(x*y,{x,y},{[0 1;-1 0],[1 -1;-2 1]}) returns
[
[
See Also
0, -1]
2, 0]
simplify, subexpr
2-113
svd
Purpose
svd
Symbolic singular value decomposition.
Syntax
sigma =
sigma =
[U,S,V]
[U,S,V]
Description
sigma = svd(A) is a symbolic vector containing the singular values of a
symbolic matrix A.
svd(A)
svd(vpa(A))
= svd(A)
= svd(vpa(A))
sigma = svd(vpa(A)) computes numeric singular values, using variable
precision arithmetic.
[U,S,V] = svd(A) and [U,S,V] = svd(vpa(A)) return numeric unitary
matrices U and V whose columns are the singular vectors and a diagonal matrix
S containing the singular values. Together, they satisfy A = U*S*V'.
Symbolic singular vectors are not available.
Examples
The statements
digits(3)
A = sym(magic(4));
svd(A)
svd(vpa(A))
[U,S,V] = svd(A)
return
[
0]
[
34]
[ 2*5^(1/2)]
[ 8*5^(1/2)]
[ .311e–6*i]
[
4.47]
[
17.9]
[
34.1]
2-114
svd
U =
[
[
[
[
–.500, .671, .500, –.224]
–.500, –.224, –.500, –.671]
–.500, .224, –.500, .671]
–.500, –.671, .500, .224]
S =
[
[
[
[
34.0,
0,
0,
0,
0,
17.9,
0,
0,
0,
0]
0,
0]
4.47,
0]
0, .835e–15]
V =
[
[
[
[
See Also
–.500, .500, .671, –.224]
–.500, –.500, –.224, –.671]
–.500, –.500, .224, .671]
–.500, .500, –.671, .224]
digits, eig, vpa
2-115
sym
Purpose
sym
Construct symbolic numbers, variables and objects.
Syntax
S
x
x
x
S
Description
S = sym(A) constructs an object S, of class 'sym', from A. If the input argument
=
=
=
=
=
sym(A)
sym('x')
sym('x','real')
sym('x','unreal')
sym(A,flag) where flag is one of 'r', 'd', 'e', or 'f'.
is a string, the result is a symbolic number or variable. If the input argument
is a numeric scalar or matrix, the result is a symbolic representation of the
given numeric values.
x = sym('x') creates the symbolic variable with name 'x' and stores the
result in x. x = sym('x','real') also assumes that x is real, so that conj(x)
is equal to x. alpha = sym('alpha') and r = sym('Rho','real') are other
examples. x = sym('x','unreal') makes x a purely formal variable with no
additional properties (i.e., ensures that x is not real). See also the reference
pages on syms.
Statements like pi = sym('pi') and delta = sym('1/10') create symbolic
numbers that avoid the floating-point approximations inherent in the values of
pi and 1/10. The pi created in this way temporarily replaces the built-in
numeric function with the same name.
S = sym(A,flag) converts a numeric scalar or matrix to symbolic form. The
technique for converting floating-point numbers is specified by the optional
second argument, which can be 'f', 'r', 'e' or 'd'. The default is 'r'.
'f' stands for “floating-point.” All values are represented in the form
'1.F'*2^(e) or '–1.F'*2^(e) where F is a string of 13 hexadecimal digits and
e is an integer. This captures the floating-point values exactly, but may not be
convenient for subsequent manipulation. For example, sym(1/10,'f') is
'1.999999999999a'*2^(–4) because 1/10 cannot be represented exactly in
floating-point.
'r' stands for “rational.” Floating-point numbers obtained by evaluating
expressions of the form p/q, p*pi/q, sqrt(p), 2^q, and 10^q for modest sized
integers p and q are converted to the corresponding symbolic form. This
effectively compensates for the roundoff error involved in the original
2-116
sym
evaluation, but may not represent the floating-point value precisely. If no
simple rational approximation can be found, an expression of the form p*2^q
with large integers p and q reproduces the floating-point value exactly. For
example, sym(4/3,'r') is '4/3', but sym(1+sqrt(5),'r') is
7286977268806824*2^(–51)
'e' stands for “estimate error.” The 'r' form is supplemented by a term
involving the variable 'eps', which estimates the difference between the
theoretical rational expression and its actual floating-point value. For
example, sym(3*pi/4) is 3*pi/4–103*eps/249.
