# Complex Step Differentiation 3

Posted by **Cleve Moler**,

Complex step differentiation is a technique that employs complex arithmetic to obtain the numerical value of the first derivative of a real valued analytic function of a real variable, avoiding the loss of precision inherent in traditional finite differences.

### Contents

#### Stimulation

It took prodding from a couple of people to bring about this post.

Many months ago I met Ricardo (Pax) Paxson, head of our Computational Biology group, in the MathWorks cafeteria.

- Pax: Hey, Cleve, you ought to blog about that complex step algorithm you invented.
- Me: What are you talking about?
- Pax: You know, differentiation using complex arithmetic. It's hot stuff. We're using it in SimBiology.
- Me: I don't remember inventing any complex step differentiation algorithm.
- Pax: Well, an old paper of yours is referenced. Lots of people are using it today.

Then last February I met Joaquim Martins, a Professor of Aerospace Engineering at the University of Michigan, during the SIAM CS&E meeting. He gave me three papers he had written about the complex step method that he and his colleagues were using to compute derivatives of simulations that couple computational fluid dynamics with structural finite-element analysis for aircraft design applications

Joaquim's papers refer to a paper on numerical differentiation using complex arithmetic that James Lyness and I had published in the *SIAM Journal of Numerical Analysis* in 1967. That's almost 50 years ago.

#### Lyness and Moler

James Lyness was a buddy of mine. We met at the ETH in Zurich when I was visiting there on my postdoc in 1965-66. He was an Englishman a few years older than I, visiting the ETH at roughly the same time. After that visit, he spent the rest of his professional career at Argonne Laboratory. He didn't work on LINPACK and EISPACK, but I would see him at Argonne when I visited there to work on those packages. The photo that I often show of the four LINPACK authors lounging on the back of Jack Dongarra's car was taken during a party at the Lyness home.

That year in Zurich was very productive research wise. James had lots of interesting ideas and we published several papers. One of them was "Numerical Differentiation of Analytic Functions". It introduces, I guess for the first time, the idea of using complex arithmetic on functions of a real variable. But the actual algorithms are pretty exotic. They are derived from Cauchy's Integral Theorem and involve things like Moebius numbers and the Euler Maclaurin summation formula. If you asked me to describe them in any detail today, I wouldn't be able to.

The complex step algorithm that I'm about to describe is much simpler than anything described in that 1967 paper. So, although we have some claim of precedence for the idea of using complex arithmetic on real functions, we certainly cannot claim to have invented today's algorithm.

The complex step algorithm was first described in a brief paper by William Squire and George Trapp in *SIAM Review* in 1998. Martins, together with his colleagues and students, offers an extensive guide in the papers I refer to below.

#### The Algorithm

We're concerned with an *analytic* function. Mathematically, that means the function is infinitely differentiable and can be smoothly extended into the complex plane. Computationally, it probably means that it is defined by a single "one line" formula, not a more extensive piece of code with `if` statements and `for` loops.

Let $F(z)$ be such a function, let $x_0$ be a point on the real axis, and let $h$ be a real parameter. Expand $F(z)$ in a Taylor series off the real axis.

$$ F(x_0+ih) = F(x_0)+ihF'(x_0)-h^2F''(x_0)/2!-ih^3F^{(3)}/3!+... $$

Take the imaginary part of both sides and divide by $h$.

$$ F'(x_0) = Im(F(x_0+ih))/h + O(h^2) $$

Simply evaluating the function $F$ at the imaginary argument $x_0+ih$, and dividing by $h$, gives an approximation to the value of the derivative, $F'(x_0)$, that is accurate to order $O(h^2)$. We might as well choose $h=10^{-8}$. Then the error in the approximation is about the same size as the roundoff error involved in storing a double precision floating point value of $F'(x_0)$.

Here, then, is the complex step differentiation algorithm in its entirety:

$$ h = 10^{-8} $$

$$ F'(x_0) \approx Im(F(x_0+ih))/h $$

#### An Example

Here is one of my favorite examples. I used it in the 1967 paper and it has become a kind of standard in this business ever since.

$$ F(x) = \frac{\mbox{e}^{x}}{(\cos{x})^3+(\sin{x})^3} $$

F = @(x) exp(x)./((cos(x)).^3 + (sin(x)).^3)

F = @(x)exp(x)./((cos(x)).^3+(sin(x)).^3)

Here's a plot over a portion of the $x$-axis, with a dot at $\pi/4$.

ezplot(F,[-pi/4,pi/2]) axis([-pi/4,pi/2,0,6]) set(gca,'xtick',[-pi/4,0,pi/4,pi/2]) line([pi/4,pi/4],[F(pi/4),F(pi/4)],'marker','.','markersize',18)

Let's find the slope at the dot, that's $F'(\pi/4)$.

