User manual | Computing Complex Singularities of Differential Equations with Chebfun

COMPUTING COMPLEX SINGULARITIES OF DIFFERENTIAL
EQUATIONS WITH CHEBFUN
AUTHOR: MARCUS WEBB∗ AND ADVISOR: LLOYD N. TREFETHEN†
Abstract. Given a solution to an ordinary differential equation (ODE) on a time interval, the
solution for complex-valued time may be of interest, in particular whether the solution is singular at
some complex time value. How can the solution be approximated in the complex plane using only the
data on the interval? A polynomial approximation of the solution always fails to capture singularities;
to extrapolate solutions with singularities, approximation with rational functions is more appropriate.
In this paper, a robust form of rational interpolation and least-squares approximation, due to Pachón,
Gonnet et al., is discussed and tested. It is found that the method avoids the issue of spurious poles
found by many standard rational approximations, but that it is not suitable when a high degree of
accuracy is required.
Key words. Complex singularities, ordinary differential equations, numerical analytic continuation, rational interpolation, Lorenz attractor, Lotka-Volterra, three-body problem.
1. Introduction. Recent decades have seen increased scientific interest in numerous questions associated with the location of complex singularities of differential
equations. For example, the Painlevé equations, whose solutions have many complex
singularities, are growing in importance due to the long list of problems described by
them: the scattering of neutrons off heavy nuclei, the statistics of the zeros of the
Riemann zeta function on the critical line Re(z) = 1/2 and, amongst many others, random matrix theory [3]. As a result, there have been some interesting publications on
the numerical methodology and mathematical analysis for such problems [7], [21], [23].
In this paper we explore a central problem in the area, analytic continuation.
Suppose one has solved an ordinary differential equation (ODE) on a time interval, and
the solution for complex-valued time is of interest, in particular whether the solution
is singular at some complex time value. How can the solution be approximated in the
complex plane using only the data on the interval?
Let f : [0, T ] → R be an analytic function solving a given ODE problem, and let
G ⊂ C be an open, connected domain in the complex plane, containing the interval
[0, T ]. An analytic function f˜ → C is an analytic continuation of f if f˜ is analytic
in G and f˜ |[0,T ] = f . For any analytic f , there exists an analytic continuation to
some open set containing [0, T ] by evaluation of the Taylor series inside their discs of
convergence, and by the identity theorem in complex analysis, analytic continuations
to connected open sets are unique. We extend the definition to include the case where
f˜ has a singularity at t0 (meaning f˜ is not analytic) if we can analytically continue f
to any set G which is missing arbitrarily small discs centered at t0 .
If you take a polynomial interpolant p of f on points in [0, T ], and evaluate p in the
complex plane, as an entire function it could not possibly capture any singularities in
f˜. Another issue with polynomial approximations is that they necessarily blow up as
you go out towards infinity in the complex plane. However, a polynomial interpolant
on Chebyshev points, scaled and shifted to [0, T ], is accurate in the Bernstein ellipse
associated with f˜ and [0, T ]. For the interval [−1, 1] the boundary of the Bernstein
∗ Cambridge Centre for Analysis, Wilberforce Road, Cambridge, CB3 0WA, UK
([email protected], http://damtp.cam.ac.uk/people/mdw42).
† Mathematical Institute, Oxford University, Oxford OX1 3LB, UK
([email protected], http://people.maths.ox.ac.uk/trefethen/).
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ellipse associated with f˜ is described by
Eρ (θ) =
1
ρe2πiθ + ρ−1 e−2πiθ ,
2
θ ∈ [0, 2π),
(1.1)
where ρ is taken to be as large as possible so that no singularities of f˜ lie inside the
ellipse. For arbitrary intervals, the ellipse is scaled and translated. By accurate, we
mean that the interpolants converge geometrically to f˜ inside Eρ ([0, 2π)) [19, Ch.8].
Rational functions are those that can be written as a quotient: r(z) = p(z)/q(z)
where p and q are polynomials. Approximations using rational functions are well
suited to numerical analytic continuation for singular functions, because a rational
function has a singularity at each root of q [19, Ch. 23]. Padé approximants, based
on matching the first few terms of the Taylor series of the function we wish to approximate, are the most well known example of rational approximants [1]. Common
rational approximation methods have problems, essentially due to the fact that they
have as many singularities as the denominator q has roots. As will be explained
more in section 3, only exceptional methods do not produce unwanted singularities,
even in exact arithmetic. However, there have been developments in algorithms for
rational interpolation that are claimed to overcome this issue. Two publications in
2011, Gonnet et al. [9] and Pachón et al. [16], stemming from research in Pachón’s
D.Phil. thesis (2010) [15], describe an algorithm for rational interpolation that is fast,
stable and robust.
In this paper we investigate the use of the algorithm in a straightforward method:
First solve the ODE numerically on [0, T ], then use numerical analytic continuation to
extend the solution into the complex plane with a rational function whose singularities
can be found and analysed. This follows an investigation by Weideman [23], although
his study had a different emphasis: the equations he considered had solutions in two
variables u(x, t), where x is a spatial variable and t is time, with the aim to compute
singularities of u for complex x and observe their dynamics as t varies.
The implementation of the method is in Chebfun (www.maths.ox.ac.uk/chebfun),
an open-source software project led by Nick Trefethen and Nick Hale at Oxford University and Toby Driscoll of the University of Delaware. Chebfun is an extension of
Matlab which overloads common vector and matrix operations to manipulate functions and operators. The intention is that the commands should feel symbolic, as if
we were working with the actual functions, but that the underlying computations are
numeric and therefore fast. Experience with Matlab or Chebfun is not necessary to
understand or appreciate the results in this paper.
There is good reasoning behind the use of this particular software package, more
than just mere convenience. Polynomial interpolation in Chebyshev points is extremely reliable for smooth functions [18], whereas analytic continuation is in fact
an ill-posed problem (small changes in function data can cause large changes in the
continuation). As we shall see in the next section, simply the degree of the Chebyshev interpolant produced by Chebfun (called a chebfun) gives us an estimate for ρ,
the parameter of the Bernstein ellipse associated with the underlying function, and
appropriate degrees for p and q in our rational approximation.
