摘要翻译：
模拟退火算法是求解全局优化问题的一种常用方法。已有的关于其性能的结果适用于离散组合优化，其中优化变量只能假设有限组的可能值。本文提出了一种新的模拟退火算法，它保证了连续变量函数优化的有限时间性能。结果对有界域上的任何优化问题都具有普遍意义，并将模拟退火方法与连续域上的马尔可夫链蒙特卡罗方法的最新收敛理论联系起来。这项工作受到统计学习理论中已知精度和置信度的有限时间学习概念的启发。
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英文标题：
《Simulated Annealing: Rigorous finite-time guarantees for optimization on
  continuous domains》
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作者：
A. Lecchini-Visintini, J. Lygeros, J. Maciejowski
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.
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PDF链接：
https://arxiv.org/pdf/709.2989