Convergence analysis of iterative deep learning algorithms for fully nonlinear BSPDEs in non-Markovian utility maximization
报告题目(Title)：Convergence analysis of iterative deep learning algorithms for fully nonlinear BSPDEs in non-Markovian utility maximization
报告人(Speaker)：马敬堂（西南财经大学）
地点(Place)：后主楼1124
时间(Time)：2026年10月26日（周一）10:00-11:00
邀请人(Inviter)：陈华杰
报告摘要
In utility maximization with non-Markovian setting, the value function satisfies a stochastic Hamilton-Jacobi-Bellman (HJB) equation, a fully nonlinear backward stochastic partial differential equation (BSPDE). We propose iterative deep learning algorithms for such BSPDEs and analyze their convergence. We derive the error estimate of the time discretization scheme for BSPDEs, that of the policy iteration scheme for discretized BSPDEs, and that of the iterative deep learning scheme for discretized BSPDEs. We also test the algorithms and show their convergence and accuracy with numerical examples. (This is joint work with Haofei Wu and Harry Zheng.)
主讲人简介
马敬堂，西南财经大学数学学院光华英才特聘教授、博士生导师、院长，教育部新世纪优秀人才，四川省学术与技术带头人。现任四川省数学会副理事长，中国运筹学会金融工程与金融风险管理分会副理事长。主要研究方向为：金融数学、随机控制计算。在SIAM Journal on Control and Optimization, Mathematics of Operations Research, European Journal of Operational Research, Insurance: Mathematics and Economics等期刊发表论文。
