Nonconvex Latent Optimally Partitioned Block-Sparse Recovery via Log-Sum and Minimax Concave Penalties

TL;DR

This paper introduces two novel nonconvex regularization techniques, LogLOP and AdaLOP, for block-sparse signal recovery that improve accuracy and are compatible with various data fidelity terms, supported by efficient algorithms and empirical validation.

Contribution

The paper extends log-sum and MCP penalties to block-sparse recovery with unknown partitions, providing flexible, efficient algorithms and demonstrating superior performance over existing methods.

Findings

Outperforms state-of-the-art baselines in accuracy

Demonstrates stable convergence of ADMM algorithms

Effective in synthetic data, spectrum estimation, and nanopore denoising

Abstract

We propose two nonconvex regularization methods, LogLOP-l2/l1 and AdaLOP-l2/l1, for recovering block-sparse signals with unknown block partitions. These methods address the underestimation bias of existing convex approaches by extending log-sum penalty and the Minimax Concave Penalty (MCP) to the block-sparse domain via novel variational formulations. Unlike Generalized Moreau Enhancement (GME) and Bayesian methods dependent on the squared-error data fidelity term, our proposed methods are compatible with a broad range of data fidelity terms. We develop efficient Alternating Direction Method of Multipliers (ADMM)-based algorithms for these formulations that exhibit stable empirical convergence. Numerical experiments on synthetic data, angular power spectrum estimation, and denoising of nanopore currents demonstrate that our methods outperform state-of-the-art baselines in estimation accuracy.