Stochastic Steepest Descent Methods for Linear Systems: Greedy Sampling & Momentum (Record no. 700)

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fixed length control field 250101s2020 xx o 000 0 eng d
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Personal name Md Sarowar Morshed
245 10 - TITLE STATEMENT
Title Stochastic Steepest Descent Methods for Linear Systems: Greedy Sampling & Momentum
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Date of production, publication, distribution, manufacture, or copyright notice 2020
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Content type term text
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Carrier type term online resource
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Summary, etc. Recently proposed adaptive Sketch & Project (SP) methods connect several well-known projection methods such as Randomized Kaczmarz (RK), Randomized Block Kaczmarz (RBK), Motzkin Relaxation (MR), Randomized Coordinate Descent (RCD), Capped Coordinate Descent (CCD), etc. into one framework for solving linear systems. In this work, we first propose a Stochastic Steepest Descent (SSD) framework that connects SP methods with the well-known Steepest Descent (SD) method for solving positive-definite linear system of equations. We then introduce two greedy sampling strategies in the SSD framework that allow us to obtain algorithms such as Sampling Kaczmarz Motzkin (SKM), Sampling Block Kaczmarz (SBK), Sampling Coordinate Descent (SCD), etc. In doing so, we generalize the existing sampling rules into one framework and develop an efficient version of SP methods. Furthermore, we incorporated the Po
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/2012.13087v1">https://arxiv.org/pdf/2012.13087v1</a>
Link text Read the full paper (PDF)
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      Available online General Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000961 09/01/2026 https://arxiv.org/pdf/2012.13087v1 09/01/2026 Research paper — read online