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scientific article; zbMATH DE number 7415093

Language	Label	Description	Also known as
English	No label defined	scientific article; zbMATH DE number 7415093

Statements

scholarly article

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publication date

27 October 2021

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full work available at URL

https://arxiv.org/abs/2005.08898

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https://jmlr.csail.mit.edu/papers/v22/20-1067.html

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Mathematics Subject Classification ID

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zbMATH DE Number

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zbMATH Keywords

low-rank matrix factorization

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scaled gradient descent

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ill-conditioned matrix recovery

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matrix sensing

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robust PCA

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matrix completion

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general losses

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Spectral Compressed Sensing via Projected Gradient Descent

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Phase Retrieval via Wirtinger Flow: Theory and Algorithms

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Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements

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Exact matrix completion via convex optimization

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Robust principal component analysis?

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Rank-Sparsity Incoherence for Matrix Decomposition

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Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA

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Nonconvex Rectangular Matrix Completion via Gradient Descent Without <i>ℓ</i>₂,<sub>∞</sub> Regularization

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Incoherence-Optimal Matrix Completion

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Robust Spectral Compressed Sensing via Structured Matrix Completion

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Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

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Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview

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The nonsmooth landscape of phase retrieval

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Non-convex Optimization for Machine Learning

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Low-rank matrix completion using alternating minimization

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Matrix recipes for hard thresholding methods

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Rapid, robust, and reliable blind deconvolution via nonconvex optimization

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Nonconvex Matrix Factorization From Rank-One Measurements

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Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution

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Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing

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The landscape of empirical risk for nonconvex losses

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Riemannian Preconditioning

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Cubic regularization of Newton method and its global performance

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Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably

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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

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The local convexity of solving systems of quadratic equations

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A geometric analysis of phase retrieval

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Guaranteed Matrix Completion via Non-Convex Factorization

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Low rank matrix completion by alternating steepest descent methods

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Low-Rank Matrix Recovery With Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number

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Guarantees of Riemannian Optimization for Low Rank Matrix Recovery

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Global Optimality in Low-Rank Matrix Optimization

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Sitelinks

Mathematics(1 entry)

mardi Publication:5159422

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