发布时间: 2017-07-04 16:49:20
题 目：Randomized block coordinate descent methods for a class of structured nonlinear optimization
报 告 人：吕召松 教授 Simon Fraser University
摘 要：Nowadays the optimization problems emerging from some application areas such as machine learning and data mining are typically huge-scale. They have brought tremendous challenge to the traditional first- and second-order methods. In this talk we consider randomized block coordinate descent (RBCD) type of methods for solving these problems, whose iteration cost is typically low. We analyze iteration complexity of some RBCD methods and present some computational results.
时 间：2017-06-28 10:30--11:30
报告人简介: 吕召松教授在加拿大Simon Fraser University的数学系工作，是国际著名的最优化专家，分别是国际著名学术杂志SIAM Journal on Optimization和Big Data and Information Analytics的编辑。