题目：Primal-dual block coordinate update methods for multiblock structured affinely constrained problems
主讲人：伦斯勒理工学院 徐扬扬 助理教授
日期：2019年5月10日（星期五）
时间：上午10:00- 11:30
地点：数据科学与jbo竞博电竞官方网站 A201
主持：凌青 教授
摘要：The alternating direction method of multipliers (ADMM) has been popularly used in many areas including imaging, statistics, and machine learning. However, its direct extension to multiple block problems is not guaranteed to converge under merely convexity assumption. This talk will give two variants of multi-block ADMM. One is based on mixing Jacobi and Gauss-Seidel updates, and the other one applies the randomization technique. For the second variant, an asynchronous parallel version will also be presented to handle extremely large-scale problems. Convergence and also numerical results will be shown for all three methods.
个人介绍：Yangyang Xu is now a tenure-track assistant professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute. He received his B.S. in Computational Mathematics from Nanjing University in 2007, M.S. in Operations Research from Chinese Academy of Sciences in 2010, and Ph.D from the Department of Computational and Applied Mathematics at Rice University in 2014. His research interests are optimization theory and methods and their applications such as in machine learning, statistics, and signal processing. He developed optimization algorithms for compressed sensing, matrix completion, and tensor factorization and learning. Recently, his research focuses on first-order methods, operator splitting, stochastic optimization methods, and high performance parallel computing. He has published over 30 papers in prestigious journals and conference proceedings. His work on block coordinate descent method for multi convex optimization has won the gold medal award in 2017 International Consortium of Chinese Mathematicians.