'd' stands for “decimal.” The number of digits is taken from the current setting
of digits used by vpa. Fewer than 16 digits loses some accuracy, while more
than 16 digits may not be warranted. For example, with digits(10),
sym(4/3,'d') is 1.333333333, while with digits digits(20), sym(4/3,'d') is
1.3333333333333332593, which does not end in a string of 3's, but is an
accurate decimal representation of the floating-point number nearest to 4/3.
See Also
digits, double, syms
eps in the online MATLAB Function Reference
2-117
syms
Purpose
syms
Short-cut for constructing symbolic objects.
Syntax
syms arg1 arg2 ...
syms arg1 arg2 ... real
syms arg1 arg2 ... unreal
Description
syms arg1 arg2 ... is short-hand notation for
arg1 = sym('arg1');
arg2 = sym('arg2'); ...
syms arg1 arg2 ... real is short-hand notation for
arg1 = sym('arg1','real');
arg2 = sym('arg2','real'); ...
syms arg1 arg2 ... unreal is short-hand notation for
arg1 = sym('arg1','unreal');
arg2 = sym('arg2','unreal'); ...
Each input argument must begin with a letter and can contain only
alphanumeric characters.
Examples
syms x beta real is equivalent to:
x = sym('x','real');
beta = sym('beta','real');
To clear the symbolic objects x and beta of 'real' status, type
syms x beta unreal
Note clear x will not clear the symbolic object x of its status 'real'. You can
achieve this, using the commands syms x unreal or clear mex or clear all.
In the latter two cases, the Maple kernel will have to be reloaded in the
MATLAB workspace. (This is inefficient and time consuming).
See Also
2-118
sym
sym2poly
Purpose
sym2poly
Symbolic-to-numeric polynomial conversion.
Syntax
c = sym2poly(s)
Description
sym2poly returns a row vector containing the numeric coefficients of a symbolic
polynomial. The coefficients are ordered in descending powers of the
polynomial’s independent variable. In other words, the vector’s first entry
contains the coefficient of the polynomial’s highest term; the second entry, the
coefficient of the second highest term; and so on.
Examples
The commands
syms x u v;
sym2poly(x^3 – 2*x – 5)
return
1
0
–2
–5
while sym2poly(u^4 – 3 + 5*u^2) returns
1
0
5
0
–3
and sym2poly(sin(pi/6)*v + exp(1)*v^2) returns
2.7183
See Also
0.5000
0
poly2sym
polyval in the online MATLAB Function Reference
2-119
symsum
Purpose
symsum
Symbolic summation.
Syntax
r
r
r
r
Description
symsum(s) is the summation of the symbolic expression s with respect to its
symbolic variable k as determined by findsym from 0 to k–1.
=
=
=
=
symsum(s)
symsum(s,v)
symsum(s,a,b)
symsum(s,v,a,b)
symsum(s,v) is the summation of the symbolic expression s with respect to the
symbolic variable v from 0 to v–1.
symsum(s,a,b) and symsum(s,v,a,b) are the definite summations of the
symbolic expression from v=a to v=b.
Examples
The commands
syms k n x
symsum(k^z)
return
1/3*k^3–1/2*k^2+1/6*k
symsum(k) returns
1/2*k^2–1/2*k
symsum(sin(k*pi)/k,0,n) returns
–1/2*sin(k*(n+1))/k+1/2*sin(k)/k/(cos(k)–1)*cos(k*(n+1))–
1/2*sin(k)/k/(cos(k)–1)
symsum(k^2,0,10) returns
385
symsum(x^k/sym(‘k!'), k, 0,inf) returns
exp(x)
2-120
symsum
Note The preceding example uses sym to create the symbolic expression
k! in order to bypass MATLAB’s expression parser, which does not recognize
! as a factorial operator.
