Titlebook: Bayesian Heuristic Approach to Discrete and Global Optimization; Algorithms, Visualiz Jonas Mockus,William Eddy,Gintaras Reklaitis Book 199

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书目名称Bayesian Heuristic Approach to Discrete and Global Optimization影响因子(影响力)




书目名称Bayesian Heuristic Approach to Discrete and Global Optimization影响因子(影响力)学科排名




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书目名称Bayesian Heuristic Approach to Discrete and Global Optimization网络公开度学科排名




书目名称Bayesian Heuristic Approach to Discrete and Global Optimization被引频次




书目名称Bayesian Heuristic Approach to Discrete and Global Optimization被引频次学科排名




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书目名称Bayesian Heuristic Approach to Discrete and Global Optimization读者反馈学科排名

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Mathematical Justification of the Bayesian Heuristics Approachefficiency is the results of real life problems [30, 44, 115, 63]. Needless to say, one should use any available mathematical justification, too. An important condition is to regard the theoretical and experimental results in right proportions.

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Long-Memory Processes and Exchange Rate Forecastinged the attention of many researchers in recent years [29, 16, 161, 17, 81, 103]. The frequency of the use of ARFIMA modeling in empirical research underscores the importance of efficient, both computational and statistical, estimation of the models. In estimating the parameters of the ARFIMA models,

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Application of Global Line-Search in the Optimization of NetworksIt is shown that GLS provides the global minimum after a finite number of steps in two cases of piecewise linear cost functions of arcs. The first case is, where all cost functions are convex. The second case is, where all costs are equal to zero at zero flow, and equal to some constant at non-zero

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Solving Differential Equations by Event- Driven Techniques for Parameter Optimizationtor circuits. We apply the well known event-driven techniques to obtain an approximate solution fast. We extend these techniques by considering pairs of nodes, instead of single nodes. The “twin-node” technique is more efficient in tightly coupled circuits as compared with the “single-node” one. The

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Optimization in Neural Networkss from real biological systems. These units are regarded as simplification of biological neurons what explains the ANN term. The information in ANN is stored in “weights” of connections between the units. It is assumed that ANN “adapts” to a problem by changing these weights.

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Bayesian Approach to Discrete Optimizationtimization of heuristic parameters using the BHA. Therefore we consider the discrete optimization as the main area of BHA application. The representation of discrete optimization as a multi-stage decision problem is also a convenient way to show how BHA works.