A framework for locally convergent random-search algorithms for discrete optimization via simulation
本文为决策变量为整数有序的随机优化问题，提供了一个局部收敛随机搜索算法的通用框架，保证算法的几乎必然收敛和已知收敛速度，便于设计新算法。
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.