See Also
findsym, int, syms
2-121
taylor
Purpose
taylor
Taylor series expansion.
Syntax
r = taylor(f)
r = taylor(f,n,v)
r = taylor(f,n,v,a)
Description
taylor(f,n,v) returns the (n–1)-order Maclaurin polynomial approximation
to f, where f is a symbolic expression representing a function and v specifies
the independent variable in the expression. v can be a string or symbolic
variable.
taylor(f,n,v,a) returns the Taylor series approximation to f about a. The
argument a can be a numeric value, a symbol, or a string representing a
numeric value or an unknown.
You can supply the arguments n, v, and a in any order. taylor determines the
purpose of the arguments from their position and type.
You can also omit any of the arguments n, v, and a. If you do not specify v,
taylor uses findsym to determine the function’s independent variable. n
defaults to 6.
The Taylor series for an analytic function f(x) about the basepoint x=a is given
below.
∞
f( x ) =
∑
(n )
(a)
n f
( x – a ) ⋅ ----------------n!
n=0
Examples
This table describes the various uses of the taylor command and its relation
to Taylor and MacLaurin series.
Mathematical Operation
5
∑
n=0
2-122
(n)
(0)
n f
x ⋅ ----------------n!
MATLAB
syms x
taylor(f)
taylor
Mathematical Operation
m
∑
taylor(f,m)
(n)
f (0)
x ⋅ ----------------- , m is a positive integer
n!
n
MATLAB
m is a positive integer
n=0
5
∑
(n)
(a)
n f
( x – a ) ⋅ ----------------- , a is a real number
n!
taylor(f,a)
a is a real number
n=0
m1
∑
(n)
f ( m2 )
( x – m 2 ) ⋅ ---------------------n!
n
taylor(f,m1,m2)
m1, m2 are positive integers
n=0
m1, m2 are positive integers
m
∑
(n)
f (a)
( x – a ) ⋅ ----------------n!
n
taylor(f,m,a)
a is real and m is a positive
integer
n=0
a is real and m is a positive integer
In the case where f is a function of two or more variables (f=f(x,y,...)), there
is a fourth parameter that allows you to select the variable for the Taylor
expansion. Look at this table for illustrations of this feature.
Mathematical Operation
5
∑
n=0
n
n
MATLAB
taylor(f,y)
y
∂
------ ⋅ n f ( x, y = 0 )
n! ∂ y
2-123
taylor
Mathematical Operation
m
∑
n=0
n
n
y
∂
------ ⋅ n f ( x, y = 0 )
n! ∂ y
MATLAB
taylor(f,y,m) or
taylor(f,m,y)
m is a positive integer
m is a positive integer
m
∑
n=0
n
n
(y – a)
∂
-------------------- ⋅ n f ( x, y = a )
n!
∂y
taylor(f,m,y,a)
a is real and m is a positive
integer
a is real and m is a positive integer
5
∑
n=0
a is real
See Also
2-124
n
n
(y – a)
∂
-------------------- ⋅ n f ( x, y = a )
n!
∂y
findsym
taylor(f,y,a)
a is real
taylortool
Purpose
taylortool
Taylor series calculator.
Syntax
taylortool
taylortool('f')
Description
taylortool initiates a GUI that graphs a function against the Nth partial sum
of its Taylor series about a basepoint x = a. The default function, value of N,
basepoint, and interval of computation for taylortool are f = x*cos(x),
N = 7, a = 0, and [-2*pi,2*pi], respectively.
taylortool('f') initiates the GUI for the given expression f.
Examples
taylortool('exp(x*sin(x))')
taylortool('sin(tan(x)) – tan(sin(x))')
See Also
funtools, rsums
2-125
tril
Purpose
tril
Symbolic lower triangle.
