On convergence of a global search strategy for reverse convex problems.
Strekalovsky, Alexander (2005)
Journal of Applied Mathematics and Decision Sciences
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Displaying similar documents to “Reverse convex problems: an approach based on optimality conditions.”
On convergence of a global search strategy for reverse convex problems.
A Trust Region Method Using Subgradient for Minimizing a Nondifferentiable Function
Milanka Gardašević Filipović (2009)
The Yugoslav Journal of Operations Research
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Solving convex program via Lagrangian decomposition
Matthias Knobloch (2004)
Kybernetika
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We consider general convex large-scale optimization problems in finite dimensions. Under usual assumptions concerning the structure of the constraint functions, the considered problems are suitable for decomposition approaches. Lagrangian-dual problems are formulated and solved by applying a well-known cutting-plane method of level-type. The proposed method is capable to handle infinite function values. Therefore it is no longer necessary to demand the feasible set with respect to the...
Bilevel programs with extremal value function: global optimality.
Aboussoror, Abdelmalek, Babahadda, Hicham, Mansouri, Abdelatif (2005)
International Journal of Mathematics and Mathematical Sciences
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On globally solving linearly constrained indefinite quadratic minimization problems by decomposition branch and bound method
Thaï Quynh Phong, Le Thi Hoai An, Pham Dinh Tao (1996)
RAIRO - Operations Research - Recherche Opérationnelle
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Strict minimizers of order in nonsmooth optimization problems
Tadeusz Antczak, Krzysztof Kisiel (2006)
Commentationes Mathematicae Universitatis Carolinae
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In the paper, some sufficient optimality conditions for strict minima of order in constrained nonlinear mathematical programming problems involving (locally Lipschitz) -convex functions of order are presented. Furthermore, the concept of strict local minimizer of order is also used to state various duality results in the sense of Mond-Weir and in the sense of Wolfe for such nondifferentiable optimization problems.