Computable performance guarantees for compressed sensing matrices

TL;DR

This paper introduces efficient algorithms to verify null space conditions in compressed sensing, enabling faster and more accurate guarantees for sparse signal recovery compared to previous LP and SDP-based methods.

Contribution

The paper presents polynomial-time algorithms and a tree search method for verifying null space conditions, improving speed and accuracy over existing approaches.

Findings

Algorithms outperform LP and SDP methods in speed and accuracy.

New procedures enable precise null space condition verification.

Numerical experiments demonstrate significant improvements.

Abstract

The null space condition for $\ell_1$ minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely recovered from the observation data via $\ell_1$ minimization. However, verifying the null space condition is known to be computationally challenging. Most of the existing methods can provide only upper and lower bounds on the proportion parameter that characterizes the null space condition. In this paper, we propose new polynomial-time algorithms to establish upper bounds of the proportion parameter. We leverage on these techniques to find upper bounds and further develop a new procedure - tree search algorithm - that is able to precisely and quickly verify the null space condition. Numerical experiments show that the execution speed and accuracy of the results obtained from our methods far exceed those of the previous methods which rely on Linear Programming (LP) relaxation and Semidefinite Programming (SDP).