In Section 2 we discuss in detail the proposed method mentioned above. Section 3
concerns the results of preliminary experiments using the robust rational interpolation
algorithm, leading to heuristics for parameters of the method such as the degrees of
p and q. The main section is Section 4, in which we illustrate the use of the method
on some ODE problems that are fascinating in their own right. Section 5 is devoted
to discussion, conclusions, and possibilities for further work in this area.
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2. The Proposed Method. To solve the ODEs on [0, T ] we use the chebop
methods built into Chebfun because they are naturally suited for returning a chebfun
as a numerical solution [6]. However, for some more complicated initial value problems, for example the Lorenz attractor, this approach can fail to converge without a
good initial guess for the solution. In this case we will use Chebfun’s overload of the
Matlab command ode113.
We will denote the underlying analytic solution to the ODE by f : C \ {tj }j∈J →
C, with singularity set {tj }j∈J , and our numerically computed chebfun solution by
u : [0, T ] → R. Note that u is a polynomial, whose degree (denoted N ) is calculated
automatically in Chebfun. The degree can be loosely described to be such that any
computed solution with higher degree would have to have some coefficients that are
zero up to machine precision, but for a better understanding see the chebfun guide
on the website, or the original chebop paper [6].
For the analytic continuation step, we ask for a rational function, r = p/q of type
(m, n) (i.e. the degrees of p and q are m and n respectively) to approximate f , using
u. To do this we use the Chebfun command ratinterp, which is a robust implementation of linearised rational interpolation. Here we will explain mathematically how
ratinterp works, based on the algorithms described in [9] and [16].
Let {x0 , . . . , xN } be the set of Chebyshev points on [−1, 1], xj = cos( jπ
N ). The
rational interpolation problem for f on x = (x0 , . . . , xN )> is as follows: Find a rational
function r of type (m, n), such that m + n = N and
r(x) = f (x),
(2.1)
where r(x) gives the vector with elements r(xj ) for each j. A solution does not always
exist: consider the type (1, 1) interpolant r such that r(±1) = 0 and r(0) = 1. But
when it does exist it is unique, as can be checked by elementary methods.
To avoid the problem of nonexistence, we must take a more general approach.
Consider the following bilinear form on continuous functions:
hf, giN =
N
2 X 00
f (xi )g(xi ).
N i=0
Here the 00 indicates having the first and
on the space ΠN of polynomials of degree
following orthogonality property:

 2
1
hTk , Tl iN =

0
(2.2)
last entries halved. It is an inner product
less than or equal to N , and it satisfies the
if k = l = 0, N,
if k = l 6= 0, N,
if k 6= l.
(2.3)
Here Tk is the Chebyshev polynomial of degree k, defined by Tk (x) = cos(k cos−1 (x))
for x ∈ [−1, 1]. Consequently, (2.3) can be proved using trigonometric identities.
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Now, define k · kN = (h·, ·iN ) 2 , and consider finding p ∈ Πm and q ∈ Πn such that
minimise kp − f qkN such that kqkN = 1.
(2.4)
The normalisation of q is necessary to avoid the trivial solution p = q = 0. Any
solution r = p/q to (2.1) can be made to satisfy (2.4). Conversely, we will see by
the following linear algebra that if m + n = N , then (2.4) has a solution such that
kp − f qkN = 0, called a linearised solution. Furthermore, (2.4) can be used to define
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more general solutions in the case that n + m < N , called linearised least-squares
solutions for the rational interpolation problem [8]. The notion of a least-squares
problem is a general one, in which the objective is to minimise a sum of squares.
Now we will see why the specific bilinear form was chosen. Let â = (â0 , . . . , âN )>
and b̂ = (b̂0 , . . . , b̂N )> define polynomials p̂ and q̂ as follows:
p̂(x) =
N
X
00
âk Tk (x),
q̂(x) =
j=0
N
X
00
b̂k Tk (x).
(2.5)
j=0
Then kp̂kN = kâk2 , kq̂kN = kb̂k2 and so any linear problem in the values of p̂
and q̂ can be restated as a linear problem in â and b̂. The transformation from
coefficient space to value space can be stated as p̂(x) = CI 00 â, and q̂(x) = CI 00 b̂
00
where C = (Tj (xi ))N
i,j=0 , and I is the identity matrix with the top-left and bottomright entries halved. By the orthogonality relation (2.3), N2 C > I 00 CI 00 = I and so if
p̂(x) = (f · q̂)(x) then we have
â =
2 > 00 00
2
C I CI â = C > I 00 F CI 00 b̂,
N
N
(2.6)
where F is diagonal matrix with diagonal entries f (x).
Now let a and b be vectors containing the first m + 1 and n + 1 rows of â and b̂
respectively, and let ã and b̃ be vectors containing the last N − m and N − n rows
of â and b̂ respectively. Let q ∈ Πn be the truncation of q ∈ ΠN (i.e. set b̃ = 0) and
suppose that p and q solve the linearised least squares problem (2.4). Then since by
definition of k · kN we have
kp − f qkN = kp − p̂kN ,
(2.7)
where p̂ ∈ ΠN interpolates f · q, minimality implies that p must be the truncation of
p̂ (i.e. ã = 0 for p). Therefore, we have kp − f qkN = kãk2 .
Now define Ẑ = N2 C > I 00 F CI 00 , Z̃ to be the matrix consisting of the first n + 1
columns and last N − m rows of Ẑ, and Z to be the matrix consisting of the first
n + 1 columns and first m + 1 rows of Ẑ. Then we have that
â = Ẑ b̂,
a = Zb,
ã = Z̃b.
(2.8)
Minimality of kãk2 over all q ∈ Πn implies that b must be made to minimise kZ̃bk2 .
Therefore, in order for a and b to be a solution for (2.4) it suffices to take b to
minimise kZ̃bk2 , then set a = Zb. The residual kp − f qkN is exactly kZ̃bk2 .
If n + m = N , then Z̃ is an n × (n + 1) matrix, so has a non-zero kernel. Hence
there exists a vector b with kZ̃bk2 = 0, and the associated polynomials p and q
exactly solve the linearised rational interpolation problem.
