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Titlebook: Non-Linear Parametric Optimization; B. Bank,J. Guddat,K. Tammer Book 1982 Springer Basel AG 1982 mathematical programming.mathematics.opti

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书目名称Non-Linear Parametric Optimization
编辑B. Bank,J. Guddat,K. Tammer
视频video
图书封面Titlebook: Non-Linear Parametric Optimization;  B. Bank,J. Guddat,K. Tammer Book 1982 Springer Basel AG 1982 mathematical programming.mathematics.opti
出版日期Book 1982
关键词mathematical programming; mathematics; optimization
版次1
doihttps://doi.org/10.1007/978-3-0348-6328-5
isbn_softcover978-3-0348-6330-8
isbn_ebook978-3-0348-6328-5
copyrightSpringer Basel AG 1982
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B. Bank,J. Guddat,D. Klatte,B. Kummer,K. Tammeras Wesen der irreversiblen Prozesse erfaßte und über Boltzmann (1871 und 1879) eine Formulierung mit Hilfe des Wahrscheinlichkeitsbegriffs erfuhr. Die bis heute unbestrittene Universalität des II. Hauptsatzes hat Gibbs (1902) damit erklärt, daß dieser Satz an keine bestimmte Modellvorstellung im Mik
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B. Bank,J. Guddat,D. Klatte,B. Kummer,K. Tammeras Wesen der irreversiblen Prozesse erfaßte und über Boltzmann (1871 und 1879) eine Formulierung mit Hilfe des Wahrscheinlichkeitsbegriffs erfuhr. Die bis heute unbestrittene Universalität des II. Hauptsatzes hat Gibbs (1902) damit erklärt, daß dieser Satz an keine bestimmte Modellvorstellung im Mik
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General Introduction,facets of the real-life situation and then only at some level of approximation. In the following considerations we assume that the functional relations between the various quantities of interest have been sufficiently well established. The quality of an optimization model then depends strongly on th
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Properties of Characteristic Parameter Sets for Special Classes of Optimization Problems,to linear normed spaces. We are concerned with characteristic parameter sets for various classes of parametric optimization problems, such sets are the feasible parameter set ? (the set of parameters for which the constraint set is non-empty) and the solubility set A, i.e. the set of parameters for
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On Procedures for Analysing Parametric Optimization Problems,xample . [1], . [1], . [5], and . [1]) and stochastic optimization (. [1], [2], [5], . [1], . [1], [2], and . [7], [8], [11]). The practical exploitation of this potential however involves the need for efficient procedures for the analysis of parameter-dependent optimization problems. It is first ne
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