题  目：Landscape analysis of non-convex optimizations in phase retrieval

内容简介：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：蔡剑锋

报告人简介：香港科技大学数学系教授，主要研究兴趣为信号，图像和数据的理论和算法基础。他在矩阵恢复，图像重构和成像算法等领域，取得了一系列开创性的科研成果。其关于矩阵补全的SVT算法对学术研究和实际应用产生重要影响，该文章谷歌被引次数超6000次。蔡剑锋教授关于图像重构的工作发表于被誉为数学四大期刊之一的Journal of the AMS。蔡剑锋教授在2017年和2018年被评为全球高被引学者，学术文章总被引超13000次。

时  间：2024年1月3日（周三）上午8：00 开始

地  点：南海楼124室

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2023年12月29日