In the general case where n+m ≤ N , we can use the Singular Value Decomposition
(SVD [20]) of Z̃ to find the minimal singular value σmin of Z̃ and associated singular
vector b such that σmin = kZ̃bk2 , and indeed this is how ratinterp solves the
linearised least squares problem (2.4). For a matrix A ∈ Rm×n , its singular value
decomposition is
A = U ΣV > ,
(2.9)
where U is an m × m orthogonal matrix whose columns are eigenvectors of AA> , V
is an n × n orthogonal matrix whose columns are eigenvectors of A> A, and Σ is an
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m × n diagonal matrix, whose diagonal entries are the square roots of the eigenvalues
of A> A or AA> , whichever is the smaller set. These diagonal entries are called the
singular values of A, and we take the decomposition so that they are ordered from
left to right in Σ, greatest (σmax ) to smallest (σmin ). By expanding x in the ordered
orthonormal basis {v1 , . . . , vn } consisting of the columns of V , we see that kAxk2
achieves its maximum σmax at x = v1 and its minimum σmin at x = vn . Matlab has
the command svd for its computation.
2
00
> 00
Noting that Tj (xi ) = cos( ijπ
N ), multiplication by CI and N C I can be interpreted as taking the discrete cosine transform (DCT) and inverse discrete cosine transform (iDCT) respectively. The Fast Fourier Transform (FFT) is used by ratinterp
to efficiently compute the DCT and iDCT when computing Z̃.
This is not the end of the story because we have not discussed uniqueness. If
the minimal singular value σmin is multiple, of multiplicity d, say, then there exist d
linearly independent vectors b such that kZ̃bk2 = σmin , corresponding to d different
polynomials q. This case corresponds to p and q potentially sharing d roots. In
exact arithmetic these will cancel, but on a computer they almost certainly will not,
producing a zero-pole pair known as a Froissart doublet.
By taking an appropriate linear combination, there exists a b with the last d − 1
entries zero, corresponding to a q ∈ Πn−(d−1) . From a robustness point of view, it
is better to take this b because it will reduce the number of poles in r, reducing the
chance of a Froissart doublet. It would be possible to use linear algebra to find this
b, but it is simpler to reduce n by d − 1 (keeping N and m the same) and start
the procedure again. This process can be repeated, giving the algorithm a unique
solution.
This idea can be taken further to improve robustness when implemented on a
computer. The user sets a tolerance parameter tol, and if there are d singular values
within tol of σmin , then n is decreased by d − 1. The resulting p and q have their
degrees reduced even further by discarding the higher coefficients smaller than tol in
absolute value.
Fig. 2.1. ratinterp is used to compute two type (30, 30) rational approximants of f (x) =
x/(1 + 25x2 ) on [−1, 1] with tol = 0 (left) and tol = 10−14 (right), returning (30, 30) and
type (1, 2) approximants respectively. The zeros of r are plotted as empty black circles and
the poles are plotted as coloured dots by the colour scale in the table below. We emphasise
that this example is typical and not exceptional.
|Residue|
Colour
(0, 10−14 )
red
[10−14 , 10−12 )
pink
[10−12 , 10−9 )
light green
5
[10−9 , 10−6 )
green
[10−6 , 10−3 )
light blue
[10−3 , ∞)
blue
The tolerance parameter is by default 10−14 , which in practice works well when
approximating functions accurate to machine precision (around 10−16 ), but if you
know that f contains noise of magnitude , then tol = 100 is a good suggestion for
the tolerance. If the tolerance is set at a value smaller than this, then ratinterp
will futilely use this noise for its approximation. We will use tol = 10−12 since u is
computed numerically as the solution to an ODE and will therefore not be accurate to
machine precision: there will be noise in u with magnitude between 10−13 and 10−12 .
In Figure 2.1, the plotted results follow the style of [9] where the poles are coloured
dots with their colour decided by their residue according to the table below. Notice
the appearance of Froissart doublets when tol = 0, where coloured dots (poles) have
an empty black circle (zero) in (almost) the same place.
The ratinterp algorithm delivers a rational function of type (µ, ν) where 0 ≤
µ ≤ m, 0 ≤ ν ≤ n. We take the roots of q to approximate the singularities of f .
ratinterp can also calculate the residues of the poles, which is implemented using
Matlab’s residue command [5].
Chebfun’s ratinterp is not restricted to using the Chebyshev points on [−1, 1]
for the nodes. The method explained above generalises to arbitrary points in the
complex plane, and the user can specify whatever nodes they want ratinterp to use.
The general approach for the linearised interpolation problem is discussed in [16].
The case where the nodes are the N th roots of unity is particularly elegant because
the polynomials satisfying a discrete orthogonality relation are simply the monomials
(powers of roots of unity make up the Fourier basis). The linearised least squares
problem and robustness steps for the case of roots of unity can be found in [9].
As well as the rational approximant of u, we can compute the Chebfun ellipse
associated with the chebfun u. The Chebfun ellipse is the Bernstein ellipse with ρ
parameter such that ρN = where is machine precision (approximately 10−16 ).
This ellipse is a good estimate for the Bernstein ellipse associated with f , discussed in
Section 1 [19, Ch.8]. This estimate is useful because at least one singularity of f lies
on the edge of the Bernstein ellipse and poles deep inside the ellipse must be spurious.
Recall that we denote the degree of the chebfun solution to the ODE problem u
by N , and this integer is computed automatically by Chebfun. Using few than N + 1
nodes for our rational approximation would not be utilising all the information we
have about u, and using more than N + 1 nodes would be approximating data that
has been interpolated from N + 1 nodes anyway. Therefore we perform our rational
approximation on N + 1 nodes if we are approximating a chebfun of degree N .
In summary, we perform the following list of operations as our proposed method:
Solve the ODE problem using chebops or ode113 on [0, T ], producing a chebfun u of
degree N ; compute a rational interpolant r = p/q from u on N + 1 Chebyshev points
in [0, T ]; compute the locations of the poles by finding the roots of q and compute
their residues using residue; and compute the Chebfun ellipse associated for u to
estimate the Bernstein ellipse for f .
