江蘇省應用數學（中國礦業大學）中心系列學術報告
報告題目：Discrete-time approximation of stochastic optimal control with partial observation
報告人：李運章（複旦大學）
報告時間：2024年5月30日（周四）下午15:00-16:00
報告地點：伟德bvA321
主持人：孫永征
報告摘要：We consider a class of stochastic optimal control problems with partial observation, and study their approximation by discrete-time control problems. We establish a convergence result by using the weak convergence technique of Kushner and Dupuis [Numerical Methods for Stochastic Control Problems in Continuous Time, Springer, New York], together with the notion of relaxed 26 (1988), pp. 1025-1061]. In particular, with a well chosen discrete-time control system, we obtain a first implementable numerical algorithm (with convergence) for the partially observed control problem. Moreover, our discrete-time approximation result would open the door to study convergence of more general numerical approximation methods, such as machine learning based methods. Finally, we illustrate our convergence result by numerical experiments on a partially observed control problem in a linear quadratic setting.
報告人簡介：李運章，複旦大學智能複雜體系實驗室青年副研究員。2020年博士畢業于複旦大學數學科學學院。2020年至2022年在複旦大學從事博士後研究工作，期間被聘為香港中文大學名譽博士後。主要研究領域為随機系統的最優控制問題的高階精度數值算法，相關成果發表于SIAM J. Control. Optim., SIAM J. Sci. Comput., SIAM J. Financial Math., ESAIM: M2AN等知名學術期刊。入選上海市晨光計劃，國家博士後創新人才支持計劃，上海市“超級博士後”激勵計劃。主持國家自然科學基金委青年科學基金項目，上海市“科技創新行動計劃”基礎研究領域項目，中國博士後科學基金面上項目，獲得複旦大學新工科人才基金資助。