Syntax
tril(X)
tril(X,K)
Description
tril(X) is the lower triangular part of X.
tril(X,K) returns a lower triangular matrix that retains the elements of X on
and below the k-th diagonal and sets the remaining elements to 0. The values
k=0, k>0, and k<0 correspond to the main, superdiagonals, and subdiagonals,
respectively.
Examples
Suppose
A =
[
a,
b,
c ]
[
1,
2,
3 ]
[ a+1, b+2, c+3 ]
Then tril(A) returns
[
a,
0,
0 ]
[
1,
2,
0 ]
[ a+1, b+2, c+3 ]
tril(A,1) returns
[
a,
b,
0 ]
[
1,
2,
3 ]
[ a+1, b+2, c+3 ]
triu(A,–1) returns
[
0,
0,
[
1,
0,
[ a+1, b+2,
See Also
2-126
diag, triu
0 ]
0 ]
0 ]
triu
Purpose
triu
Symbolic upper triangle.
Syntax
triu(X)
triu(X, K)
Description
triu(X) is the upper triangular part of X.
triu(X, K) returns an upper triangular matrix that retains the elements of X
on and above the k-th diagonal and sets the remaining elements to 0. The
values k=0, k>0, and k<0 correspond to the main, superdiagonals, and
subdiagonals, respectively.
Examples
Suppose
A =
[
a,
b,
c ]
[
1,
2,
3 ]
[ a+1, b+2, c+3 ]
Then triu(A) returns
[
[
[
a,
0,
0,
b,
c ]
2,
3 ]
0, c+3 ]
triu(A,1) returns
[ 0, b, c ]
[ 0, 0, 3 ]
[ 0, 0, 0 ]
triu(A,–1) returns
[
[
[
See Also
a,
b,
c ]
1,
2,
3 ]
0, b+2, c+3 ]
diag, tril
2-127
vpa
Purpose
vpa
Variable precision arithmetic.
Syntax
R = vpa(A)
R = vpa(A,d)
Description
vpa(A) uses variable-precision arithmetic (VPA) to compute each element of A
to d decimal digits of accuracy, where d is the current setting of digits. Each
element of the result is a symbolic expression.
vpa(A,d) uses d digits, instead of the current setting of digits.
Examples
The statements
digits(25)
q = vpa(sym(sin(pi/6)))
p = vpa(pi)
w = vpa('(1+sqrt(5))/2')
return
q =
.5000000000000000000000000
p = 3.141592653589793238462643
w =
1.618033988749894848204587
vpa pi 75 computes π to 75 digits.
The statements
A = vpa(hilb(2),25)
B = vpa(hilb(2),5)
2-128
vpa
return
A =
[
1., .5000000000000000000000000]
[.5000000000000000000000000, .3333333333333333333333333]
B =
[
1., .50000]
[.50000, .33333]
See Also
digits, double
2-129
zeta
zeta
Purpose
Riemann Zeta function.
Syntax
Y = zeta(X)
Y = zeta(n, X)
Description
zeta(X) evaluates the Zeta function at the elements of X, a numeric matrix, or
a symbolic matrix. The Zeta function is defined by
∞
ζ(w) =
∑
k=1
1
-----w
k
zeta(n, X) returns the n-th derivative of zeta(X).
Examples
zeta(1.5) returns 2.6124.
zeta(1.2:0.1:2.1) returns
Columns 1 through 7
5.5916
3.9319
3.1055
2.6124
Columns 8 through 10
1.7497
1.6449
1.5602
zeta([x 2;4 x+y]) returns
[
zeta(x), 1/6*pi^2]
[ 1/90*pi^4, zeta(x+y)]
diff(zeta(x),x,3) returns zeta(3,x).
2-130
2.2858
2.0543
1.8822
ztrans
Purpose
ztrans
z-transform.
