Lifting $\ell_1$-optimization strong and sectional thresholds

TL;DR

This paper develops a new method to analyze the strong and sectional thresholds of $ ext{l}_1$-minimization in compressed sensing, addressing complex combinatorial questions that previous techniques could not resolve.

Contribution

The paper introduces a novel mechanism for analyzing the strong and sectional thresholds of $ ext{l}_1$-minimization, advancing understanding of non-typical sparse recovery scenarios.

Findings

Provides a new approach to analyze strong thresholds

Addresses the combinatorial complexity of sectional thresholds

Enhances theoretical understanding of sparse recovery limits

Abstract

In this paper we revisit under-determined linear systems of equations with sparse solutions. As is well known, these systems are among core mathematical problems of a very popular compressed sensing field. The popularity of the field as well as a substantial academic interest in linear systems with sparse solutions are in a significant part due to seminal results \cite{CRT,DonohoPol}. Namely, working in a statistical scenario, \cite{CRT,DonohoPol} provided substantial mathematical progress in characterizing relation between the dimensions of the systems and the sparsity of unknown vectors recoverable through a particular polynomial technique called $\ell_1$-minimization. In our own series of work \cite{StojnicCSetam09,StojnicUpper10,StojnicEquiv10} we also provided a collection of mathematical results related to these problems. While, Donoho's work \cite{DonohoPol,DonohoUnsigned} established (and our own work \cite{StojnicCSetam09,StojnicUpper10,StojnicEquiv10} reaffirmed) the typical or the so-called \emph{weak threshold} behavior of $\ell_1$-minimization many important questions remain unanswered. Among the most important ones are those that relate to non-typical or the so-called \emph{strong threshold} behavior. These questions are usually combinatorial in nature and known techniques come up short of providing the exact answers. In this paper we provide a powerful mechanism that that can be used to attack the "tough" scenario, i.e. the \emph{strong threshold} (and its a similar form called \emph{sectional threshold}) of $\ell_1$-minimization.