scientific article; zbMATH DE number 7415093
From MaRDI portal
Publication:5159422
Tian Tong, Yuejie Chi, Cong Ma
Publication date: 27 October 2021
Full work available at URL: https://arxiv.org/abs/2005.08898
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
matrix completionrobust PCAlow-rank matrix factorizationmatrix sensinggeneral lossesill-conditioned matrix recoveryscaled gradient descent
Related Items
GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery ⋮ Fast gradient method for low-rank matrix estimation ⋮ A block-randomized stochastic method with importance sampling for CP tensor decomposition ⋮ Provable sample-efficient sparse phase retrieval initialized by truncated power method
Uses Software
Cites Work
- Low rank matrix completion by alternating steepest descent methods
- A geometric analysis of phase retrieval
- The landscape of empirical risk for nonconvex losses
- Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution
- Matrix recipes for hard thresholding methods
- Rapid, robust, and reliable blind deconvolution via nonconvex optimization
- The local convexity of solving systems of quadratic equations
- Cubic regularization of Newton method and its global performance
- Exact matrix completion via convex optimization
- Riemannian Preconditioning
- Guarantees of Riemannian Optimization for Low Rank Matrix Recovery
- Guaranteed Matrix Completion via Non-Convex Factorization
- Incoherence-Optimal Matrix Completion
- Phase Retrieval via Wirtinger Flow: Theory and Algorithms
- Robust Spectral Compressed Sensing via Structured Matrix Completion
- Robust principal component analysis?
- Rank-Sparsity Incoherence for Matrix Decomposition
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Global Optimality in Low-Rank Matrix Optimization
- Non-convex Optimization for Machine Learning
- Spectral Compressed Sensing via Projected Gradient Descent
- Nonconvex Matrix Factorization From Rank-One Measurements
- Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
- Low-Rank Matrix Recovery With Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
- Nonconvex Rectangular Matrix Completion via Gradient Descent Without ℓ₂,∞ Regularization
- Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
- Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA
- Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably
- Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Low-rank matrix completion using alternating minimization
- The nonsmooth landscape of phase retrieval
This page was built for publication:
