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Convergence Analysis of a Class of Nonlinear Penalization Methods for Constrained Optimization via First-Order Necessary Optimality Conditions

Author

Listed:
  • X.X. Huang

    (Chongqing Normal University)

  • X.Q. Yang

    (Hong Kong Polytechnic University)

Abstract

We propose a scheme to solve constrained optimization problems by combining a nonlinear penalty method and a descent method. A sequence of nonlinear penalty optimization problems is solved to generate a sequence of stationary points, i.e., each point satisfies a first-order necessary optimality condition of a nonlinear penalty problem. Under some conditions, we show that any limit point of the sequence satisfies the first-order necessary condition of the original constrained optimization problem.

Suggested Citation

  • X.X. Huang & X.Q. Yang, 2003. "Convergence Analysis of a Class of Nonlinear Penalization Methods for Constrained Optimization via First-Order Necessary Optimality Conditions," Journal of Optimization Theory and Applications, Springer, vol. 116(2), pages 311-332, February.
    • Handle: RePEc:spr:joptap:v:116:y:2003:i:2:d:10.1023_a:1022503820909
    DOI: 10.1023/A:1022503820909
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    References listed on IDEAS

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    1. X. Q. Yang & V. Jeyakumar, 1997. "First and Second-Order Optimality Conditions for Convex Composite Multiobjective Optimization," Journal of Optimization Theory and Applications, Springer, vol. 95(1), pages 209-224, October.
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    Cited by:

    1. Duan Yaqiong & Lian Shujun, 2016. "Smoothing Approximation to the Square-Root Exact Penalty Function," Journal of Systems Science and Information, De Gruyter, vol. 4(1), pages 87-96, February.

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