Smoothing penalty approach for solving second-order cone complementarity problems

被引：1

作者：

Nguyen, Chieu Thanh ^{[1
]}

Alcantara, Jan Harold ^{[2
]}

Hao, Zijun ^{[3
]}

Chen, Jein-Shan ^{[4
]}

机构：

[1] Vietnam Natl Univ Agr, Fac Informat Technol, Dept Math, Hanoi 131000, Vietnam

[2] RIKEN, Ctr Adv Intelligence Project, Tokyo 1030027, Japan

[3] North Minzu Univ, Sch Math & Informat Sci, Yinchuan 750021, Peoples R China

[4] Natl Taiwan Normal Univ, Dept Math, Taipei 116059, Taiwan

来源：

JOURNAL OF GLOBAL OPTIMIZATION | 2025年 / 91卷 / 01期

关键词：

Second-order cone; Nonlinear complementarity problem; Penalty method; MATRIX-SPLITTING METHOD; REGULARIZATION METHOD; NEURAL-NETWORK; NEWTON METHODS; REFORMULATION; CONVERGENCE; PROGRAMS;

D O I：

10.1007/s10898-024-01427-8

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

In this paper, we propose a smoothing penalty approach for solving the second-order cone complementarity problem (SOCCP). The SOCCP is approximated by a smooth nonlinear equation with penalization parameter. We show that any solution sequence of the approximating equations converges to the solution of the SOCCP under the assumption that the associated function of the SOCCP satisfies a uniform Cartesian-type property. We present a corresponding algorithm for solving the SOCCP based on this smoothing penalty approach, and we demonstrate the efficiency of our method for solving linear, nonlinear and tensor complementarity problems in the second-order cone setting.

引用

页码：39 / 58

页数：20

共 36 条

[1] Unified smoothing approach for best hyperparameter selection problem using a bilevel optimization strategy [J].