Numerical comparisons of two effective methods for mixed complementarity problems

Jein Shan Chen*, Shaohua Pan, Ching Yu Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Recently there have two different effective methods proposed by Kanzow et al. in (Kanzow, 2001 [8]) and (Kanzow and Petra, 2004 [9]), respectively, which commonly use the Fischer-Burmeister (FB) function to recast the mixed complementarity problem (MCP) as a constrained minimization problem and a nonlinear system of equations, respectively. They all remark that their algorithms may be improved if the FB function is replaced by other NCP functions. Accordingly, in this paper, we employ the generalized Fischer-Burmeister (GFB) where the 2-norm in the FB function is relaxed to a general p-norm (p > 1) for the two methods and investigate how much the improvement is by changing the parameter p as well as which method is influenced more when we do so, by the performance profiles of iterations and function evaluations for the two methods with different p on MCPLIB collection.

Original languageEnglish
Pages (from-to)667-683
Number of pages17
JournalJournal of Computational and Applied Mathematics
Issue number3
Publication statusPublished - 2010 Jun 1


  • Convergence rate
  • MCP
  • Semismooth
  • The generalized FB function

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics


Dive into the research topics of 'Numerical comparisons of two effective methods for mixed complementarity problems'. Together they form a unique fingerprint.

Cite this