In this paper we compare several implementations of Kogbetliantz's algorithm for computing the SVD on sequential as well as on parallel machines. Comparisons are based on timings and on operation counts. The numerical accuracy of the different methods is also analyzed
We propose a systolic architecture for computing a singular value decomposition of an m x n matrix,...
The singular values of a matrix are conventionally computed using either the bidiagonalization algo...
Efficiently updating an SVD-based data representation while keeping accurate track of the data mean ...
We describe a new Jacobi ordering for parallel computation of SVD problems. The ordering uses the hi...
We describe three new Jacobi orderings for parallel computation of SVD problems on tree architecture...
We describe three new Jacobi orderings for parallel computation of SVD problems on tree architecture...
In this paper, we present an efficient algorithm for the certification of numeric singular value dec...
The system of equations that govern kinematically redundant manipulators is commonly solved by #ndin...
Singular value decomposition (SVD) is used in many applications such as real-time signal processing ...
AbstractIt is shown that the cyclic Kogbetliantz algorithm ultimately converges quadratically when n...
The goal of this survey is to give a view of the state-of-the-art of computing the Singular Value De...
The ordinary Singular Value Decomposition (SVD) is widely used in statistical and signal processing...
We present a stream algorithm for the Singular-Value Decomposition (SVD) of an M ×N matrix A. Our al...
AbstractThis paper reports several parallel singular value decomposition (SVD) algorithms on the hyp...
Journal PaperArithmetic issues in the calculation of the Singular Value Decomposition (SVD) are disc...
We propose a systolic architecture for computing a singular value decomposition of an m x n matrix,...
The singular values of a matrix are conventionally computed using either the bidiagonalization algo...
Efficiently updating an SVD-based data representation while keeping accurate track of the data mean ...
We describe a new Jacobi ordering for parallel computation of SVD problems. The ordering uses the hi...
We describe three new Jacobi orderings for parallel computation of SVD problems on tree architecture...
We describe three new Jacobi orderings for parallel computation of SVD problems on tree architecture...
In this paper, we present an efficient algorithm for the certification of numeric singular value dec...
The system of equations that govern kinematically redundant manipulators is commonly solved by #ndin...
Singular value decomposition (SVD) is used in many applications such as real-time signal processing ...
AbstractIt is shown that the cyclic Kogbetliantz algorithm ultimately converges quadratically when n...
The goal of this survey is to give a view of the state-of-the-art of computing the Singular Value De...
The ordinary Singular Value Decomposition (SVD) is widely used in statistical and signal processing...
We present a stream algorithm for the Singular-Value Decomposition (SVD) of an M ×N matrix A. Our al...
AbstractThis paper reports several parallel singular value decomposition (SVD) algorithms on the hyp...
Journal PaperArithmetic issues in the calculation of the Singular Value Decomposition (SVD) are disc...
We propose a systolic architecture for computing a singular value decomposition of an m x n matrix,...
The singular values of a matrix are conventionally computed using either the bidiagonalization algo...
Efficiently updating an SVD-based data representation while keeping accurate track of the data mean ...
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