金融工程研究中心学术报告：Deep learning algorithms with iteration policy for the nonlinear BSPDEs

报 告 人：马敬堂 西南财经大学 教授、博士生导师、院长 报告时间：2024.07.30(周二) 10:00-11:00 报告地点：金融工程研究中心105报告厅 报告摘要： In this talk, we will present the recent work on the deep learning algorithms for solving the nonlinear backward stochastic partial differential equations (BSPDEs). In particular we focus on continuous-time optimal investment (utility maximization) under the rough volatility models which are non-Markovian. The optimal value is expressed by a nonlinear BSPDE. The deep learning algorithms with iteration policy are proposed to solve the nonlinear BSPDE and analyzed in regards to the convergence.  (This is joint work with Haofei Wu and Harry Zheng.)   个人简介： 马敬堂，西南财经大学数学学院、教授、博士生导师、院长，教育部新世纪优秀人才。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，East Asian Journal on Applied Mathematics杂志副主编。主要研究方向为：计算数学与金融数学（期权定价模型、最优投资算法、随机控制计算、HJB方程数值解）。在SIAM Journal on Control and Optimization, European Journal of Operational Research, Journal of Computational Physics等期刊发表论文。