ResearchTrend.AI
Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form

Srinadh Bhojanapalli
Nicolas Boumal
Prateek Jain
Praneeth Netrapalli
ArXiv (abs)PDFHTML

Papers citing "Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form"

10 / 10 papers shown
Title
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
Yuetian Luo
Xudong Li
Anru R. Zhang
91
11
0
03 Aug 2021
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
Junhyung Lyle Kim
Jose Antonio Lara Benitez
Taha Toghani
Cameron R. Wolfe
Zhiwei Zhang
Anastasios Kyrillidis
76
0
0
16 Jun 2021
Lecture notes on non-convex algorithms for low-rank matrix recovery
Lecture notes on non-convex algorithms for low-rank matrix recovery
Irène Waldspurger
64
1
0
21 May 2021
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
T. Roddenberry
Santiago Segarra
Anastasios Kyrillidis
58
0
0
17 Dec 2020
Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis
Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis
Courtney Paquette
B. V. Merrienboer
Elliot Paquette
Fabian Pedregosa
101
27
0
08 Jun 2020
Polynomial time guarantees for the Burer-Monteiro method
Polynomial time guarantees for the Burer-Monteiro method
Diego Cifuentes
Ankur Moitra
162
37
0
03 Dec 2019
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Yuejie Chi
Yue M. Lu
Yuxin Chen
75
427
0
25 Sep 2018
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
Pursuit of Low-Rank Models of Time-Varying Matrices Robust to Sparse and Measurement Noise
A. Akhriev
Jakub Mareˇcek
Andrea Simonetto
69
8
0
10 Sep 2018
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Smoothed analysis of the low-rank approach for smooth semidefinite programs
Thomas Pumir
Samy Jelassi
Nicolas Boumal
128
27
0
11 Jun 2018
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
S. Du
Jason D. Lee
204
272
0
03 Mar 2018

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.