学术报告（6.2）TOH Kim-Chuan
威尼斯人-威尼斯人娱乐场 迎接120周年校庆学术报告
报告题目：Exploiting Solution Sparsity in Big Data Optimization
报告人：TOH Kim-Chuan教授（新加坡国立大学）
时间：2019年6月2日（星期日）10:00—11:00
地点：苏州大学本部精正楼（数学楼）307
摘要：In this talk, we shall demonstrate how solution sparsity in important optimization problems such as sparse optimization models in machine learning, semidefinite programming, and many others can be exploited to design highly efficient algorithms.
The solution sparsity property appears naturally when one applies a semismooth Newton (SSN) method to solve the subproblems in an augmented Lagrangian method (ALM) designed for certain classes of structured convex optimization problems. With in-depth analysis of the underlying generalized Jacobians and sophisticated numerical implementation, one can solve the subproblems at surprisingly low costs. For lasso problems with sparse solutions, the cost of solving a single ALM subproblem by our second order method is comparable or even lower than that in a single iteration of many first order methods.
Consequently, with the fast convergence of the SSN based ALM, we are able to solve many challenging large scale convex optimization problems in big data applications efficiently and robustly. For the purpose of illustration, we present a highly efficient software called SuiteLasso for solving various well-known Lasso-type problems.
This talk is based on joint work with Xudong Li (Fudan U.) and Defeng Sun (PolyU).
报告人简介：
TOH Kim-Chuan（卓金全），新加坡国立大学教务长讲席教授（Provost’s Chair Professor），研究方向：优化、快速算法。个人网页：//www.math.nus.edu.sg/~mattohkc/
欢迎参加！