期刊
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
卷 42, 期 3, 页码 769-788出版社
SIAM PUBLICATIONS
DOI: 10.1137/S0363012901398393
关键词
generalized semi-infinite optimization; convexity; Stackelberg game; nonlinear complementarity problem function; smoothing; optimality condition
We introduce a new numerical solution method for semi-infinite optimization problems with convex lower level problems. The method is based on a reformulation of the semi-infinite problem as a Stackelberg game and the use of regularized nonlinear complementarity problem functions. This approach leads to central path conditions for the lower level problems, where for a given path parameter a smooth nonlinear finite optimization problem has to be solved. The solution of the semi-infinite optimization problem then amounts to driving the path parameter to zero. We show convergence properties of the method and give a number of numerical examples from design centering and from robust optimization, where actually so-called generalized semi-infinite optimization problems are solved. The presented method is easy to implement, and in our examples it works well for dimensions of the semi-infinite index set at least up to 150.
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