Syntax
F = ztrans(f)
F = ztrans(f,w)
F = ztrans(f,k,w)
Description
F = ztrans(f) is the z-transform of the scalar symbol f with default
independent variable n. The default return is a function of z.
f = f( n ) ⇒ F = F( z)
The z-transform of f is defined as:
∞
F( z) =
f(n)
∑ ---------n
z
0
where n is f’s symbolic variable as determined by findsym. If
f = f(z), then ztrans(f) returns a function of w.
F = F(w)
F = ztrans(f,w) makes F a function of the symbol w instead of the default z.
∞
F( w) =
f(n)
∑ ---------n
w
0
F = ztrans(f,k,w) takes f to be a function of the symbolic variable k.
∞
F( w) =
f(k)
∑ --------k
w
0
2-131
ztrans
Examples
Z-Transform
f( n ) = n
MATLAB Operation
f = n^4
4
∞
Z[ f ] =
∑
f ( n )z
–n
returns
n=0
3
ztrans(f)
2
z ( z + 11z + 11z + 1 )
= --------------------------------------------------------5
(z – 1)
g( z ) = a
z*(z^3+11*z^2+11*z+1)/(z–1)^5
g = a^z
z
∞
Z[ g ] =
∑ g ( z )w
–z
z=0
ztrans(g)
returns
w
= -------------a–w
–w/(–w+a)
f ( n ) = sin an
f = sin(a*n)
∞
Z[ f ] =
∑
f ( n )w
–n
n=0
w sin a
= --------------------------------------------2
1 – 2w cos a + w
2-132
ztrans(f,w)
returns
sin(a)*w/(1–w*cos(a)+w^2)
ztrans
Z-Transform
f ( n, k ) = e
– kn
MATLAB Operation
2
cos kn
f = exp(k*n^2)*cos(k*n)
∞
Z[ f] =
∑ f ( n, k )x
–k
k=0
2
2
–n
x – ( e cos n )x
= ------------------------------------------------------------------2
2
2
–n
– 2n
x – ( 2e cos n )x + e
See Also
ztrans(f,k,x)
returns
x*(–exp(–n*2)*cos(n)+x)/
(–2*x*exp(–n^2)*cos(n) + x^2+
exp(–2*n^2))
fourier, iztrans, laplace
2-133
ztrans
2-134
A
Compatibility Guide
Compatibility with Earlier Versions . . . . . . . . . A-2
Obsolete Functions . . . . . . . . . . . . . . . . . A-3
A
Compatibility Guide
Compatibility with Earlier Versions
Earlier versions of the Symbolic Math Toolboxes work with version 4.0 or 4.1
of MATLAB and version V, release 2 of Maple. The goal was to provide access
to Maple with a language syntax that is familiar to MATLAB users. This was
been done without modifying either of the two underlying systems.
However, it is not possible to provide completely seamless integration without
modifying MATLAB. For example, if f and g are strings representing symbolic
expressions, we would prefer to use the notation f+g for their sum, instead of
symadd(f,g). But f+g attempts to add the individual characters in the two
strings, rather than concatenate them with a plus sign in between. Similarly,
if A is a matrix whose elements are symbolic expressions, we would prefer to
use A(i,j) to access a individual expression, instead of sym(A,i,j). But if A
is a matrix of strings, then A(i,j) is a single character, not a complete
expression.
This version of the Symbolic Math Toolboxes makes extensive use of the new
MATLAB object capabilities and works with Maple V, Release 4. For this
reason, it is not fully compatible with version 1 of the Symbolic Math Toolbox.
A-2
Obsolete Functions
Obsolete Functions
This version maintains some compatibility with version 1. For example, the
following obsolete functions continue to be available in version 2, though you
should avoid using them as future releases may not include them.
Function
Description
determ
Symbolic matrix determinant
linsolve
Solve simultaneous linear equations
eigensys
Symbolic eigenvalues and eigenvectors
singvals
Symbolic singular values and singular vectors
numeric
Convert symbolic matrix to numeric form
symop
Symbolic operations
symadd
Add symbolic expressions
symsub
Subtract symbolic expressions
symmul
Multiply symbolic expressions
symdiv
Divide symbolic expressions
sympow
Power of symbolic expression
eval
Evaluate a symbolic expression
In version 1, these functions accepted strings as arguments and returned
strings as results. In version 2, they accept either strings or symbolic objects as
input arguments and produce symbolic objects as results. version 2 provides
overloaded MATLAB operators or new functions that you can use to replace
most of these functions in your existing code.
