Mathematical Theory and Applications ›› 2022, Vol. 42 ›› Issue (3): 71-.doi: 10.3969/j.issn.1006-8074.2022.03.006
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Zhou Hongmin,Luo Xianbing*, Ye Changlun
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Abstract: In this paper, a gradient projection optimization method is applied to solve a class of stochastic optimal control problems. The Monte Carlo method is a common method to deal with stochastic optimal control problems, but it has a notoriously slow convergence rate. We choose the Quasi-Monte Carlo method with faster convergence. In order to make the random sampling dimensions and time discrete points independent, we use the Karhunen-Lo${\grave{\rm e}}$ve truncation for the Brown motion. Sobol sequences of the Quasi-Monte Carlo method are used for sampling. The error of numerical approximation is presented, and the effectiveness of the method is verified by numerical experiments.
Key words: Stochastic differential equation, Optimal control , $Karhunen-Lo\grave{e}ve$ expansion, Quasi-Monte Carlo method
Zhou Hongmin, Luo Xianbing, Ye Changlun. Quasi-Monte Carlo Method for a Class of Stochastic Optimal Control Problems[J]. Mathematical Theory and Applications, 2022, 42(3): 71-.
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URL: http://mta.csu.edu.cn/EN/10.3969/j.issn.1006-8074.2022.03.006
http://mta.csu.edu.cn/EN/Y2022/V42/I3/71