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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Cross-entropy method ： ウィキペディア英語版 Cross-entropy method The cross-entropy (CE) method attributed to Reuven Rubinstein is a general Monte Carlo approach to combinatorial and continuous multi-extremal optimization and importance sampling. The method originated from the field of ''rare event simulation'', where very small probabilities need to be accurately estimated, for example in network reliability analysis, queueing models, or performance analysis of telecommunication systems. The CE method can be applied to static and noisy combinatorial optimization problems such as the traveling salesman problem, the quadratic assignment problem, DNA sequence alignment, the max-cut problem and the buffer allocation problem, as well as continuous global optimization problems with many local extrema. In a nutshell the CE method consists of two phases: #Generate a random data sample (trajectories, vectors, etc.) according to a specified mechanism. #Update the parameters of the random mechanism based on the data to produce a "better" sample in the next iteration. This step involves minimizing the ''cross-entropy'' or Kullback–Leibler divergence. ==Estimation via importance sampling== Consider the general problem of estimating the quantity $\backslash ell\; =\; \backslash mathbb\_)"\; TITLE="H(\backslash mathbf)">)\; =\; \backslash int\; H(\backslash mathbf)\backslash ,\; f(\backslash mathbf;\; \backslash mathbf)\backslash ,\; \backslash textrm\backslash mathbf$, where $H$ is some ''performance function'' and $f(\backslash mathbf;\backslash mathbf)$ is a member of some parametric family of distributions. Using importance sampling this quantity can be estimated as $\backslash hat\; =\; \backslash frac\; \backslash sum\_^N\; H(\backslash mathbf\_i)\; \backslash frac)\}$, where $\backslash mathbf\_1,\backslash dots,\backslash mathbf\_N$ is a random sample from $g\backslash ,$. For positive $H$, the theoretically ''optimal'' importance sampling density (pdf) is given by $g^$ *(\mathbf) = H(\mathbf) f(\mathbf;\mathbf)/\ell. This, however, depends on the unknown $\backslash ell$. The CE method aims to approximate the optimal PDF by adaptively selecting members of the parametric family that are closest (in the Kullback–Leibler sense) to the optimal PDF $g^$ *. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Cross-entropy method」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.