For example, the version 1 statements
f = '1/(5+4*cos(x))'
g = int(int(diff(f,2)))
e = symsub(f,g)
simple(e)
A-3
A
Compatibility Guide
continue to work in version 2. However, with version 2, the preferred approach
is
syms x
f = 1/(5+4*cos(x))
g = int(int(diff(f,2)))
e = f - g
simple(e)
The version 1 statements
H = sym(hilb(3))
I = sym(eye(3))
X = linsolve(H,I)
t = sym(0)
for j = 1:3
t = symadd(t,sym(X,j,j))
end
t
continue to work in version 2. However, the preferred approach is
H
I
X
t
=
=
=
=
sym(hilb(3))
eye(3)
H\I
sum(diag(X))
You can no longer use the sym function in this way.
M = sym(3,3,'1/(i+j–t)')
Instead, you must change the code to something like this
syms t
[J,I] = meshgrid(1:3)
M = 1./(I+J–t)
As in version 1, you can supply diff, int, solve, and dsolve with string
arguments in version 2. In version 2, however, these functions return symbolic
objects instead of strings.
For some computations, the new release of Maple produces results in a
different format.
A-4
Obsolete Functions
For example, with version 1, the statement
[x,y] = solve('x^2 + 2*x*y + y^2 = 4', 'x^3 + 4*y^3 = 1')
produces
x =
[
–RootOf(_Z^3–2*_Z^2–4*_Z–3)–2]
[–RootOf(3*_Z^3+6*_Z^2–12*_Z+7)+2]
y =
[
RootOf(_Z^3–2*_Z^2–4*_Z–3)]
[RootOf(3*_Z^3+6*_Z^2–12*_Z+7)]
The same statement works in version 2, but produces results with the RootOf
expressions expanded to exhibit the multiple solutions.
A-5
A
Compatibility Guide
A-6
Index
Symbols
right division
array 2-8
matrix 2-8
subtraction 2-8
transpose
array 2-9
matrix 2-9
- 2-8
* 2-8
+ 2-8
.* 2-8
./ 2-8
.<bd> 2-8
.^ 2-9
.’ 2-9
/ 2-8
@sym directory 1-16
B
\ 1-71, 1-72
backslash operator 1-71
beam equation 1-115
Bernoulli polynomials 1-130
Bessel functions 1-114, 1-131
differentiating 1-18
integrating 1-26
besseli 1-114
besselj 1-18
besselk 1-110, 1-113, 1-114
beta function 1-131
binomial coefficients 1-130
branch cut 1-46
\<bd> 2-8
^ 2-9
’ 2-9
A
abstract functions 1-10
Airy differential equation 1-110, 1-113
Airy function 1-110, 1-130
algebraic equations
solving 2-109
all 2-29
arithmetic operations 2-8-2-9
left division
array 2-8
matrix 2-8
matrix addition 2-8
multiplication
array 2-8
matrix 2-8
power
array 2-9
matrix 2-9
C
calculus 1-17-1-46
ccode 2-11
characteristic polynomial 1-75, 1-77, 2-97
Chebyshev polynomial 1-132
circuit analysis
using the Laplace transform for 1-120
circulant matrix 1-11, 1-59
clear 1-28
clearing variables
in the Maple workspace 1-28
in the MATLAB workspace 1-28, 2-118
I-1
Index
collect 1-48, 2-12
E
colspace 1-73, 2-13
eig 1-74, 2-25
column space 1-73
complementary error function 1-131
complex conjugate 2-15
complex number
imaginary part of 2-66
real part of 2-102
complex symbolic variables 1-9
eigenvalue trajectories 1-86-1-95
eigenvalues 1-74-1-80, 1-87, 2-25
computing 1-74
eigenvectors 1-75
elliptic integrals 1-131
eps 1-8
error function 1-131
Euler polynomials 1-130
expand 1-49, 2-28
compose 2-14
conj 1-10, 2-15, 2-116
converting symbolic matrices to numeric form
