On saddle points in nonconvex semi-infinite programming
AbstractIn this paper we apply two convexification procedures to the Lagrangian of a nonconvex semi-infinite programming problem. Under the reduction approach it is shown that, locally around a local minimizer, this problem can be transformed equivalently in such a way that the transformed Lagrangian fulfills saddle point optimality conditions, where for the first procedure both the original objective function and constraints (and for the second procedure only the constraints) are substituted by their pth powers with sufficiently large power p. These results allow that local duality theory and corresponding numerical methods (e.g. dual search) can be applied to a broader class of nonconvex problems. Copyright Springer Science+Business Media, LLC. 2012
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Bibliographic InfoArticle provided by Springer in its journal Journal of Global Optimization.
Volume (Year): 54 (2012)
Issue (Month): 3 (November)
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Web page: http://www.springer.com/business/operations+research/journal/10898
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- Nader Kanzi, 2011. "Necessary optimality conditions for nonsmooth semi-infinite programming problems," Journal of Global Optimization, Springer, vol. 49(4), pages 713-725, April.
- Qingxiang Zhang, 2009. "Optimality conditions and duality for semi-infinite programming involving B-arcwise connected functions," Computational Optimization and Applications, Springer, vol. 45(4), pages 615-629, December.
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