Verifiable conditions of $\ell_1$-recovery of sparse signals with sign restrictions

TL;DR

This paper establishes necessary and sufficient conditions for exact $\

Contribution

It introduces verifiable conditions for $\

Findings

Provides criteria for exact $\

Derives error bounds for imperfect recovery

Offers computational methods for assessing $s$-semigoodness

Abstract

We propose necessary and sufficient conditions for a sensing matrix to be "s-semigood" -- to allow for exact $\ell_1$-recovery of sparse signals with at most $s$ nonzero entries under sign restrictions on part of the entries. We express the error bounds for imperfect $\ell_1$-recovery in terms of the characteristics underlying these conditions. Furthermore, we demonstrate that these characteristics, although difficult to evaluate, lead to verifiable sufficient conditions for exact sparse $\ell_1$-recovery and to efficiently computable upper bounds on those $s$ for which a given sensing matrix is $s$-semigood. We concentrate on the properties of proposed verifiable sufficient conditions of $s$-semigoodness and describe their limits of performance.