Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Reconstruction with a Convex Constraint

TL;DR

This paper introduces a projected Nesterov's proximal-gradient algorithm with adaptive step size and momentum acceleration for sparse signal reconstruction within convex constraints, demonstrating improved convergence and practical performance.

Contribution

The paper develops a novel PNPG method combining adaptive step size, Nesterov's acceleration, and convex-set constraints, with theoretical convergence proofs and application-independent tuning.

Findings

Effective in tomographic and compressed sensing reconstructions

Achieves $ ext{O}(k^{-2})$ convergence rate

Demonstrates superior performance over existing methods

Abstract

We develop a projected Nesterov's proximal-gradient (PNPG) approach for sparse signal reconstruction that combines adaptive step size with Nesterov's momentum acceleration. The objective function that we wish to minimize is the sum of a convex differentiable data-fidelity (negative log-likelihood (NLL)) term and a convex regularization term. We apply sparse signal regularization where the signal belongs to a closed convex set within the closure of the domain of the NLL; the convex-set constraint facilitates flexible NLL domains and accurate signal recovery. Signal sparsity is imposed using the $\ell_1$-norm penalty on the signal's linear transform coefficients or gradient map, respectively. The PNPG approach employs projected Nesterov's acceleration step with restart and an inner iteration to compute the proximal mapping. We propose an adaptive step-size selection scheme to obtain a good local majorizing function of the NLL and reduce the time spent backtracking. Thanks to step-size adaptation, PNPG does not require Lipschitz continuity of the gradient of the NLL. We present an integrated derivation of the momentum acceleration and its $\mathcal{O}(k^{-2})$ convergence-rate and iterate convergence proofs, which account for adaptive step-size selection, inexactness of the iterative proximal mapping, and the convex-set constraint. The tuning of PNPG is largely application-independent. Tomographic and compressed-sensing reconstruction experiments with Poisson generalized linear and Gaussian linear measurement models demonstrate the performance of the proposed approach.