发表机构
School of Informatics, University of Edinburgh; School of Computer Science, State Key Laboratory for Novel Software Technology, New Cornerstone Science Laboratory, Nanjing University; Department of Computer Science, University of California, Santa Barbara（爱丁堡大学信息学院; 南京大学计算机学院; 加州大学圣塔芭芭拉分校计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出并分析了一种非自适应模拟退火算法以估计吉布斯分布的配分函数，实现了迄今最有效的归约，并通过下界证明其在广泛参数范围内最优。
AI 中文摘要
本文给出了一种用于估计吉布斯分布配分函数的非自适应模拟退火算法的简单分析。该分析产生了迄今为止此类问题中最有效的归约方法。我们还为一般算法和非自适应算法建立了下界，表明我们的算法在广泛的参数范围内是最优的。
英文摘要
In this note, we give a simple analysis of a non-adaptive simulated annealing algorithm for estimating the partition function of Gibbs distributions. This yields the most efficient reduction of this kind so far. We also establish lower bounds for both general and non-adaptive algorithms, showing that our algorithm is optimal over a broad range of parameters.
