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A Globally Convergent Sequential Quadratic Programming Algorithm for Mathematical Programs with Linear Complementarity Constraints

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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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Authors and Affiliations

  1. Department of Applied Mathematics and Physics, Graduate School of Engineering, Kyoto University, Kyoto, 606-01, Japan

    Masao Fukushima

  2. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ont., Canada, L8S 4K1

    Zhi-Quan Luo

  3. Department of Mathematical Sciences, Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, 21218-2682, U.S.A.

    Jong-Shi Pang

Authors
  1. Masao Fukushima
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  2. Zhi-Quan Luo
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  3. Jong-Shi Pang
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Fukushima, M., Luo, ZQ. & Pang, JS. A Globally Convergent Sequential Quadratic Programming Algorithm for Mathematical Programs with Linear Complementarity Constraints. Computational Optimization and Applications 10, 5–34 (1998). https://doi.org/10.1023/A:1018359900133

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  • DOI: https://doi.org/10.1023/A:1018359900133

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