Research Initiation: Unconstrained Optimization in DiscreteEvent Simulation

在许多真实的世界系统中，它们是如此复杂， 系统参数之间的函数关系 并且其性能还没有被很好地理解。 模拟往往 这是唯一可以用来评估 系统 标准的数学编程技术不能 用于优化系统的性能。 该项目将 开展研究，旨在开发有效的方法， 优化随机系统的性能 连续决策变量 该项目涉及三个 问题：1）在什么条件下，几个新的随机 近似算法收敛，2）当这些条件 满意的模拟，以及3）如何随机逼近 算法比较经验？

In many real world systems, they are of such complexity that the functional relationship between the parameters of the system and its performance is not well understood. Simulation is often the only tool that can be used to evaluate the performance of the system. Standard mathematical programming techniques cannot be used to optimize the performance of the system. This project will conduct research aimed at developing efficient methods for optimizing the performance of stochastic system with respect to continuous decision variables. The project is concerned with three issues: 1) under what conditions do several new stochastic approximation algorithms converge, 2) when are these conditions satisfied in simulation, and 3) how do stochastic approximation algorithms compare empirically?