On Model-Based RIP-1 Matrices

TL;DR

This paper establishes tight bounds for the model-based RIP-1 property in the l_1 norm, focusing on tree-sparsity and block-sparsity models, and explores implications for sparse recovery.

Contribution

It provides the first tight bounds for model-based RIP-1 in the l_1 norm for key sparsity models, extending understanding beyond the classical RIP bounds.

Findings

Tight bounds for model-based RIP-1 in l_1 norm are derived.

Results apply to tree-sparsity and block-sparsity models.

Implications for sparse recovery are discussed.

Abstract

The Restricted Isometry Property (RIP) is a fundamental property of a matrix enabling sparse recovery. Informally, an m x n matrix satisfies RIP of order k in the l_p norm if ||Ax||_p \approx ||x||_p for any vector x that is k-sparse, i.e., that has at most k non-zeros. The minimal number of rows m necessary for the property to hold has been extensively investigated, and tight bounds are known. Motivated by signal processing models, a recent work of Baraniuk et al has generalized this notion to the case where the support of x must belong to a given model, i.e., a given family of supports. This more general notion is much less understood, especially for norms other than l_2. In this paper we present tight bounds for the model-based RIP property in the l_1 norm. Our bounds hold for the two most frequently investigated models: tree-sparsity and block-sparsity. We also show implications of our results to sparse recovery problems.