A family of NCP functions and a descent method for the nonlinear complementarity problem

Jein Shan Chen, Shaohua Pan

研究成果: 雜誌貢獻文章同行評審

66 引文 斯高帕斯(Scopus)

摘要

In last decades, there has been much effort on the solution and the analysis of the nonlinear complementarity problem (NCP) by reformulating NCP as an unconstrained minimization involving an NCP function. In this paper, we propose a family of new NCP functions, which include the Fischer-Burmeister function as a special case, based on a p-norm with p being any fixed real number in the interval (1,+∞), and show several favorable properties of the proposed functions. In addition, we also propose a descent algorithm that is indeed derivative-free for solving the unconstrained minimization based on the merit functions from the proposed NCP functions. Numerical results for the test problems from MCPLIB indicate that the descent algorithm has better performance when the parameter p decreases in (1,+∞). This implies that the merit functions associated with p (1,2), for example p=1.5, are more effective in numerical computations than the Fischer-Burmeister merit function, which exactly corresponds to p=2.

原文英語
頁(從 - 到)389-404
頁數16
期刊Computational Optimization and Applications
40
發行號3
DOIs
出版狀態已發佈 - 2008 七月 1

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

指紋 深入研究「A family of NCP functions and a descent method for the nonlinear complementarity problem」主題。共同形成了獨特的指紋。

引用此