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Nonmonotone Line Search Algorithm for Constrained Minimax Problems

Author

Listed:
  • Y.H. Yu

    (Peking University)

  • L. Gao

    (Peking University)

Abstract

In this paper, an algorithm for constrained minimax problems is presented which is globally convergent and whose rate of convergence is two-step superlinear. The algorithm applies SQP to the constrained minimax problems by combining a nonmonotone line search and a second-order correction technique, which guarantees a full steplength while close to a solution, such that the Maratos effect is avoided and two-step superlinear convergence is achieved.

Suggested Citation

  • Y.H. Yu & L. Gao, 2002. "Nonmonotone Line Search Algorithm for Constrained Minimax Problems," Journal of Optimization Theory and Applications, Springer, vol. 115(2), pages 419-446, November.
    • Handle: RePEc:spr:joptap:v:115:y:2002:i:2:d:10.1023_a:1020896407415
    DOI: 10.1023/A:1020896407415
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    Cited by:

    1. Fusheng Wang, 2013. "A hybrid algorithm for linearly constrained minimax problems," Annals of Operations Research, Springer, vol. 206(1), pages 501-525, July.
    2. Ping Hu & Xu-Qing Liu, 2013. "A Nonmonotone Line Search Slackness Technique for Unconstrained Optimization," Journal of Optimization Theory and Applications, Springer, vol. 158(3), pages 773-786, September.
    3. Jin-bao Jian & Qing-juan Hu & Chun-ming Tang, 2014. "Superlinearly Convergent Norm-Relaxed SQP Method Based on Active Set Identification and New Line Search for Constrained Minimax Problems," Journal of Optimization Theory and Applications, Springer, vol. 163(3), pages 859-883, December.

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