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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-03-02
摘要
族群 annealing is an easily parallelizable sequential 蒙地卡羅 algorithm that is well suited for simulating the equilibrium properties of systems with rough free-energy landscapes. In this work we seek to understand and improve the performance of 族群 annealing. We derive several useful relations between quantities that describe the performance of 族群 annealing and use these relations to suggest methods to optimize the algorithm. These optimization methods were tested by performing large-scale simulations of the three-dimensional (3D) Edwards-Anderson (Ising) spin glass and measuring several observables. The optimization methods were found to substantially decrease the amount of 計算 work necessary as compared to previously used, unoptimized versions of 族群 annealing. We also obtain more accurate valu
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