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Titlebook: Generalized Convexity, Generalized Monotonicity and Applications; Proceedings of the 7 Andrew Eberhard,Nicolas Hadjisavvas,Dinh The Luc Con

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Characterizing Invex and Related PropertiesA characterization of ., given by Glover and Craven, is extended to functions in abstract spaces. . for a vector function coincides with . in a restricted set of directions. The . property of Jeyakumar and Mond is also characterized. Some differentiability properties of the invex . are also obtained.
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Second Order Optimality Conditions for Nonsmooth Multiobjective Optimization ProblemsIn this paper second-order necessary optimality conditions for nonsmooth vector optimization problems are given by smooth approximations. We extend to the vector case the approach introduced by Ermoliev, Norkin and Wets to define generalized derivatives for discontinuous functions as limit of the classical derivatives of regular functions.
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A Plastics Overview: Figures and Tablesussed, the idea of polyhedral combinatorics is outlined and the use of convexity concepts in algorithmic design is shown. Moreover, combinatorial optimization problems arising from convex configurations in the plane are discussed.
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Hillard M. Lazarus,Alvin H. Schmaierproblems we state weak and strong duality theorems based on different generalized concavity assumptions. The proposed dual problems provide a unified framework generalizing Wolfe and Mond-Weir results.
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Standard Operating Procedures (SOP),is transformation preserves pseudoconvexity of a function. The result is then used to characterize sums of two linear fractional functions which are still pseudoconvex. This in turn leads to a characterization of pseudolinear sums of two linear fractional functions.
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(Generalized) Convexity and Discrete Optimizationussed, the idea of polyhedral combinatorics is outlined and the use of convexity concepts in algorithmic design is shown. Moreover, combinatorial optimization problems arising from convex configurations in the plane are discussed.
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