On Low Rank Matrix Approximations with Applications to Synthesis Problem in Compressed Sensing

TL;DR

This paper introduces randomized and derandomized algorithms for low-rank matrix approximation to solve the synthesis problem in compressed sensing, achieving near-optimal accuracy with efficient computation.

Contribution

It formulates the synthesis problem as low-rank approximation and proposes algorithms with provable accuracy bounds, including derandomized versions, for efficient matrix synthesis.

Findings

Algorithms achieve accuracy bounds of O(1)sqrt(ln(mn)/k)

Methods are optimal up to logarithmic factors

Preliminary numerical results demonstrate effectiveness

Abstract

We consider the synthesis problem of Compressed Sensing - given s and an MXn matrix A, extract from it an mXn submatrix A', certified to be s-good, with m as small as possible. Starting from the verifiable sufficient conditions of s-goodness, we express the synthesis problem as the problem of approximating a given matrix by a matrix of specified low rank in the uniform norm. We propose randomized algorithms for efficient construction of rank k approximation of matrices of size mXn achieving accuracy bounds O(1)sqrt({ln(mn)/k) which hold in expectation or with high probability. We also supply derandomized versions of the approximation algorithms which does not require random sampling of matrices and attains the same accuracy bounds. We further demonstrate that our algorithms are optimal up to the logarithmic in m and n factor. We provide preliminary numerical results on the performance of our algorithms for the synthesis problem.