学术报告

报告题目：Landscape analysis of non-convex optimizations in phase retrieval

报告人：蔡剑锋（香港科技大学）

报告时间：2022年3月28日（星期一）15:00-17:00

腾讯会议：350-897-563

参加人员：教师、研究生、本科生

报告摘要：Non-convex optimization is a ubiquitous tool in scientific and engineering research. For many important problems, simple non-convex optimization algorithms often provide good solutions efficiently and effectively, despite possible local minima. One way to explain the success of these algorithms is through the global landscape analysis. In this talk, we present some results along with this direction for phase retrieval. The main results are, for several of non-convex optimizations in phase retrieval, a local minimum is also global and all other critical points have a negative directional curvature. The results not only will explain why simple non-convex algorithms usually find a global minimizer for phase retrieval, but also will be useful for developing new efficient algorithms with a theoretical guarantee by applying algorithms that are guaranteed to find a local minimum.

报告人简介：蔡剑锋，香港科技大学数学系教授。2000年获复旦大学学士学位，2007年获香港中文大学博士学位。曾先后在新加坡国立大学，美国洛杉矶加州大学，和美国爱荷华大学工作。2015年加入香港科技大学数学系。研究兴趣是数据科学和成像技术中的算法设计和分析。在2017年和2018年被评选为全球高被引学者。