A proximal MM method for the zero-norm regularized PLQ composite optimization problem

TL;DR

This paper introduces a proximal majorization-minimization method for zero-norm regularized PLQ problems, providing convergence guarantees and demonstrating superior performance over existing methods through numerical experiments.

Contribution

It develops a novel proximal MM algorithm for nonconvex nonsmooth zero-norm regularized PLQ problems, with proven convergence and practical efficiency.

Findings

Global convergence and linear rate established

Method outperforms ADMM in solution quality and speed

Numerical experiments confirm theoretical results

Abstract

This paper is concerned with a class of zero-norm regularized piecewise linear-quadratic (PLQ) composite minimization problems, which covers the zero-norm regularized $\ell_1$-loss minimization problem as a special case. For this class of nonconvex nonsmooth problems, we show that its equivalent MPEC reformulation is partially calm on the set of global optima and make use of this property to derive a family of equivalent DC surrogates. Then, we propose a proximal majorization-minimization (MM) method, a convex relaxation approach not in the DC algorithm framework, for solving one of the DC surrogates which is a semiconvex PLQ minimization problem involving three nonsmooth terms. For this method, we establish its global convergence and linear rate of convergence, and under suitable conditions show that the limit of the generated sequence is not only a local optimum but also a good critical point in a statistical sense. Numerical experiments are conducted with synthetic and real data for the proximal MM method with the subproblems solved by a dual semismooth Newton method to confirm our theoretical findings, and numerical comparisons with a convergent indefinite-proximal ADMM for the partially smoothed DC surrogate verify its superiority in the quality of solutions and computing time.