3. Preliminary Experiments. A key question to ask when computing rational
approximants is, what are good choices for m and n? This turns out to be rather
troublesome. The following quote is from Padé Approximants by G.A. Baker and
P.R. Graves-Morris [1].
“In practice, whether one expects them or not, defects occur for all
but the simplest functions.”
Here, defect is synonymous with the spurious poles mentioned in the previous section.
Baker and Graves-Morris are referring to Padé approximants rather than the rational
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interpolation and least-squares approximants considered in this paper, but the principle still applies (see Figure 2.1). For the case of robust rational interpolation on
the unit circle, this issue is discussed in [9, sections 4 and 6]. For this project, the
author performed some preliminary experiments with robust rational least-squares
approximation on an interval.
Fig. 3.1. Type (m, 2) rational approximants computed with ratinterp for various chebfuns on [−2, 2], for m in the range [0, N − 2] (where N is the degree of the chebfun). Each
function has a singularity at ±i, the top two having simple poles and the bottom two having
branch points. We plot the error in: r on [−2, 2], computed location of singularities, and (in
the case of poles) the computed residue at the poles.
In the preliminary experiments, the author found that the behaviour of ratinterp
depended heavily on N , the degree of the chebfun being approximated. N is a good
indication of how complicated f is on [0, T ] and therefore indicative of how complicated good rational approximations of f will be. The experiments agreed with this
line of argument and the author recommends the following for m and n: take m = 12 N
and take n to be greater than the number of singularities you expect r to have. This
will not always give the best results; it depends on the number of singularities and
their types. Branch points usually require a higher value of m to be approximated
well and simple poles require a lower value. This is demonstrated in Figure 3.1: the
bottom two plots have better accuracy in both r and the location of the singularities
to the right of m = 40, whereas the top two plots have better accuracy to the left of
m = 40.
In experiments, when m is taken in the range of 13 N and 32 N , Froissart doublets
and spurious poles are avoided. In some cases meaningful poles are removed, but this
appears to be the only real issue encountered. A situation where this occurs is when m
is set too high, robust ratinterp removes all of the poles from the approximant and
returns a polynomial. An entire function is not helpful if we want to find singularities!
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We see this in all four of the plots in Figure 3.1, because the red and blue lines, which
rely on the existence of singularities, do not extend as far as the black lines, which
only rely on the existence of a rational approximant.
Ideally, we would state and prove theorems that explain results such as those
illustrated in Figure 3.1, but at present, little is understood about the robust leastsquares algorithm. Can the relationship between the SVD of the linearised problem
and the singularities of the resulting rational approximant be made precise? A more
specific question is how high m must be for the robust algorithm to remove all of
the poles from the approximant. The rest of this article is an investigation into what
ratinterp is capable of for the computation of singularities of differential equations,
as a first step towards the required understanding.
4. ODEs under Investigation. This is the main section of the paper. Here we
discuss some specific ODEs with results showing computations of complex singularities
for their solutions. The first two examples are ones with straightforward explicit
solutions, and the last three are some examples from applied mathematics that are
interesting in their own right.
4.1. Simple Poles. The following nonlinear ODE (with appropriate boundary
conditions) has solution tanh(α + βx) for arbitrary constants α, β:
df
d2 f
+ 2βf
= 0.
2
dx
dx
(4.1)
We used Chebfun to solve (4.1) with α = 0, β = 1 on the domain [−5, 5] and
boundary conditions f (±5) = tanh(±5). The numerically computed solution u is
a chebfun of degree N = 105, so following the strategy described in the previous
two sections we used ratinterp to generate a type (b 21 N c, 10) = (52, 10) rational
approximant r on the 106 Chebyshev points in [−5, 5]. ratinterp returned a type
(31, 2) approximant which appears in Figures 4.1 and 4.2.
Table 4.1
Error Data for Rational Approximation of tanh(z)
Quantity
u on [−5, 5]
r on [−5, 5]
poles(r)
residues(r)
Max Error
1.0 × 10−12
7.1 × 10−12
4.0 × 10−8
2.4 × 10−7
Mean Error
2.0 × 10−13
1.4 × 10−12
4.0 × 10−8
2.4 × 10−7
When f (x) = tanh(x) is extended into the complex plane it has simple poles at
(2j + 1) π2 i for each j ∈ Z, each with residue 1. In Figure 4.1 and Table 4.1 we can see
that just using our heuristic from Section 3 for the values of m and n, we get fairly
accurate results for the locations of the poles and residues. If one decreases m, r can
approximate the zeros at ±πi and even the poles further out at ± 3π
2 i, but accuracy
of the approximation on [−5, 5] is sacrificed.
In Figure 4.2, bottom right, we can see that the rational approximation is only
accurate in a small oval around the interval, a barrier beyond which r blows up
polynomially (of very high degree). Nonetheless, if we are only interested in the
nearest singularities to the real line this small window of meaningful continuation is
satisfactory. The same cannot be said for the chebfun u, which blows up quickly
outside of the Bernstein ellipse (bottom left).
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Fig. 4.1. Schematic of the numerical analytic continuation of the solution to (4.1) on
[−5, 5], showing simple poles at ± π2 i (blue dots) and a zero at 0 (circle). The Chebfun ellipse,
computed by Chebfun, is slightly larger than the actual Bernstein ellipse associated with tanh.
Fig. 4.2. Contour plots of the absolute value of tanh(z) (top), the chebfun numerical
solution to (4.1) u (left) and its ratinterp approximant r (right). The contours are coloured
from blue to red on [0, 5]. The chebfun u is useless as an approximation of tanh(z) outside
of the Bernstein ellipse whereas the ratinterp approximant r reaches much further.
4.2. Logarithmic Branch Points. The following example is a first order ODE
with solution f (t) = log(1+(t−γ)2 ) for an arbitrary constant γ (given an appropriate
initial value):
df
+ 2(γ − t) exp(−f ) = 0.
dt
9
(4.2)
The exponential term in (4.2) may seem contrived, but an example from combustion
theory, the time independent Frank-Kamenetskii blowup equation, takes a similar
form: d2 f /dx2 + A exp(f ) = 0 [17].
