Proximal point algorithm for nonlinear complementarity problem based on the generalized fischer-burmeister merit function

Yu Lin Chang*, Jein Shan Chen, Jia Wu

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

This paper is devoted to the study of the proximal point algorithm for solving monotone and nonmonotone nonlinear complementarity problems. The proximal point algorithm is to generate a sequence by solving subproblems that are regularizations of the original problem. After given an appropriate criterion for approximate solutions of subproblems by adopting a merit function, the proximal point algorithm is verified to have global and superlinear convergence properties. For the purpose of solving the subproblems efficiently, we introduce a generalized Newton method and show that only one Newton step is eventually needed to obtain a desired approximate solution that approximately satisfies the appropriate criterion under mild conditions. The motivations of this paper are twofold. One is analyzing the proximal point al-gorithm based on the generalized Fischer-Burmeister function which includes the Fischer-Burmeister function as special case, another one is trying to see if there are relativistic change on numerical performance when we adjust the parameter in the generalized Fischer-Burmeister.

原文英語
頁(從 - 到)153-169
頁數17
期刊Journal of Industrial and Management Optimization
9
發行號1
DOIs
出版狀態已發佈 - 2013

ASJC Scopus subject areas

  • 商業與國際管理
  • 策略與管理
  • 控制和優化
  • 應用數學

指紋

深入研究「Proximal point algorithm for nonlinear complementarity problem based on the generalized fischer-burmeister merit function」主題。共同形成了獨特的指紋。

引用此