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Copyright © by SIAM. Unauthorized Reproduction of This Article Is Prohibited. PARALLEL BIDIAGONALIZATION of a DENSE MATRIX 827
Minimum-Residual Methods for Sparse Least-Squares Using Golub-Kahan Bidiagonalization a Dissertation Submitted to the Institute
Restarted Lanczos Bidiagonalization for the SVD in Slepc
Cache Efficient Bidiagonalization Using BLAS 2.5 Operators Howell, G. W. and Demmel, J. W. and Fulton, C. T. and Hammarling, S
Weighted Golub-Kahan-Lanczos Algorithms and Applications
Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture
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D2.9 Novel SVD Algorithms
A Golub-Kahan Davidson Method for Accurately Computing a Few Singular Triplets of Large Sparse Matrices
On the Lanczos and Golub–Kahan Reduction Methods Applied to Discrete Ill-Posed Problems
Krylov Subspace Methods for Inverse Problems with Application to Image Restoration Mohamed El Guide
Accuracy and Efficiency of One–Sided Bidiagonalization Algorithm Nela Bosner
Parallel Implementation of Singular Value Decomposition (SVD) in Image Compression Using Open Mp and Sparse Matrix Representation
AMS526: Numerical Analysis I (Numerical Linear Algebra for Computational and Data Sciences) Lecture 19: Computing the SVD; Sparse Linear Systems
DIVIDE and CONQUER LOW-RANK PRECONDITIONING TECHNIQUES ∗ 1. Introduction. Krylov Subspace Methods Preconditioned with a Form O
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LANCZOS and GOLUB-KAHAN REDUCTION METHODS APPLIED to ILL-POSED PROBLEMS a Dissertation Submitted to Kent State University In
The Canonical Correlations of Matrix Pairs and Their Numerical Computation
Top View
The Regularizing Effect of the Golub-Kahan Iterative Bidiagonalization and Revealing the Noise Level in the Data
An Improved Algorithm for Computing the Singular Value Decomposition
STABLE COMPUTATION of the CS DECOMPOSITION: SIMULTANEOUS BIDIAGONALIZATION 1. Introduction. the CS Decomposition Presents Unique
Quaternion Singular Value Decomposition Based on Bidiagonalization to a Real Matrix Using Quaternion Householder Transformations
A Bibliography of Publications of Gene H. Golub
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Augmented Implicitly Restarted Lanczos Bidiagonalization Methods James Baglama University of Rhode Island,
[email protected]
Accelerating the Reduction to Upper Hessenberg, Tridiagonal, and Bidiagonal Forms Through Hybrid GPU-Based Computing
A ROBUST and EFFICIENT PARALLEL SVD SOLVER BASED on RESTARTED LANCZOS BIDIAGONALIZATION∗ 1. Introduction. the Computation of S
The Singular Value Decomposition, Or SVD, Has a Long History with Many Improvements Over the Years, Both in Its Implementations and Algorithmically
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[email protected]
Slepc Users Manual Scalable Library for Eigenvalue Problem Computations
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Bidiagonalization of Matrices and Solution of Linear Equations* C
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A Robust and Efficient Parallel SVD Solver Based on Restarted Lanczos Bidiagonalization