Fig. 4.3. 3D plot of the absolute value of f (z) = log(1 + (z − 6)2 ) on [0, 10] × [−6, 6] ⊆
C. The plot is coloured by argument ranging from −π to π. We can clearly see the two
logarithmic singularities and the branch cuts behind them, across which the argument of f is
discontinuous.
We consider the case γ = 6, for which we have plotted the surface generated by
the absolute value of the analytical solution. The surface is coloured by the argument
of the complex value in order to see that along the branch cuts the solution is not
continuous. We solved (4.2) using chebops on the interval [0, 10] with initial data
f (0) = log(37). The solution u has degree N = 137 so as in the previous example, we
used this information and computed a type (b 21 N c, 10) = (68, 10) rational approximant r on 138 Chebyshev points in [0, 10]. ratinterp returned a type (57, 4) rational
function with poles at t = 6.0017 ± 1.0429i, 6.0109 ± 1.2515i.
This is typical of rational approximants; in practice branch cuts are approximated
by lines of poles in the rational function as in Figure 4.4 and the actual location of
the branch point itself is not very accurate (see Table 4.2).
Table 4.2
Error data for rational approximation of the solution to (4.2). The error in the location
of the branch points (poles(r)) is much worse than that for the poles in the previous example.
Quantity
u on [0, 10]
r on [0, 10]
poles(r)
Max Error
3.7 × 10−12
1.9 × 10−9
4.3 × 10−2
Mean Error
1.1 × 10−12
2.0 × 10−10
4.3 × 10−2
What relevance do the residues of the poles of r have for f at branch point
singularities? For this example the poles of r have residues of absolute value 1.3×1014
10
Fig. 4.4. Top: Schematic of the poles (blue dots) and roots (circles) of the ratinterp
approximant of the numerical solution to (4.2) on [0, 10]. Middle: Contour plot of the absolute value of the underlying solution log(1 + (z − 6)2 ). Left: polynomial approximation
of the solution to (4.2), which blows up outside of its Bernstein ellipse. Right: ratinterp
approximant r which approximates the solution further into the complex plane. The contours
are coloured blue to red on [0, 5].
and 1.4 × 1014 , which are surprisingly large. This could be a feature of our method for
calculating the residues, Matlab’s residue command [5]. The documentation warns
that the method is unstable in some circumstances. It could also be a property of the
rational interpolation and least squares approximation for branch point singularities.
4.3. Lorenz Attractor. Viswanath and Şahutoğlu 2010 [21] is a fascinating
paper, which puts forward the point of view that although the Lorenz attractor is
a well known example in applied mathematics, relatively little is known about the
mathematical analysis of its solutions. The authors present an analytic treatment
where they consider time as a complex variable and show that a certain class of
solutions respresented by so-called Ψ-series are singular, with complex logarithmic
singularities close to the real line.
The system was originally studied by Lorenz, who derived it from the simplified
11
equations of convection rolls arising models of the atmosphere:
dx
= 10(y − x),
dt
dy
= 28x − y − xz,
dt
dz
= −8z/3 + xy.
dt
(4.3)
It is an early example of a chaotic dynamical system, in which small changes in initial
data can produce wildly different results in the solution. Lorenz used this in his 1963
paper to argue that accurate long-range weather prediction may be impossible [12].
Definition 4.1. A logarithmic Ψ-series centered at t0 is a series of the form
∞
X
Pj (η)(t − t0 )j ,
η = log(b(t − t0 )),
(4.4)
j=−J
where J is an integer, Pj is a polynomial and b is a complex number with |b| = 1.
We can take b = ±i without loss of generality. As t → t0 a Ψ-series behaves
asymptotically like a pole of Jth order, as the polynomials in η are “overpowered” by
the (t − t0 )−J term (Hille calls them pseudopoles [10]). We expect pseudopoles to be
approximated well by rational functions because of this asymptotic approximation.
Ψ-series solutions of the Lorenz attractor described in [21] are of the form
P−1 (η)
+ P0 (η) + P1 (η)(t − t0 ) + P2 (t − t0 )2 + . . . ,
t − t0
Q−1 (η)
Q−2 (η)
+
+ Q0 (η) + Q1 (η)(t − t0 ) + Q2 (t − t0 )2 + . . . ,
y(t) =
2
(t − t0 )
t − t0
R−2 (η)
R−1 (η)
z(t) =
+
+ R0 (η) + R1 (η)(t − t0 ) + R2 (t − t0 )2 + . . . ,
2
(t − t0 )
t − t0
x(t) =
(4.5)
in the disc |t − t0 | ≤ r for some r > 0 but with the singular point t = t0 and a branch
cut deleted from the disc. It should be borne in mind that it has not yet been proved
that all singular solutions of the Lorenz attractor take this form.
The chebop methods in Chebfun struggle to solve the Lorenz system without a
good initial starting point as it is nonlinear and highly oscillatory, so we use Chebfun’s
overload of ode113. The same applies to examples in subsections 4.4 and 4.5. For
this experiment we set our initial conditions to be
x(0) = −14,
y(0) = −15,
z(0) = 20,
(4.6)
which gives the beautiful butterfly shaped trajectory in 3-dimensional space shown in
the second plot in Figure 4.5. We solved the Lorenz system on [0, 5] with the above
initial conditions, and ode113 returned three chebfuns ux , uy and uz with degrees
Nx = 462, Ny = 509 and Nz = 498. We used our general strategy and computed
rational approximants of types (231, 20), (255, 20) and (249, 20) and ratinterp returned rational functions of types (173, 10), (227, 10) and (221, 10) respectively.
We can see from (4.5) that x, y and z have precisely the same singularities, t0 ,
so we should expect our rational approximants rx , ry and rz to have the same singularities (assuming the initial conditions give a solution of this Ψ-series form). Table
4.3 lists the locations of the 10 computed singularities for each component and we do
12
Fig. 4.5. An ode113 solution to the Lorenz system solved on the interval [0, 5] with initial
conditions (4.6). For each singular point computed by ratinterp, t ∈ C with Re(t) = t0 , we
have plotted a dotted line on at time t0 and a star at point (x(t0 ), y(t0 ), z(t0 )).
see some agreement with this hypothesis, but the agreement is only to 1 or 2 decimal
places. Just as in the elementary branch point example, the singularities are not approximated to a very high degree of accuracy, even if in this case we are approximating
pseudopoles.
