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

X.X. Huang and X.Q. Yang
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X.X. Huang: Chongqing Normal University
X.Q. Yang: Hong Kong Polytechnic University

Journal of Optimization Theory and Applications, 2003, vol. 116, issue 2, No 5, 332 pages

Abstract: 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.

Keywords: Nonlinear penalization; necessary optimality conditions; differentiability; locally; Lipschitz functions; smooth approximate variational principle (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1023/A:1022503820909

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