The complex step approximation where I have not yet fixed the step size `h` is

Fpc = @(x,h) imag(F(x+i*h))/h;

The centered finite difference approximation with step size `h` is

Fpd = @(x,h) (F(x+h) - F(x-h))/(2*h);

Compare them for a sequence of decreasing `h`.

format long disp([' h complex step finite differerence']) for h = 10.^(-(1:16)) disp([h Fpc(pi/4,h) Fpd(pi/4,h)]) end

h complex step finite differerence 0.100000000000000 3.144276040634560 3.061511866568119 0.010000000000000 3.102180075411270 3.101352937655877 0.001000000000000 3.101770529535847 3.101762258158169 0.000100000000000 3.101766435192940 3.101766352480162 0.000010000000000 3.101766394249620 3.101766393398542 0.000001000000000 3.101766393840188 3.101766393509564 0.000000100000000 3.101766393836091 3.101766394841832 0.000000010000000 3.101766393836053 3.101766443691645 0.000000001000000 3.101766393836052 3.101766399282724 0.000000000100000 3.101766393836052 3.101763290658255 0.000000000010000 3.101766393836053 3.101785495118747 0.000000000001000 3.101766393836053 3.101963130802687 0.000000000000100 3.101766393836052 3.097522238704187 0.000000000000010 3.101766393836053 3.108624468950438 0.000000000000001 3.101766393836052 3.108624468950438 0.000000000000000 3.101766393836053 8.881784197001252

As predicted, complex step has obtained its full double precision result half way through the sequence of `h's`. On the other hand, the finite difference method never achieves more than half double precision accuracy and completely breaks down by the time `h` reaches the last value.

#### Symbolic Toolbox

Since we have access to the Symbolic Toolbox, we can get the exact answer. Redefine `x` and `F` to be symbolic.

```
syms x
F = exp(x)/((cos(x))^3 + (sin(x))^3)
```

F = exp(x)/(cos(x)^3 + sin(x)^3)

Differentiate and evaluate the derivative at $\pi/4$.

Fps = simple(diff(F)) exact = subs(Fps,pi/4) flexact = double(exact)

Fps = (exp(x)*(cos(3*x) + sin(3*x)/2 + (3*sin(x))/2))/(cos(x)^3 + sin(x)^3)^2 exact = 2^(1/2)*exp(pi/4) flexact = 3.101766393836051

This verifies the result we obtained with the complex step.

#### Error Plot

Now that we now the exact answer, we can plot the error from computing the first derivative by complex step and finite differences.

h = 10.^(-(1:16)'); errs = zeros(16,2); for k = 1:16 errs(k,1) = abs(Fpc(pi/4,h(k))-exact); errs(k,2) = abs(Fpd(pi/4,h(k))-exact); end loglog(h,errs) axis([eps 1 eps 1]) set(gca,'xdir','rev') legend('complex step','finite difference','location','southwest') xlabel('step size h') ylabel('error')

This confirms that complex step is accurate for small step sizes whereas the finite difference approach never achieves full accuracy.

#### References

Lyness, James N., and Moler, Cleve B., Numerical Differentiation of Analytic Functions, *SIAM J. Numerical Analysis 4*, 1967, pp. 202-210. epubs.siam.org/doi/abs/10.1137/0704019

Squire, William, and Trapp, George, Using complex variables to estimate derivatives of real functions, *SIAM Review 40*, 1998, pp. 110-112. epubs.siam.org/doi/abs/10.1137/S003614459631241X

Martins, Joaquim R. R. A., Sturdza, Peter, and Alonso, Juan J., The Complex-Step Derivative Approximation, *ACM Transactions on Mathematical Software 29*, 2003, pp. 245-262, Martins2003CSD.pdf

Martins, Joaquim R. R. A., A Guide to the Complex-Step Derivative Approximation, complex step guide

Kenway, Gaetan K.W., Kennedy, Graeme J., and Martins, Joaquim R. R. A., A scalable parallel approach for high-fidelity steady-state aeroelastic analysis and adjoint derivative computations, AIAA Journal (In Press)

Get the MATLAB code

Published with MATLAB® R2013b

**Category:**- Algorithms,
- Calculus,
- History,
- Numerical Analysis

## 3 CommentsOldest to Newest

**1**of 3

Hi Cleve,

I had to throw one of my own tools at this function, to see how it might compare.

F = @(x) exp(x)./((cos(x)).^3 + (sin(x)).^3);

[d,derr] = derivest(F,pi/4)

d =

3.10176639383597

derr =

4.83758615404711e-13

derr is an estimate provided by the code of its uncertainty in the result.

exact = 2^(1/2)*exp(pi/4)

exact =

3.10176639383605

d – exact

ans =

-7.8159700933611e-14

Derivest uses by default a 4th order finite difference, with a multiple term variation of Richardson extrapolation. The error term comes from the extrapolation.

So finite differences are able to do reasonably well, given the right numerical scheme. I can’t quite reach the accuracy you got with the complex step result though.

John

**2**of 3

Complex step works for matrix functions, too:

The complex step approximation to the Fréchet derivative of a matrix function, http://link.springer.com/article/10.1007/s11075-009-9323-y

**3**of 3

I hope this doesn’t sound like a plug, but seeing this post reminded me of three blog posts I wrote a few years ago, whose titles all begin with “Complex Magic,” all based on your paper with Lyness, with the last one looking at an application to exponential time differencing (ETD), based on a paper by Kassam and Trefethen. Here are the links to those posts, in case anyone may find them useful:

http://matlabician.wordpress.com/2009/12/18/complex-magic-part-2-differentiation-via-integration/

## Recent Comments