Stochastic Gradient Descent for Linear Systems with Missing Data

DOI10.4208/nmtma.OA-2018-0066zbMath1438.65095arXiv1702.07098OpenAlexW2963826889WikidataQ128340667 ScholiaQ128340667MaRDI QIDQ5234755

Anna Ma, Deanna Needell

Publication date: 2 October 2019

Published in: Numerical Mathematics: Theory, Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1702.07098

zbMATH Keywords

linear systemsiterative methodsleast squares problemsmissing data

Mathematics Subject Classification ID

Iterative numerical methods for linear systems (65F10) Randomized algorithms (68W20) Numerical linear algebra (65F99)

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Approximate Solutions of Linear Systems at a Universal Rate ⋮ Randomized Kaczmarz method with adaptive stepsizes for inconsistent linear systems ⋮ Randomized Douglas–Rachford Methods for Linear Systems: Improved Accuracy and Efficiency ⋮ The Nesterov accelerated gradient algorithm for auto-regressive exogenous models with random lost measurements: interpolation method and auxiliary model method ⋮ Surrounding the solution of a linear system of equations from all sides ⋮ Greedy Kaczmarz Algorithm Using Optimal Intermediate Projection Technique for Coherent Linear Systems

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