Work

Gradient descent with random initialization: fast global convergence for nonconvex phase retrieval

Year: 2019

Type: article

Source: Mathematical Programming

DOI: 10.1007/s10107-019-01363-6

Authors: Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma

Institutions: Princeton University, Carnegie Mellon University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, Office of Naval Research, National Institute of General Medical Sciences, Division of Electrical, Communications and Cyber Systems, Air Force Office of Scientific Research, Army Research Office

Awards: G1523888516, CAREER: Robust Methods for High-Dimensional Signal Processing under Geometric Constraints, Collaborative Research: Statistical Methods for RNA-seq Based Transcriptomic Analysis of Macrophage Function in Spinal Cord Injury, G2767619165, G3467583470, G4061742820, G4884717353, G556410599, G6098521345, G6671297155, Statistical Methods for Ultrahigh-dimensional Biomedical Data, G7142813775, G7452299184, Robust and Distributed Statistical Learning from Big Data, CIF: Medium: Collaborative Research: Nonconvex Optimization for High-Dimensional Signal Estimation: Theory and Fast Algorithms, G854152385, G8597684915, G8876996369

OpenAlex ID: W2790687101

FWCI: 23.67

Cites: 54

Cited by: 208

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