Accurate singular values of bidiagonal matrices
SIAM Journal on Scientific and Statistical Computing
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A Parallel Algorithm for Computing the Singular Value Decomposition of a Matrix
SIAM Journal on Matrix Analysis and Applications
A Stable and Efficient Algorithm for the Rank-One Modification of the Symmetric Eigenproblem
SIAM Journal on Matrix Analysis and Applications
A Divide-and-Conquer Algorithm for the Bidiagonal SVD
SIAM Journal on Matrix Analysis and Applications
A Divide-and-Conquer Algorithm for the Symmetric TridiagonalEigenproblem
SIAM Journal on Matrix Analysis and Applications
Applied numerical linear algebra
Applied numerical linear algebra
ScaLAPACK user's guide
On Computing an Eigenvector of a Tridiagonal Matrix. Part I: Basic Results
SIAM Journal on Matrix Analysis and Applications
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
SIAM Journal on Scientific Computing
Orthogonal Eigenvectors and Relative Gaps
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Scientific Computing
A new singular value decomposition algorithm suited to parallelization and preliminary results
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
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An algorithm mainly consisting of a part of Divide and Conquer and the twisted factorization is proposed for bidiagonal SVD. The algorithm costs O(n^2)flops and is highly parallelizable when singular values are isolated. If strong clusters exist, the singular vector computation needs reorthgonalization. In such case, the cost of the algorithm increases to O(n^2+nk^2)flops and the parallelism may worsen depending on the distribution of singular values. Here k is the size of the largest cluster. The algorithm needs only O(n) working memory for every type of matrices.