一、题目

Consensus-based High Dimensional Global Non-convex Optimization in Machine Learning

二、主讲人

金石

三、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 parameters, 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.

四、主讲人介绍

金石，上海交大自然科学研究院院长、讲席教授。上海应用数学中心主任，上海交大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，入选国家级人才计划。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。

五、邀请人

芮洪兴 数学学院教授

六、时间

11月11日（周四）10:00

七、地点

腾讯会议，ID：940 638 639，密码：211111

八、主办方

山东大学数学学院