A Jacobi–Davidson type method for the generalized singular value problem
ME Hochstenbach - Linear Algebra and its Applications, 2009 - Elsevier
Linear Algebra and its Applications, 2009•Elsevier
We discuss a new method for the iterative computation of some of the generalized singular
values and vectors of a large sparse matrix. Our starting point is the augmented matrix
formulation of the GSVD. The subspace expansion is performed by (approximately) solving
a Jacobi–Davidson type correction equation, while we give several alternatives for the
subspace extraction. Numerical experiments illustrate the performance of the method.
values and vectors of a large sparse matrix. Our starting point is the augmented matrix
formulation of the GSVD. The subspace expansion is performed by (approximately) solving
a Jacobi–Davidson type correction equation, while we give several alternatives for the
subspace extraction. Numerical experiments illustrate the performance of the method.
We discuss a new method for the iterative computation of some of the generalized singular values and vectors of a large sparse matrix. Our starting point is the augmented matrix formulation of the GSVD. The subspace expansion is performed by (approximately) solving a Jacobi–Davidson type correction equation, while we give several alternatives for the subspace extraction. Numerical experiments illustrate the performance of the method.
Elsevier