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Titlebook: Generalized Convexity; Proceedings of the I Sándor Komlósi,Tamás Rapcsák,Siegfried Schaible Conference proceedings 1994 Springer-Verlag Ber

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51#
發(fā)表于 2025-3-30 10:52:33 | 只看該作者
A survey on optimality and duality in nonsmooth programmingis study is the Clarke subdifferential redefined for arbitrary nonlinear functions by the help of the generalized directional derivative of Clarke. In particular, the generalized Kuhn-Tucker sufficient optimality conditions and variants of Mond-Weir duality results are reviewed.
52#
發(fā)表于 2025-3-30 13:03:52 | 只看該作者
0075-8442 exity, this book presents severalimportantdevelopments in recently emerging areas. Also, a number ofinteresting applications are reported.978-3-540-57624-2978-3-642-46802-5Series ISSN 0075-8442 Series E-ISSN 2196-9957
53#
發(fā)表于 2025-3-30 18:38:55 | 只看該作者
Univex sets, functions and univex nonlinear programmingnify the concepts of convexity, B-vexity, invexity and B-invexity. Some of their properties are proved and applications in nonlinear programming are discussed. Furthermore, generalized univex functions are also introduced and their relationships to univex functions and convex (generalized convex) fu
54#
發(fā)表于 2025-3-30 23:46:24 | 只看該作者
55#
發(fā)表于 2025-3-31 04:41:57 | 只看該作者
56#
發(fā)表于 2025-3-31 06:56:48 | 只看該作者
57#
發(fā)表于 2025-3-31 10:06:40 | 只看該作者
Equivalence between generalized gradients and subdifferentials for a suitable class of lower semiconich have a .-monotone subdifferential of order one (see def. (2.1)), the proximal subgradient, the lower semigradient and the generalized gradient coincide. This class includes, for instance, the convex functions, the .. perturbations of convex functions, the primal lower-nice functions (see [16]).
58#
發(fā)表于 2025-3-31 13:21:34 | 只看該作者
Generalizing convexity for second order optimality conditionson for a constrained minimization problem. In this paper we present, in section 1, an appropriate generalization of local and global convexity, which takes into account the structure of the feasible set and thus enables us to narrow the usual gap between necessary and sufficient optimality condition
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