RSP-Based Analysis for Sparsest and Least $\ell_1$-Norm Solutions to Underdetermined Linear Systems

TL;DR

This paper introduces a deterministic, linear programming-based analysis of when $ ext{l}_1$-minimization uniquely recovers the sparsest solution to underdetermined linear systems, emphasizing the role of the range space property (RSP).

Contribution

It establishes a necessary and sufficient RSP-based condition for the uniqueness of least $ ext{l}_1$-norm solutions and develops a new theory for sparse recovery using RSP of order $K$.

Findings

RSP is key to understanding $ ext{l}_1$-minimization's success.

New matrix properties like RSP of order $K$ are introduced.

The analysis explains the gap between theory and practical performance.

Abstract

Recently, the worse-case analysis, probabilistic analysis and empirical justification have been employed to address the fundamental question: When does $\ell_1$-minimization find the sparsest solution to an underdetermined linear system? In this paper, a deterministic analysis, rooted in the classic linear programming theory, is carried out to further address this question. We first identify a necessary and sufficient condition for the uniqueness of least $\ell_1$-norm solutions to linear systems. From this condition, we deduce that a sparsest solution coincides with the unique least $\ell_1$-norm solution to a linear system if and only if the so-called \emph{range space property} (RSP) holds at this solution. This yields a broad understanding of the relationship between $\ell_0$- and $\ell_1$-minimization problems. Our analysis indicates that the RSP truly lies at the heart of the relationship between these two problems. Through RSP-based analysis, several important questions in this field can be largely addressed. For instance, how to efficiently interpret the gap between the current theory and the actual numerical performance of $\ell_1$-minimization by a deterministic analysis, and if a linear system has multiple sparsest solutions, when does $\ell_1$-minimization guarantee to find one of them? Moreover, new matrix properties (such as the \emph{RSP of order $K$} and the \emph{Weak-RSP of order $K$}) are introduced in this paper, and a new theory for sparse signal recovery based on the RSP of order $K$ is established.