Solving mathematical programs with complementarity constraints as nonlinear programs

Roger Fletcher, Sven Leyffer (Lead / Corresponding author)

Research output: Contribution to journalArticle

134 Citations (Scopus)

Abstract

We consider solving mathematical programs with complementarity constraints (MPCCs) as nonlinear programs (NLPs) using standard NLP solvers. This approach is appealing because it allows existing off-the-shelf NLP solvers to tackle large instances of MPCCs. Numerical experience on MacMPEC, a large collection of MPCC test problems is presented. Our experience indicates that sequential quadratic programming (SQP) methods are very well suited for solving MPCCs and at present outperform interior-point solvers both in terms of speed and reliability. All NLP solvers also compare very favorably to special MPCC solvers on tests published in the literature.

Original languageEnglish
Pages (from-to)15-40
Number of pages26
JournalOptimization Methods and Software
Volume19
Issue number1 SPEC. ISS.
DOIs
Publication statusPublished - 1 Feb 2004

Keywords

  • Complementarity constraints
  • Interior-point methods
  • MPCC
  • Nonlinear programming
  • Sequential quadratic programming

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