1-9
cosine integral function 2-16
cosine integrals 1-131
cosint 2-16
exponential integrals 1-131
Extended Symbolic Math Toolbox 1-3, 1-131,
1-143-1-148, 2-93, 2-100
ezplot 1-34
D
F
Dawson’s integral 1-131
decimal symbolic expressions 1-8
default symbolic variable 1-13
definite integration 1-25
factor 1-50, 2-47
det 2-17
finverse 2-49
diag 2-18
floating-point arithmetic 1-64
floating-point symbolic expressions 1-7
format 1-64
diff 1-17, 2-20
difference equations
solving 1-126
differentiation 1-17-1-21
diffraction 1-132
digamma function 1-131
digits 1-9, 2-21
Dirac Delta function 1-115, 1-131
discontinuities 1-45
discrim 1-93
double 1-67, 2-22
dsolve 1-108, 1-114, 2-23
I-2
expm 2-27
factorial function 1-11
factorial operator 2-121
findsym 1-14, 2-48
fortran 2-50
fourier 2-51
Fourier transform 1-112-1-119, 2-51
Fresnel integral 1-131
function calculator 2-54
functional composition 2-14
functional inverse 2-49
funtool 2-54
Index
G
gamma function 1-131
Gegenbauer polynomial 1-131
generalized hypergeometric function 1-131
Givens transformation 1-69, 1-78
golden ratio 1-6
H
harmonic function 1-131
Heaviside function 1-117, 1-131
Hermite polynomial 1-131
Hilbert matrix 1-9, 1-70
horner 1-49, 2-57
hyperbolic cosine function 1-131
hyperbolic sine function 1-131
hypergeometric function 1-131
inverse Laplace transform 2-63
inverse z-transform 2-70
iztrans 2-70
J
Jacobi polynomial 1-132
jacobian 1-20, 2-72
Jacobian matrix 1-20, 2-72
jordan 1-81, 2-73
Jordan canonical form 1-81-1-82, 2-73
L
Laguerre polynomial 1-132
Lambert’s W function 1-131, 2-75
lambertw 2-75
laplace 2-76
Laplace transform 1-120-1-125, 2-76
I
latex 2-79
IEEE floating-point arithmetic 1-65
ifourier 1-113, 2-60
left division
array 2-8
matrix 2-8
Legendre polynomial 1-132
limit 1-21, 2-80
limits 1-21-1-23
two-sided 1-22
undefined 1-22
linear algebra 1-69-1-95
logarithm function 1-131
logarithmic integral 1-131
ilaplace 2-63
imag 2-66
incomplete gamma function 1-131
initializing the Maple kernel 2-84
initstring variable 2-84
int 1-23, 2-67
integral transforms 1-112-1-129
Fourier 1-112-1-119
Laplace 1-120-1-125
z-transform 1-126-1-129
integration 1-23-1-29
definite 1-25
with real constants 1-26
inv 2-68
inverse Fourier transform 2-60
M
machine epsilon 1-8
MacLaurin series 1-31
Maple 1-2
I-3
Index
maple 2-81
N
output argument 1-141
Maple functions
accessing 1-11, 1-136-1-142
Maple help 2-94
Maple kernel
accessing 2-81
initializing 2-84
Maple library 2-84
Maple Orthogonal Polynomial Package 2-93
Maple packages 1-143
loading 1-144
Maple procedure 1-143, 2-100
compiling 1-148
installing 2-100
writing 1-145-1-147
null 1-72, 2-95
mapleinit 2-84
poly2sym 2-98
matrix addition 2-8
matrix condition number 1-72
matrix diagonal 2-18
matrix exponential 2-27
matrix inverse 2-68
matrix lower triangle 2-126
matrix rank 2-101
matrix size 2-108
matrix upper triangle 2-127
M-file
creating 1-16
mfun 1-130, 2-85
polygamma function 1-131
polynomial discriminants 1-93
power
