best365网页版登录学术报告

Perturbed Amplitude Flow for Phase Retrieval

高冰

（南开大学best365网页版登录）

报告时间：2021年6月1日星期二 下午3:00-4:00

会议地点：沙河E404（线下）; 腾讯会议ID:127 416 879（线上）

报告摘要： In this talk, we propose a new non-convex algorithm, Perturbed Amplitude Flow (PAF), for solving the phase retrieval problem, i.e., the reconstruction of a signal x∈Hn (H=R or C) from phaseless samples bj = |⟨aj , x⟩|, j = 1, . . . , m. We prove that PAF can recover cx (|c| = 1) under O(n) Gaussian random measurements (optimal order of measurements). Starting with a designed initial point, our PAF algorithm iteratively converges to the true solution at a linear rate for both real, and complex signals. Besides, PAF algorithm needn’t any truncation or re-weighted procedure, so it enjoys simplicity for implementation. The effectiveness, and benefit of the proposed method are validated by the simulation studies.

报告人简介: 高冰博士，2017年毕业于中国科学院数学与系统科学院计算数学专业。2017-2019在香港科技大学从事博士后研究。2019-至今在南开大学数学学院工作。

邀请人： 谢家新