Table 4.3
Poles of the ratinterp approximation to our numerical solution to the Lorenz attractor.
The 3 components should have the same poles, but we only agree to 1 or 2 decimal places.
rx
0.9301 ± 0.1642i
1.6394 ± 0.1873i
2.4530 ± 0.1669i
3.1834 ± 0.1816i
4.1492 ± 0.1520i
ry
0.9295 ± 0.1564i
1.6384 ± 0.1773i
2.4519 ± 0.1572i
3.1836 ± 0.1717i
4.1484 ± 0.1446i
rz
0.9297 ± 0.1565i
1.6382 ± 0.1796i
2.4520 ± 0.1570i
3.1818 ± 0.1715i
4.1484 ± 0.1445i
Max Separation
7.6 × 10−2
1.0 × 10−2
9.9 × 10−3
1.0 × 10−2
7.5 × 10−3
In Figure 4.6 we have plotted the locations of the singularities and zeros of the
ratinterp approximations to the chebfun solutions to the Lorenz system. The singularity structure is similar to that of the periodic solution computed by Viswanath
and Şahutoğlu in Figure 1.1 of [21].
Figure 4.7 shows an important feature of robust rational interpolation and least
squares on an interval. The clustering of the contours implies the existence of complex
singularities in the underlying solution with real parts about 0.2 and 4.8, which have
been missed by the rational approximation. In these experiments, this has been
normal behaviour: singularities with real part close to the edges of the interval do not
often appear in the rational approximant, but are lost behind the oval shaped barrier
of high-degree-polynomial increase.
The residues of the poles are huge, of magnitude around 10100 , even bigger than
those of the previous example. Some investigation should be made into whether the
calculated figures for the residues are a facet of our rational approximation of branch
points or due to the noted instability of the residue command.
It has been proved that for solutions on the Lorenz attractor, any singularity
t0 ∈ C must have |Im(t0 )| > 0.037 [21]. This is important, because if in general a
13
Fig. 4.6. A schematic of the rational approximation of ux (blue), uy (green) and uz (red)
of the ode113 solution to (4.3) on [0, 5]. The singularity structure is similar to that of the
periodic solution computed by Viswanath and Şahutoğlu in Figure 1.1 of [21].
Fig. 4.7. A contour plot of the absolute value of the ratinterp approximant rx (t) of the
numerical solution to the Lorenz attractor (4.3). The contours are coloured blue to red on
[0, 80]
solution to a differential equation satisfies |Im(t0 )| > τ , the transformation
ζ=
exp(πt/2τ ) − 1
exp(πt/2τ ) + 1
(4.7)
maps the strip where the solution is analytic to the unit disc. The solution must
therefore have a globally convergent expansion in powers of ζ. Numerical experiments
such as the ones we have done here, and those done by Viswanath and Şahutoğlu
14
for [21] can help inform the analysis in this fashion.
4.4. Lotka-Volterra Predator-Prey Model. Our next example is from mathematical ecology. A simple population model for interacting species is the LotkaVolterra predator-prey model
dx
= αx − βxy,
dt
dy
= −γy + δxy,
dt
(4.8)
where α, β, γ, δ are positive constants with γ < β. In the model, y represents the
population of a predator and x represents the population of its prey as they vary
over time [11]. The coefficient α encodes the rate of reproduction in prey irrespective
of predators, β describes the rate at which predators kill the prey, γ represents the
starvation of predators in the absence of prey, and δ expresses the rate at which
predators reproduce given enough prey to feed on.
If we restrict ourselves to the physically meaningful case where x, y > 0, the
system can be integrated exactly. Dividing the two equations and using the chain
rule gives the separable equation,
α − βy
δx − γ dx
=
,
x dy
y
(4.9)
and we obtain an implicit solution by integrating with respect to y:
H = (α log y − βy) + (γ log x − δx).
(4.10)
H can be considered as the first integral, Hamiltonian or energy of the system and is
a constant that depends only on the initial conditions. We plot this trajectory (x, y)
in Figure 4.9 for different values of H. The parameters and initial conditions we use
here are α = β = 0.5, γ = δ = 1, x(0) = 2, y(0) = 3.
There are still many open problems for the analysis of the Lorenz attractor, which
is a three-dimensional autonomous system [21]. In contrast, the analysis of the LotkaVolterra system, a plane autonomous system, is further developed. Hille proved that
the plane quadratic system
dx
= x(a0 + a1 x + a2 y),
dt
dy
= y(b0 + b1 x + b2 y),
dt
(4.11)
(4.12)
has a logarithmic Ψ-series singularity of order 1 (i.e. J = 1 in the expression (4.4))
if (a1 − b1 )(a2 − b2 )/(a1 b2 − a2 b1 ) is a positive integer [10, Sec. 12.5,12.6]. For the
Lotka-Volterra system, we have
(a1 − b1 )(a2 − b2 )
(0 − δ)(−β − 0)
=
= 1.
a1 b2 − a2 b1
0 · 0 − (−β)(δ)
(4.13)
Hence the Lotka-Volterra system always has a simple logarithmic Ψ-series singularity
in the complex plane.
As in the previous subsection, we used ode113 to solve the Lotka-Volterra system
on [0, 45], which returned two chebfuns u and v with degrees Nx = 743 and Ny = 737.
15
Fig. 4.8. ode113 solutions u and v to (4.8) with α = β = 0.5, γ = δ = 1, u(0) = 2, v(0) =
3 in blue and green respectively.
Fig. 4.9. Left: Implicit solutions to (4.8) with α = β = 0.5, γ = δ = 1, for various values
of H. Right: A numerically computed trajectory for the same problem with initial conditions
u(0) = 2, v(0) = 3. The point on the trajectory corresponding to u and v at the time that is
the real part of the (periodic) complex singularities has been marked ((u, v) = (3.44, 2.27)).
