报告题目：Some new optimization theory for convergence analysis of first-order algorithms
报告时间：2018年11月9日（周五）下午15:30点-16:30点
报告地点：创新园大厦A1101
报告人：张进 香港浸会大学
校内报告联系人：刘永朝 联系电话：84708351-8141
报告摘要：
We present some new theoretical results for the topics of optimality conditions, constraint qualifications and error bounds in optimization, and show how these theoretical results can be used for analyzing the convergence of some very popular first-order algorithms which have been finding wide applications in data science domains. Some fundamental models such as the LASSO and grouped LASSO are studied, and it is shown that the linear convergence can be obtained if some algorithms are implemented for these models such as the proximal gradient method, the proximal alternating linearized minimization algorithm and the randomized block coordinate proximal gradient method. We provide a novel analytic framework based on variational analysis techniques (e.g., error bound, calmness, metric subregularity) for the convergence analysis of first-order algorithms. By this new analytic framework, we significantly improve some convergence rate results in the literature and obtain some new results.
报告人简介：张进，1986年3月出生，香港浸会大学研究助理教授。2007年于大连理工大学人文社会科学学院获文学学士，2010年于大连理工大学数学科学学院获得理学硕士学位，2014年12月于加拿大维多利亚大学数学与统计系获得应用数学博士学位。2015年7月进入香港浸会大学数学系工作。张进主要从事最优化理论方面，特别是最优性条件和约束规格方面的研究，发表SCI检索论文10余篇，其中多篇论文发表在 Mathematical Programming，SIAM Journal on Optimization等优化领域顶级期刊上。
