A framework for locally convergent random-search algorithms for discrete optimization via simulation
A framework for locally convergent random-search algorithms for discrete optimization via simulation
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DOI：
10.1145/1276927.1276932
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
L. Hong;Barry L. Nelson
中科院分区：
文献类型：
--
作者：
L. Hong;Barry L. Nelson
本文的目标是提供一个一般的框架，局部收敛的随机搜索算法的随机优化问题时，目标函数是嵌入在随机模拟和决策变量是整数顺序。该框架保证了理想的渐近性质，包括几乎必然收敛和已知的收敛速度，任何算法，符合其温和的条件。在这个框架内，算法设计者可以结合复杂的搜索方案和复杂的统计过程来设计新的算法。
The goal of this article is to provide a general framework for locally convergent random-search algorithms for stochastic optimization problems when the objective function is embedded in a stochastic simulation and the decision variables are integer ordered. The framework guarantees desirable asymptotic properties, including almost-sure convergence and known rate of convergence, for any algorithms that conform to its mild conditions. Within this framework, algorithm designers can incorporate sophisticated search schemes and complicated statistical procedures to design new algorithms.