  Many interesting and fundamentally practical optimization problems, ranging
from optics, to signal processing, to radar and acoustics, involve constraints
on the Fourier transform of a function. It is well-known that the {\em fast
Fourier transform} (fft) is a recursive algorithm that can dramatically improve
the efficiency for computing the discrete Fourier transform. However, because
it is recursive, it is difficult to embed into a linear optimization problem.
In this paper, we explain the main idea behind the fast Fourier transform and
show how to adapt it in such a manner as to make it encodable as constraints in
an optimization problem. We demonstrate a real-world problem from the field of
high-contrast imaging. On this problem, dramatic improvements are translated to
an ability to solve problems with a much finer grid of discretized points. As
we shall show, in general, the "fast Fourier" version of the optimization
constraints produces a larger but sparser constraint matrix and therefore one
can think of the fast Fourier transform as a method of sparsifying the
constraints in an optimization problem, which is usually a good thing.
