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Titlebook: Generalized Convexity and Generalized Monotonicity; Proceedings of the 6 Nicolas Hadjisavvas,Juan Enrique Martínez-Legaz,Je Conference proc

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書目名稱Generalized Convexity and Generalized Monotonicity
副標(biāo)題Proceedings of the 6
編輯Nicolas Hadjisavvas,Juan Enrique Martínez-Legaz,Je
視頻videohttp://file.papertrans.cn/383/382189/382189.mp4
叢書名稱Lecture Notes in Economics and Mathematical Systems
圖書封面Titlebook: Generalized Convexity and Generalized Monotonicity; Proceedings of the 6 Nicolas Hadjisavvas,Juan Enrique Martínez-Legaz,Je Conference proc
出版日期Conference proceedings 2001
關(guān)鍵詞Convexity; Generalized convexity; Konvexit?t; Monotonicity; differential equation; generalisierte Konvexi
版次1
doihttps://doi.org/10.1007/978-3-642-56645-5
isbn_softcover978-3-540-41806-1
isbn_ebook978-3-642-56645-5Series ISSN 0075-8442 Series E-ISSN 2196-9957
issn_series 0075-8442
copyrightSpringer-Verlag Berlin Heidelberg 2001
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Discrete Higher Order Convex Functions and their Applicationsrandom variables involved are discrete. We look for the minimum or maximum of a linear functional acting on an unknown probability distribution subject to a finite number of moment constraints. Using linear programming methodology, we present structural theorems, in both the univariate and multivari
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Cuts and Semidefinite Relaxations for Nonconvex Quadratic Problemsedure can be applied for generating tighter positive semidefinite relaxations. Computational results indicate that the relative gap obtained by the relaxations is less than 1% when the number of variables is up to 100. We also show a branch and bound procedure for obtaining an exact solution of the
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The Steiner Ratio of ,y”, that means, given a finite set . of points in the plane, search for a network interconnecting these points with minimal length. This shortest network must be a tree and is called a Steiner Minimal Tree (SMT). It may contain vertices different from the points which axe to be connected. Such point
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Normal Cones to Sublevel Sets: An Axiomatic Approach the definition given in [.] (resp. [.]) is recovered. Moreover, the results obtained in [.] are extended in this more general setting. Under mild assumptions, quasiconvex continuous functions are classified, establishing an equivalence relation between functions with the same normal operator. Appli
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