An unconstrained differentiable penalty method for implicit complementarity problems

Boshi Tian, Donghui Li, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

ÂIn this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform ξ-P-function. Instead of solving the unconstrained penalized equations directly, we consider a corresponding unconstrained optimization problem and apply the trust-region Gauss–Newton method to solve it. We prove that the local solution of the unconstrained optimization problem identifies that of the complementarity problems under monotone assumptions. We carry out numerical experiments on the test problems from MCPLIB, and show that the proposed method is efficient and robust.
Original languageEnglish
Pages (from-to)775-790
Number of pages16
JournalOptimization Methods and Software
Volume31
Issue number4
DOIs
Publication statusPublished - 3 Jul 2016

Keywords

  • exponential convergence rate
  • implicit complementarity problems
  • lower order penalty method
  • trust-region Gauss–Newton method

ASJC Scopus subject areas

  • Software
  • Control and Optimization
  • Applied Mathematics

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