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Titlebook: Lineare Optimierung; Modell, L?sung, Anwe Thomas Unger,Stephan Dempe Textbook 2010 Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden Gm

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21#
發(fā)表于 2025-3-25 06:58:11 | 只看該作者
Innere-Punkte-Methode,h gr??ere Aufgaben zu l?sen vermag. Da die Rechenzeit eines Algorithmus von den Daten, von der Darstellung der Zahlen im Computer und manchmal auch vom Zufall abh?ngt, wollen wir uns auf den schlechtestm?glichen Fall beschr?nken. Zur Berechnung der Rechenzeit addiert man zun?chst die elementaren Rec
22#
發(fā)表于 2025-3-25 07:37:23 | 只看該作者
23#
發(fā)表于 2025-3-25 12:09:40 | 只看該作者
Thomas Unger,Stephan Dempessary for students interested in applications to engineering and the sciences. Although it is aimed primarily at upperclassmen and beginning graduate students, the only prere- quisite is the standard calculus course usually required of under- graduates in engineering and science. Most beginning stud
24#
發(fā)表于 2025-3-25 17:07:59 | 只看該作者
25#
發(fā)表于 2025-3-25 21:49:11 | 只看該作者
26#
發(fā)表于 2025-3-26 04:06:09 | 只看該作者
Thomas Unger,Stephan Dempen methods of Monte Carlo integration using R..Gibbs samplingThe first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous stat
27#
發(fā)表于 2025-3-26 07:48:47 | 只看該作者
28#
發(fā)表于 2025-3-26 11:25:35 | 只看該作者
Thomas Unger,Stephan Dempen methods of Monte Carlo integration using R..Gibbs samplingThe first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous stat
29#
發(fā)表于 2025-3-26 14:02:21 | 只看該作者
Thomas Unger,Stephan Dempen methods of Monte Carlo integration using R..Gibbs samplingThe first seven chapters use R for probability simulation and computation, including random number generation, numerical and Monte Carlo integration, and finding limiting distributions of Markov Chains with both discrete and continuous stat
30#
發(fā)表于 2025-3-26 18:30:53 | 只看該作者
Thomas Unger,Stephan Dempeand finding limiting distributions of Markov Chains with both discrete and continuous states. Applications include coverage probabilities of binomial confidence intervals, estimation of disease prevalence from screening tests, parallel redundancy for improved reliability of systems, and various kind
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