Sparsity-Guided Multi-Parameter Selection in $\ell_1$-Regularized Models via a Fixed-Point Proximity Approach

TL;DR

This paper introduces a fixed-point proximity algorithm for multi-parameter selection in $\, ext{l}_1$-regularized models, enabling controlled structured sparsity with theoretical guarantees and practical effectiveness.

Contribution

It develops a novel fixed-point proximity method for joint computation of solutions and auxiliary vectors, facilitating precise sparsity control in multi-penalty models.

Findings

The method reliably achieves prescribed sparsity patterns.

It demonstrates strong approximation accuracy in numerical experiments.

Provides a theoretical relationship between parameters and sparsity.

Abstract

We study a regularization framework that combines a convex fidelity term with multiple $\ell_1$-based regularizers, each linked to a distinct linear transform. This multi-penalty model enhances flexibility in promoting structured sparsity. We analyze how the choice of regularization parameters governs the sparsity of solutions under the given transforms and derive a precise relationship between the parameters and resulting sparsity patterns. This insight enables the development of an iterative strategy for selecting parameters to achieve prescribed sparsity levels. A key computational challenge arises in practice: effective parameter tuning requires simultaneous access to the regularized solution and two auxiliary vectors derived from the sparsity analysis. To address this, we propose a fixed-point proximity algorithm that jointly computes all three vectors. Together with our theoretical characterization, this algorithm forms the basis of a practical multi-parameter selection scheme. Numerical experiments demonstrate that the proposed method reliably produces solutions with desired sparsity patterns and strong approximation accuracy.