报告题目 (Title)：Pontryagin’s maximum principle, optimal control problem solver RIOTS_95, machine learning and fractional calculus (庞特里亚金极大值原理，最优控制问题求解器RIOTS_95, 机器学习和分数阶微积分)
报告人 (Speaker)：陈阳泉 教授（University of California, Merced, USA）
报告时间 (Time)：2023年12月1日(周五) 11:00
报告地点 (Place)：腾讯会议(363-278-420)
邀请人(Inviter)：李常品、蔡敏
报告摘要：This seminar is of tutorial nature to inspire research efforts in connecting optimal control (OC) and machine learning (ML) and fractional calculus (FC). First, a tutorial on static and dynamic optimization problems is presented with a uniform Lagrangian multiplier framework. After a simple exposure to calculus of variation (COV) and its transversality conditions, we derive the basic solution based on Hamiltonian and the Pontryagin’s maximum principle (PMP). An analytical simplest example is presented to show the need of a general-purpose toolbox to solve OCPs (optimal control problems). Then RITOS_95, a general OCP solver in the form of a Matlab toolbox is introduced with some solved example OCPs. Live OCP solving runs in Matlab will be shown.