Results 231 to 240 of about 632,408 (276)
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2000
In this chapter a theory of discrete optimization called discrete convex analysis will be introduced. For a comprehensive treatment we refer the reader to Stoer and Witzgall [227], Fujishige [96, Chapter IV], and Murota [179] [181]. Martinez-Legaz [165] seems to have been the first writer to apply the discrete convex analysis to the cooperative game ...
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In this chapter a theory of discrete optimization called discrete convex analysis will be introduced. For a comprehensive treatment we refer the reader to Stoer and Witzgall [227], Fujishige [96, Chapter IV], and Murota [179] [181]. Martinez-Legaz [165] seems to have been the first writer to apply the discrete convex analysis to the cooperative game ...
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Abstract Convexity in Measure Theory and in Convex Analysis
Journal of Mathematical Sciences, 2003The paper under report is a survey on the so-called ``abstract convex analysis'' and on some applications to optimization problems. If \(\Omega\) is a set and \(H\) is a class of functions from \(\Omega\) into \(\mathbb{R}\), a function \(f: \Omega\to\mathbb{R}\cup \{+\infty\}\) is called \(H\)-convex if it is the supremum of a family of functions ...
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1999
Convex geometry is at once simple and amazingly rich. While the classical results go back many decades, during that previous to this book's publication in 1999, the integral geometry of convex bodies had undergone a dramatic revitalization, brought about by the introduction of methods, results and, most importantly, new viewpoints, from probability ...
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Convex geometry is at once simple and amazingly rich. While the classical results go back many decades, during that previous to this book's publication in 1999, the integral geometry of convex bodies had undergone a dramatic revitalization, brought about by the introduction of methods, results and, most importantly, new viewpoints, from probability ...
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Convex Discriminant Analysis Tools for Non Convex Pattern Recognition
2002The estimation of convex sets when inside and outside observations are available is often needed in current research applications.
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Non-probabilistic polygonal convex set model for structural uncertainty quantification
Applied Mathematical Modelling, 2021Jie Liu, Chao Jiang, Rengui Bi
exaly