报告题目;Consensus-based High Dimensional Global Non-convex Optimization in Machine Learning
报告专家：金石教授 (上海交通大学)
邀请人;李秋齐
时间：2022 年 6 月 2 日 (星期四) 10:30-11:30 AM
报告形式：在线报告 ( 腾讯会议）
腾讯会议号：884 127 284
入会链接：https://meeting.tencent.com/dm/KbQP0x9fk9V0
报告摘要
We introduce a stochastic interacting particle consensus system for global optimization of high dimensional
non-convex functions. This algorithm does not use gradient of the function thus is suitable for non-smooth
functions. We prove，for fully discrete systems, that under dimension-independent conditions on the param-
eters, with suitable initial data, the algorithms converge to the neighborhood of the global minimum almost
surely. We also introduce an Adaptive Moment Estimation (ADAM) based version to significantly improve
its performance in high-space dimension.
专家简介
金石教授，上海交通大学自然科学研究院院长、讲席教授。上海应用数学中心主任，交通大学教育部科学与工程计算重点实
验室主任兼人工智能数学中心主任。曾获冯康科学计算奖，当选 AMS Fellow、SIAM Fellow、欧洲人文和自然科学院外籍院
士、欧洲科学院院士等。
