Weak Stability of $\ell_1$-minimization Methods in Sparse Data Reconstruction

TL;DR

This paper develops a unified weak stability theory for $ ext{l}_1$-minimization in sparse data reconstruction, establishing necessary and sufficient conditions for robustness under various matrix properties, using classical error bounds.

Contribution

It introduces a unified framework for weak stability of $ ext{l}_1$-minimization under the weak range space property, including the first stability result under the constant-free property.

Findings

Weak stability characterized by the weak range space property.

Error bounds measured by Robinson's constant.

First stability result under the constant-free range space property.

Abstract

As one of the most plausible convex optimization methods for sparse data reconstruction, $\ell_1$-minimization plays a fundamental role in the development of sparse optimization theory. The stability of this method has been addressed in the literature under various assumptions such as restricted isometry property (RIP), null space property (NSP), and mutual coherence. In this paper, we propose a unified means to develop the so-called weak stability theory for $\ell_1$-minimization methods under the condition called weak range space property of a transposed design matrix, which turns out to be a necessary and sufficient condition for the standard $\ell_1$-minimization method to be weakly stable in sparse data reconstruction. The reconstruction error bounds established in this paper are measured by the so-called Robinson's constant. We also provide a unified weak stability result for standard $\ell_1$-minimization under several existing compressed-sensing matrix properties. In particular, the weak stability of $\ell_1$-minimization under the constant-free range space property of order $k$ of the transposed design matrix is established for the first time in this paper. Different from the existing analysis, we utilize the classic Hoffman's Lemma concerning the error bound of linear systems as well as the Dudley's theorem concerning the polytope approximation of the unit $\ell_2$-ball to show that $\ell_1$-minimization is robustly and weakly stable in recovering sparse data from inaccurate measurements.