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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 Stochastic tunneling ： ウィキペディア英語版 Stochastic tunneling In numerical analysis, stochastic tunneling (STUN) is an approach to global optimization based on the Monte Carlo method-sampling of the function to be objective minimized in which the function is nonlinearly transformed to allow for easier tunneling among regions containing function minima. Easier tunneling allows for faster exploration of sample space and faster convergence to a good solution. == Idea == Monte Carlo method-based optimization techniques sample the objective function by randomly "hopping" from the current solution vector to another with a difference in the function value of $\backslash Delta\; E$. The acceptance probability of such a trial jump is in most cases chosen to be $\backslash min\backslash left(1;\backslash exp\backslash left(-\backslash beta\backslash cdot\backslash Delta\; E\backslash right)\backslash right)$ (Metropolis criterion) with an appropriate parameter $\backslash beta$. The general idea of STUN is to circumvent the slow dynamics of ill-shaped energy functions that one encounters for example in spin glasses by tunneling through such barriers. This goal is achieved by Monte Carlo sampling of a transformed function that lacks this slow dynamics. In the "standard-form" the transformation reads $f\_:=1-\backslash exp\backslash left($ -\gamma\cdot\left( E(x)-E_o\right) \right) where $E\_o$ is the lowest function value found so far. This transformation preserves the loci of the minima. $f\_$ is then used in place of $E$ in the original algorithm giving a new acceptance probability of $\backslash min\backslash left(1;\backslash exp\backslash left(-\backslash beta\backslash cdot\backslash Delta\; f\_\backslash right)\backslash right)$ The effect of such a transformation is shown in the graph. 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「Stochastic tunneling」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.