Kavli Affiliate: David Gross
| First 5 Authors: Tim Fuchs, David Gross, Felix Krahmer, Richard Kueng, Dustin G. Mixon
| Summary:
Given an arbitrary matrix $Ainmathbb{R}^{ntimes n}$, we consider the
fundamental problem of computing $Ax$ for any $xinmathbb{R}^n$ such that $Ax$
is $s$-sparse. While fast algorithms exist for particular choices of $A$, such
as the discrete Fourier transform, there is currently no $o(n^2)$ algorithm
that treats the unstructured case. In this paper, we devise a randomized
approach to tackle the unstructured case. Our method relies on a representation
of $A$ in terms of certain real-valued mutually unbiased bases derived from
Kerdock sets. In the preprocessing phase of our algorithm, we compute this
representation of $A$ in $O(n^3log n)$ operations. Next, given any unit vector
$xinmathbb{R}^n$ such that $Ax$ is $s$-sparse, our randomized fast transform
uses this representation of $A$ to compute the entrywise $epsilon$-hard
threshold of $Ax$ with high probability in only $O(sn +
epsilon^{-2}|A|_{2toinfty}^2nlog n)$ operations. In addition to a
performance guarantee, we provide numerical results that demonstrate the
plausibility of real-world implementation of our algorithm.
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