A globally convergent sequential quadratic programming algorithm for mathematical programs with linear complementarity constraints

A globally convergent sequential quadratic programming algorithm for mathematical programs with linear complementarity constraints

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Article ID: iaor19992039
Country: Netherlands
Volume: 10
Issue: 1
Start Page Number: 5
End Page Number: 34
Publication Date: Apr 1998
Journal: Computational Optimization and Applications
Authors: , ,
Keywords: gradient methods
Abstract:

This paper presents a sequential quadratic programming algorithm for computing a stationary point of a mathematical program with linear complementarity constraints. The algorithm is based on a reformulation of the complementarity condition as a system of semismooth equations by means of Fischer–Burmeister functional, combined with a classical penalty function method for solving constrained optimization problems. Global convergence of the algorithm is established under appropriate assumptions. Some preliminary computational results are reported.

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