Table 4.4
Singularities of the Lotka-Volterra equations for α = β = 0.5, γ = δ = 1. They should,
by periodicity, have the same imaginary part, but we can see that the accuracy in this respect
is quite low.
rx
11.0204 ± 0.9166i
22.3637 ± 0.9169i
33.7086 ± 0.9168i
ry
11.0244 ± 0.9170i
22.3692 ± 0.9171i
33.7128 ± 0.9168i
Separation
4.0 × 10−3
5.5 × 10−3
4.2 × 10−3
For the benefit of the reader, these two chebfuns have been plotted in Figure 4.8.
Following the heuristics discussed in Section 3, we used ratinterp with target types
(371, 20) and (366, 20) on 372 and 366 Chebyshev points in [0, 45], which returned
rational approximants of types (297, 6) and (287, 6).
In Figure 4.10, we see that the periodicity of the solution is expressed somewhat
in the rational approximant, but the appoximant still blows up outside of an oval
shaped barrier. What should plainly be singularities just off the real line near time 0
and time 45 are lost behind the barrier, just as in the Lorenz attractor example. To
approximate these singularities, can solve the system on [−15, 15] and [30, 60].
Although the user can see where the barrier is by inspection, and that there may
be further singularities behind it, if an automated procedure for locating singularities
16
Fig. 4.10. We solved the Lotka-Volterra system on the interval [0, 45] using ode113.
These are contour plots of rational approximants rx (t) (top) and ry (t) (bottom) to the numerical solution, in the complex t-plane. We can see the periodic nature of the solutions in
the complex plane, with period around 11.3, and the branch cut associated with each singularity, the beginnings of which protrude outwards from the real line. The contours are coloured
blue to red on [0, 20]
is what we desire, estimates for the size and shape of the oval barrier for the rational
approximant would be necessary.
4.5. Three-Body Problem. The three-body problem is the term used to refer
to the system of ODEs modelling the motion of three points of prescribed masses
under mutual Newtonian gravitation in three dimensions:
d2 x1
x2 − x1
x3 − x1
= m2
+ m3
,
dt2
kx2 − x1 k3
kx3 − x1 k3
d2 x2
x1 − x2
x3 − x2
= m1
+ m3
,
dt2
kx1 − x2 k3
kx3 − x2 k3
d2 x3
x1 − x3
x2 − x3
= m1
+ m2
.
dt2
kx1 − x3 k3
kx2 − x3 k3
(4.14)
Here x1 , x2 and x3 are the positions of three particles with masses m1 , m2 and m3
respectively, in space at time t ∈ R. The problem is a classic that has fascinated
some of the greatest mathematicians: Newton, Euler, Lagrange, Laplace, Poincaré
and even those of today.
In 2000 Chenciner and Montgomery published a paper proving the existence of a
“remarkable periodic solution to the three-body problem in the case of equal masses”
17
[4], where the three particles travel around in a planar figure of eight shape as in
Figure 4.11. This particular solution was first discovered numerically by C. Moore
in 1993 [14]. Such solutions to the general n-body problem with smooth periodic
trajectories have since been called choreographies and it is now an active area of
research [13].
Fig. 4.11. The paths of the three complex-valued bodies x1 , x2 and x3 solving (4.14) travel
periodically in a figure of eight shape in the complex plane. We colour the bodies and their
paths for the first third of their periods here in blue, green and red respectively.
Since this special case of the three-body problem is planar, we can use complex
arithmetic to represent the positions x1 , x2 and x3 1 . Without loss of generality we
may assume the masses are all equal to 1. The initial conditions for the figure of eight
are given in the paper (computed by Carles Simó) and are as follows:
x1 = −x2 = 0.97000436 − 0.24308753i, x3 = 0,
ẋ1 = ẋ2 = 0.466203685 + 0.43236573i, ẋ3 = −0.93240737 − 0.86473146i,
(4.15)
The resulting choreography has period T = 6.32591398 (approx.), so we used ode113
to solve the system on the interval [0, 12.65182796] (two periods) which returned
u1 , u2 and u3 , chebfuns of degree 330, 330 and 335 respectively. We used ratinterp
to compute type (165, 20), (165, 20), and 167, 20) rational approximations on 331, 331
and 336 Chebyshev points on the interval, which returned rational functions r1 , r2
and r3 of types (157, 7), (157, 7) and (159, 6).
The resulting singularity structure shown in Figure 4.12 is different from all the
other examples: Because x1 , x2 and x3 are complex valued, the singularities do not
necessarily come in conjugate pairs. Now, let us turn to the complex t-plane (as opposed to the complex xi plane) on which the solution should be T -periodic throughout
and let us assume that x3 has a singularity at complex time t = αT + βi. Our argument will be simplest if we use x3 because its initial position is the origin. It follows
from the four-fold symmetry of the solution that x3 is singular for values of t with real
parts: ±αT, T /2 ± αT (modulo T ), and imaginary part ±β. This is precisely what
we see in our numerical analytic continuation (see Figure 4.12 and Table 4.5), four
singularities for each body per period, with two bodies participating in each.
1 To be clear, each body x takes complex values in a figure eight shape for real time t, but we
i
also consider complex time, at which xi can take any value in the complex plane
18
Fig. 4.12. Contour plots of the figure-of-eight solution solved using ode113 on the time
interval [0, 12.6518], extended into the complex plane using rational interpolation. Each red
dot is a singularity in the system for complex time, with two bodies participating in each.
The contours are coloured from blue to red on the interval [0, 2]
Now, by construction the particles divide the circuit into three by time, so
x1 (t) = x2 (t + T /3) = x3 (t + 2T /3). Therefore if x3 and x2 (or x1 ) participate in the
singularity at αT , x3 also has a singularity with real part αT − T /3 (or αT − 2T /3).
We can use these two symmetrical observations to find equations involving α
modulo T ; solving for α, we can find all possibilities for its numerical value. For
example, αT +T /3 = T /2−αT implies α = 1/12. One finds that the only possibilities
are α = 1/12, 5/12, 7/12, 11/12.
This gives us good reason to postulate that the real parts of the singularities of
the figure of eight solution are (modulo T and to 4 decimal places),

 1.5815, 2.6358, 4.7444, 5.7988 (for x1 ),
0.5272, 1.5815, 3.6901, 4.7444 (for x2 ),
αT =
(4.16)

0.5272, 2.6358, 3.6901, 5.7988 (for x3 ),
and our numerical results agree with this to 1 or 2 decimal places (see Table 4.5 and
Figure 4.13). This is, as with the previous three examples, not very accurate, but
nonetheless ratinterp did not produce any spurious poles.
