报告题目：A Framework for Analyzing Variance Reduced Stochastic Gradient Methods and a New One for Non-smooth Non-convex Optimization

报告人: 梁经纬 副教授（上海交通大学）

报告时间: 2022年10月13日上午8:30-9:30

报告地点: 腾讯会议（线上）

会议ID：775-893-216       会议密码：2210

报告校内联系人：肖现涛 教授  联系电话：84708351-8307

报告摘要: Over the past years, stochastic optimization methods are becoming increasingly popular in traditional areas including inverse problems and signal/image processing. In this talk, I will introduce SPRING, a novel stochastic version of proximal alternating linearized minimization (PALM) algorithm for solving a class of non-smooth and non-convex optimization problems which arise in many statistical machine learning, computer vision and imaging applications. Theoretically, I will show that our proposed method with variance-reduced stochastic gradient estimators, such as SAGA and SARAH, achieves state-of-the-art oracle complexities. Numerical experiments on sparse non-negative matrix factorization, sparse principal component analysis and blind image deconvolution are also presented to demonstrate the efficiency of our algorithm.

报告人简介: 梁经纬，副教授，上海交通大学自然科学研究院。梁经纬于2013年获得上海交通大学数学硕士学位，之后于2016年获得法国卡昂大学数学博士学位。2017至2020年，梁经纬在英国剑桥大学理论物理与应用数学系从事博士后研究工作，并于2020年底加入伦敦玛丽王后大学数学科学学院任数据科学讲师。2021年7月，正式加入上海交通大学。梁经纬的主要研究兴趣为数学图像处理，非光滑优化和数据科学等。