array 2-9
matrix 2-9
pretty 1-32, 2-99
procread 1-146, 2-100
prod 1-11
mfunlist 2-86
mhelp 2-94
multiplication
array 2-8
matrix 2-8
I-4
null space 1-72
null space basis 2-95
numden 2-96
numeric symbolic expressions 1-7
O
ordinary differential equations
solving 2-23
orthogonal polynomials 1-131, 2-93
P
poly 1-75, 2-97
R
rank 2-101
rational arithmetic 1-65
rational symbolic expressions 1-7
real 2-102
real property 1-9
real symbolic variables 1-9, 1-28
reduced row echelon form 2-103
Riemann sums
evaluating 2-104
Index
Riemann Zeta function 1-130, 2-130
right division
array 2-8
matrix 2-8
Rosser matrix 1-76, 1-77
rref 2-103
rsums 2-104
S
shifted sine integral 1-131
simple 1-52, 2-105
simplifications 1-47-1-55
simplify 1-52, 2-106
simultaneous differential equations
solving 1-109-1-110, 1-122
simultaneous linear equations
solving systems of 1-71, 1-106
sine integral function 2-107
sine integrals 1-131
singular value decomposition 1-82-1-86, 2-114
sinint 2-107
solve 1-96, 2-109
solving equations 1-96-1-111
algebraic 1-96-1-107, 2-109
difference 1-126
ordinary differential 1-107-1-111, 2-23
special functions 1-130-1-135
evaluating numerically 2-85
listing 2-86
spherical coordinates 1-19
subexpr 1-56, 2-111
subexpressions 1-56-1-58
subs 1-59, 2-112
substituting in symbolic expressions 2-112
substitutions 1-56-1-63
subtraction 2-8
summation
symbolic 1-30
svd 1-82, 2-114
sym 1-5, 1-6, 1-7, 1-9, 1-11, 1-28, 2-116
sym2poly 2-119
symbolic expressions 1-96
C code representation of 2-11
creating 1-6
decimal 1-8
differentiating 2-20
expanding 2-28
factoring 2-47
finding variables in 2-48
floating-point 1-7
Fortran representation of 2-50
integrating 2-67
LaTeX representation of 2-79
numeric 1-7
prettyprinting 2-99
rational 1-7
simplifying 2-105, 2-106, 2-111
substituting in 2-112
summation of 2-120
taking a limit of 2-80
Taylor series expansion of 2-122
symbolic math functions
creating 1-15
symbolic math programs
debugging 1-141
writing 1-136-1-140
Symbolic Math Toolbox
compatibility with earlier versions A-2
demo 1-5
obsolete functions A-3
symbolic matrix
computing eigenvalue of 1-77
coverting to numeric form 1-9
I-5
Index
creating 1-11
differentiating 1-19
symbolic objects
about 1-5
creating 2-116, 2-118
symbolic polynomials
converting to numeric form 2-119
creating from coefficient vector 2-98
Horner representation of 2-57
symbolic summation 1-30-??
symbolic variables
clearing 2-118
complex 1-9
creating 1-6
default 1-13
real 1-9, 1-28
syms 1-7, 2-118
symsize 2-108
symsum 1-30, 2-120
T
taylor 1-31, 2-122, 2-122
Taylor series 1-31
Taylor series expansion 2-122
taylortool 2-125
Tony’s trick 1-84
trace mode 1-141
transpose
array 2-9
matrix 2-9
tril 2-126
triu 2-127
U
unreal property 1-10
I-6
V
variable-precision arithmetic 1-64-1-68, 2-128
setting accuracy of 2-21
variable-precision numbers 1-66
vpa 1-66, 2-128
Z
zeta 2-130
ztrans 2-131
z-transform 1-126-1-129, 2-131
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