统计力学
模拟退火
连接(主束)
类比
计算机科学
数学优化
最优化问题
统计物理学
极值优化
自由度(物理和化学)
多元统计
复杂系统
理论计算机科学
算法
数学
人工智能
物理
多群优化
机器学习
热力学
哲学
语言学
几何学
作者
Scott Kirkpatrick,C. D. Gelatt,M.P. Vecchi
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:1983-05-13
卷期号:220 (4598): 671-680
被引量:42433
标识
DOI:10.1126/science.220.4598.671
摘要
There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.
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