PARNES: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals

TL;DR

This paper introduces PARNES, an efficient algorithm combining NESTA-LASSO and Pareto root-finding for accurate and rapid recovery of sparse and approximately sparse signals, with proven convergence guarantees.

Contribution

It presents PARNES, a novel algorithm that integrates NESTA-LASSO with Pareto root-finding, offering guaranteed local linear convergence for sparse signal recovery.

Findings

PARNES achieves comparable performance to existing solvers.

The modified NESTA-LASSO converges rapidly under RIP and sparsity conditions.

Numerical experiments validate the effectiveness of PARNES.

Abstract

In this article, we propose an algorithm, NESTA-LASSO, for the LASSO problem, i.e., an underdetermined linear least-squares problem with a 1-norm constraint on the solution. We prove under the assumption of the restricted isometry property (RIP) and a sparsity condition on the solution, that NESTA-LASSO is guaranteed to be almost always locally linearly convergent. As in the case of the algorithm NESTA proposed by Becker, Bobin, and Candes, we rely on Nesterov's accelerated proximal gradient method, which takes O(e^{-1/2}) iterations to come within e > 0 of the optimal value. We introduce a modification to Nesterov's method that regularly updates the prox-center in a provably optimal manner, and the aforementioned linear convergence is in part due to this modification. In the second part of this article, we attempt to solve the basis pursuit denoising BPDN problem (i.e., approximating the minimum 1-norm solution to an underdetermined least squares problem) by using NESTA-LASSO in conjunction with the Pareto root-finding method employed by van den Berg and Friedlander in their SPGL1 solver. The resulting algorithm is called PARNES. We provide numerical evidence to show that it is comparable to currently available solvers.