Efficient Algorithms and Acceleration Techniques for Solving Convex Clustering Problems
报告题目：Efficient Algorithms and Acceleration Techniques for Solving Convex Clustering Problems
报告专家：Kim-Chuan Toh（National University of Singapore）
报告时间：2022年12月1日11:00-12:00
报告地点：腾讯会议：515-452-254，密码：610065
报告摘要：We develop a semismooth Newton based augmented Lagrangian (SSNAL) algorithm for solving large-scale convex clustering problems. Extensive numerical experiments on both simulated and real data demonstrate that our algorithm is highly efficient and robust for solving large-scale problems. We also introduce an adaptive sieving technique to reduce the dimension of the problems we have to solve. As a result, we can accelerate our SSNAL algorithm by more than 7 times and the ADMM algorithm by more than 14 times.
This is based on joint work with:
Yancheng Yuan, Hong Kong Polytechnic University,
Defeng Sun, Hong Kong Polytechnic University,
Tsung-Hui Chang, Chinese University of Hong Kong – Shenzhen
专家简介：Dr Toh is a Professor in the Department of Mathematics at the National University of Singapore (NUS). He obtained his BSc degree from NUS and PhD degree from Cornell University. He works extensively on convex programming, particularly large-scale matrix optimization problems such as semidefinite programming, and structured convex problems arising from machine learning and data science.
He is currently serving as an Area Editor for Mathematical Programming Computation, a co-Editor for Mathematical Programming, an Associate Editor for SIAM Journal on Optimization, and ACM Transactions on Mathematical Software. He received the 2017 Farkas Prize and the 2018 triennial Beale-Orchard Hays Prize. He is a Fellow of the Society for Industrial and Applied Mathematics, and a Fellow of the Singapore National Academy of Science.
邀请人：宋恩彬
![[lecture]Kim-Chuan Toh-01.png](http://tianyuan.scu.edu.cn/upload/default/20221201/%5Blecture%5DKim-Chuan%20Toh-01.png "[lecture]Kim-Chuan Toh-01.png")