【上海财经大学】Efficient first order methods for convex and nonconvex functional constrained optimization

报告题目：Efficient first order methods for convex and nonconvex functional constrained optimization

报告人: 邓琪 副教授（上海财经大学）

报告时间: 2022年11月1日上午10:00-11:00

报告地点: 腾讯会议（线上）

会议ID：297-863-741     会议密码：202210

报告校内联系人：肖现涛   联系电话：84708351-8307

报告摘要: Functional constrained optimization is becoming more and more important in machine learning and operations research. Such problems have potential applications in risk-averse machine learning, semi-supervised learning and robust optimization among others. In this talk, we will present some new first order algorithms for solving convex or nonconvex functional constrained problems. First, we present a primal-dual method (ConEx) which utilizes linear approximations of the constraint functions to define the extrapolation (or acceleration) step. We show that this method is a unified algorithm that achieves some of the best-known rate of convergence for solving different functional constrained convex composite problems. We will discuss how to extend our algorithm to the nonconvex setting in a proximal point framework. Second, we will discuss how to further improve the optimization performance by exploiting the problem structure. In particular, we develop new adaptive and accelerated primal-dual methods which obtain even better convergence rates on problems with strongly convex constraint functions. We illustrate the advantage of these new algorithms with an application in sparsity-inducing optimization.

报告人简介: 邓琪，现为上海财经大学信息管理与工程学院常任副教授，博士生导师。本科毕业于上海交通大学计算机系，博士毕业于美国佛罗里达大学计算机信息科学与工程系，主要研究兴趣为连续优化算法及其在机器学习和中的应用。其研究成果发表在数学优化优化、运营管理和机器学习等领域的期刊和会议上，如Mathematical Programming, Production and Operations Management, NeurIPS 等。