Coderivative-based semi-Newton method in nonsmooth difference programming

• 成果类型: Article
• 署名作者: Aragon-Artacho, Francisco J.; Mordukhovich, Boris S.; Perez-Aros, Pedro
• 署名单位: Universitat d'Alacant; Wayne State University; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-024-02142-8
• 发表日期: 2025
• 页码: 385-432
• 关键词: prox-regularity; tilt stability; algorithm

摘要

This paper addresses the study of a new class of nonsmooth optimization problems, where the objective is represented as a difference of two generally nonconvex functions. We propose and develop a novel Newton-type algorithm to solving such problems, which is based on the coderivative generated second-order subdifferential (generalized Hessian) and employs advanced tools of variational analysis. Well-posedness properties of the proposed algorithm are derived under fairly general requirements, while constructive convergence rates are established by using additional assumptions including the Kurdyka-& Lstrok;ojasiewicz condition. We provide applications of the main algorithm to solving a general class of nonsmooth nonconvex problems of structured optimization that encompasses, in particular, optimization problems with explicit constraints. Finally, applications and numerical experiments are given for solving practical problems that arise in biochemical models, supervised learning, constrained quadratic programming, etc., where advantages of our algorithms are demonstrated in comparison with some known techniques and results.