On the strong convergence of forward-backward splitting in reconstructing jointly sparse signals

TL;DR

This paper proves strong convergence of a forward-backward splitting algorithm for reconstructing an infinite set of jointly sparse vectors from incomplete measurements, extending previous finite-dimensional and single-vector results.

Contribution

It establishes new strong convergence results for the forward-backward splitting algorithm in the context of infinite-dimensional joint sparse recovery, which was not previously addressed.

Findings

Proves strong convergence of the algorithm for infinite-dimensional joint sparsity

Extends convergence analysis beyond finite-dimensional cases

Addresses computational aspects of convex relaxation with mixed norm penalty

Abstract

We consider the problem of reconstructing an infinite set of sparse, finite-dimensional vectors, that share a common sparsity pattern, from incomplete measurements. This is in contrast to the work [17], where the single vector signal can be infinite-dimensional, and [28], which extends the aforementioned work to the joint sparse recovery of finite number of infinite-dimensional vectors. In our case, to take account of the joint sparsity and promote the coupling of nonvanishing components, we employ a convex relaxation approach with mixed norm penalty $\ell_{2,1}$. This paper discusses the computation of the solutions of linear inverse problems with such relaxation by a forward-backward splitting algorithm. However, since the solution matrix possesses infinitely many columns, the arguments of [17] no longer apply. As such, we establish new strong convergence results for the algorithm, in particular when the set of jointly sparse vectors is infinite.