The Decompositional Approach to Matrix Computation
Computing in Science and Engineering
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
Parallelism of double divide and conquer algorithm for singular value decomposition
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
Implementing a parallel matrix factorization library on the cell broadband engine
Scientific Programming - High Performance Computing with the Cell Broadband Engine
A new algorithm for singular value decomposition and its parallelization
Parallel Computing
Parallel double divide and conquer and its evaluation on a super computer
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Dynamic normal forms and dynamic characteristic polynomial
Theoretical Computer Science
High-performance bidiagonal reduction using tile algorithms on homogeneous multicore architectures
ACM Transactions on Mathematical Software (TOMS)
An improved parallel singular value algorithm and its implementation for multicore hardware
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The authors present a stable and efficient divide-and-conquer algorithm for computing the singular value decomposition (SVD) of a lower bidiagonal matrix. Previous divide-and-conquer algorithms all suffer from a potential loss of orthogonality among the computed singular vectors unless extended precision arithmetic is used. A generalization that computes the SVD of a lower banded matrix is also presented.