A Geometrical Stability Condition for Compressed Sensing

TL;DR

This paper introduces a new geometrical stability condition for compressed sensing that enhances understanding of stable and robust recovery of signals, especially in noisy environments, by connecting it with classical concepts and convex programs.

Contribution

It formulates a novel geometrical stability condition for approximate signal recovery in compressed sensing, linking it with restricted singular values and complexity theory.

Findings

New stability condition for noisy measurements

Exact recovery implies stability for nearby signals

Connections with convex geometry and complexity theory

Abstract

During the last decade, the paradigm of compressed sensing has gained significant importance in the signal processing community. While the original idea was to utilize sparsity assumptions to design powerful recovery algorithms of vectors $x \in \mathbb{R}^d$, the concept has been extended to cover many other types of problems. A noteable example is low-rank matrix recovery. Many methods used for recovery rely on solving convex programs. A particularly nice trait of compressed sensing is its geometrical intuition. In recent papers, a classical optimality condition has been used together with tools from convex geometry and probability theory to prove beautiful results concerning the recovery of signals from Gaussian measurements. In this paper, we aim to formulate a geometrical condition for stability and robustness, i.e. for the recovery of approximately structured signals from noisy measurements. We will investigate the connection between the new condition with the notion of restricted singular values, classical stability and robustness conditions in compressed sensing, and also to important geometrical concepts from complexity theory. We will also prove the maybe somewhat surprising fact that for many convex programs, exact recovery of a signal $x_0$ immediately implies some stability and robustness when recovering signals close to $x_0$.