A one-sided Jacobi algorithm for computing the singular value decomposition on avector computer
SIAM Journal on Scientific and Statistical Computing
A Proof of Convergence for Two Parallel Jacobi SVD Algorithms
IEEE Transactions on Computers
A note on one-sided Jacobi algorithm
Numerische Mathematik
Jacobi's method is more accurage than QR
SIAM Journal on Matrix Analysis and Applications
Accurate Symmetric Indefinite Linear Equation Solvers
SIAM Journal on Matrix Analysis and Applications
Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
New Fast and Accurate Jacobi SVD Algorithm. II
SIAM Journal on Matrix Analysis and Applications
Implicit standard Jacobi gives high relative accuracy
Numerische Mathematik
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We describe two main classes of one-sided trigonometric and hyperbolic Jacobi-type algorithms for computing eigenvalues and eigenvectors of Hermitian matrices. These types of algorithms exhibit significant advantages over many other eigenvalue algorithms. If the matrices permit, both types of algorithms compute the eigenvalues and eigenvectors with high relative accuracy. We present novel parallelization techniques for both trigonometric and hyperbolic classes of algorithms, as well as some new ideas on how pivoting in each cycle of the algorithm can improve the speed of the parallel one-sided algorithms. These parallelization approaches are applicable to both distributed-memory and shared-memory machines. The numerical testing performed indicates that the hyperbolic algorithms may be superior to the trigonometric ones, although, in theory, the latter seem more natural.