The singularity structure of the figure of eight solution was brought to the author’s
attention in a private communication with Divakar Viswanath, for which the author
is very grateful. The symmetry argument above for the possible locations of the
19
Table 4.5
Singularities of three bodies for the figure-of-eight solution to the three body problem.
Two bodies participate in each singularity.
r1
1.5776 − 0.5735i
2.6331 + 0.5447i
4.7462 + 0.5518i
5.7981 − 0.5532i
7.9078 − 0.5533i
8.9621 + 0.5511i
11.0378 + 0.5387i
r2
1.6141 − 0.5387i
3.6898 − 0.5511i
4.7440 + 0.5533i
6.8537 + 0.5532i
7.9057 − 0.5518i
10.0187 − 0.5447i
11.0743 + 0.5735i
r3
2.6309 + 0.5416i
3.6886 − 0.5484i
5.8002 − 0.5527i
6.8516 + 0.5527i
8.9633 + 0.5484i
10.0210 − 0.5416i
Separation
5.0 × 10−2
3.8 × 10−3
3.0 × 10−3
2.7 × 10−3
2.2 × 10−3
2.2 × 10−3
2.6 × 10−3
3.9 × 10−3
3.9 × 10−3
5.0 × 10−2
Fig. 4.13. The configuration of the particles at times t = 0.5272, 2.6358, 3.6901, 5.7988,
the real parts of the singular points of x3 . Coloured black is x3 (t) with x1 and x2 shown as
empty circles. The bodies form an isosceles triangle at these times.
singularities is due him as well.
5. Discussion.
5.1. Computing Complex Singularities of Differential Equations. We
have performed numerical experiments using Chebfun’s ratinterp for computing
complex singularities of solutions to some interesting ODE problems. We used a
strategy developed in preliminary experiments and demonstrated that we can successfully find the singularities of difficult problems while avoiding spurious poles. The
claim of Gonnet et al. [9], that the algorithm is robust, is evidently justified.
The robust algorithm ratinterp has potential for the numerical study of parabolic
PDEs and parametrised problems, because implementation of our strategy allows the
computation of the singularities at each time step or parameter variation to be automated. It is however, not suitable for applications which require a high level of
accuracy. As was pointed out in [9] the error in the rational approximant is increased
by the inclusion of the robustness procedure, dependent on the tol parameter. But
20
if ratinterp is used to reliably find the approximate locations of the singularities,
other methods such as the method of steepest ascent can be used to find precise
locations [7].
5.2. Stability of Barycentric Interpolation Formulas for Extrapolation.
During our experiments, we noticed a surprising instability. When ratinterp returns
the rational approximant, if we use Chebyshev points, equispaced points around the
unit circle or some other specific types of points, it returns a rational barycentric
interpolation formula. This is a formula of the form
N
N
X
q(xj )
wj f (xj ) X wj
, wj = Q
,
(5.1)
r(x) =
x
−
x
x
−
x
j
j
i6=j (xi − xj )
j=0
j=0
where f is the function we are approximating in the interpolation points, {xj } (which
throughout this paper have been Chebyshev points on [a, b]). It is a tricky calculation
to show that for Chebyshev points the weights are wj = (−1)j q(xj ), after cancelling
out factors independent of j, with w0 and wN equal to half this formula [2].
The instability arises for x outside of the Chebfun ellipse for f (defined in the
Section 2), and is due to the following formula:
N
X
j=0
wj
q(x)
.
= QN
x − xj
i=0 (x − xi )
(5.2)
As the absolute value of x increases, the right hand side of (5.2) decreases to machine
precision, beyond which it cannot decrease any further. The result is a dramatic loss
of accuracy in (5.1) until there are no accurate digits at all. We can see in Figure 4.1
for example, that a polynomial can be extrapolated throughout the Bernstein ellipse,
but outside of that it is useless as an approximation of the underlying function, so if
we cannot stably evaluate our rational approximant outside of the Bernstein ellipse we
are doing no better than we can with a polynomial! More detail can be found in a short
article written with Trefethen and Gonnet [22]. ratinterp has since been corrected
to evaluate the rational function as the quotient of two polynomials, each evaluated
by a numerically stable version of polynomial barycentric interpolation formula.
5.3. Further Work. There are some issues with the calculation of residues.
In Gonnet et al.’s program ratdisk [9], the precursor to ratinterp, the residue
is calculated using a trapezium rule approximation to the associated contour integral. This turns out not to be very accurate for all but the simplest of functions.
The method used in Chebfun’s ratinterp is implemented using Matlab’s residue
function, which is much more accurate but represents an ill-posed problem; if the
numerator is close to a polynomial with multiple roots then small changes in the data
can make arbitrarily large changes in the resulting poles and residues [5].
We noted that the residues calculated in this way can be remarkably large, most
probably because of this instability. A short study comparing different methods of
calculating residues should be done to get to the bottom of the issue.
Gonnet, Güttel and Trefethen have produced a paper on a robust implementation
of Padé approximation [8]. They achieve robustness using the SVD of a linearised
problem, just as in the robust implementation of rational interpolation and least
squares. The question of how the SVD of the linear system is related to the singularities in the Padé approximant should be explored too, just as for rational interpolation,
and their comparison could lead to a more concrete theory of these SVD-based robust
algorithms for rational approximation.
21
6. Acknowledgements. The author would like to thank Nick Trefethen for
his guidance and supervision during the project. His advice and suggestions were
invaluable, especially when he took the time to read and comment on the early drafts
of this paper. Many thanks to André Weideman for his insight and stimulating
discussion during his brief stay in the UK, Pedro Gonnet for his astounding expertise in
scientific computing, Alex Townsend for the voice of reason in times of confusion, and
everyone at Oxford University in the Numerical Analysis Group for their hospitality,
especially the administrator, Lotti Ekert. This paper was part of a summer research
project funded by an EPSRC Undergraduate Vacation Bursary.
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