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Block Gram-Schmidt algorithms and their stability properties

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Block Gram-Schmidt algorithms serve as essential kernels in many scientific computing applications, but for many commonly used variants, a rigorous treatment of their stability properties remains open. This work provides a comprehensive categorization of block Gram-Schmidt algorithms, particularly those used in Krylov subspace methods to build orthonormal bases one block vector at a time. Known stability results are assembled, and new results are summarized or conjectured for important communication-reducing variants. Additionally, new block versions of low-synchronization variants are derived, and their efficacy and stability are demonstrated for a wide range of challenging examples. Numerical examples are computed with a versatile MATLAB package hosted at https://github .com /katlund /BlockStab, and scripts for reproducing all results in the paper are provided. Block Gram-Schmidt implementations in popular software packages are discussed, along with a number of open problems. An appendix containing all algorithms type-set in a uniform fashion is provided.(c) 2021 Elsevier Inc. All rights reserved.

StabilityLoss of orthogonalityGram-SchmidtBlock Krylov subspace methodsCONJUGATE-GRADIENT ALGORITHMKRYLOV SUBSPACE METHODSERROR ANALYSISQR FACTORIZATIONCHOLESKY QREFFICIENTPARALLELGMRESCONVERGENCEARNOLDI

Carson, Erin、Lund, Kathryn、Rozlonik, Miroslav、Thomas, Stephen

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Charles Univ Prague

Czech Acad Sci

Natl Renewable Energy Lab

2022

Linear Algebra and its Applications

Linear Algebra and its Applications

EISCI
ISSN:0024-3795
年,卷(期):2022.638
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