In this paper we give evidence to show that in one-sided Jacobi SVD computation the sorting of column norms in each sweep is very important. An e cient parallel ring Jacobi ordering for computing singular value decomposition is described. This ordering can generate n(n 1)=2 di erent index pairs and sort column norms at the same time. The one-sided Jacobi SVD algorithm using this parallel orderi...

This paper is the result of contrived efforts to break the barrier between numerical accuracy and run time efficiency in computing the fundamental decomposition of numerical linear algebra – the singular value decomposition (SVD) of a general dense matrix. It is an unfortunate fact that the numerically most accurate one–sided Jacobi SVD algorithm is several times slower than generally less accu...

This paper is the result of contrived efforts to break the barrier between numerical accuracy and run time efficiency in computing the fundamental decomposition of numerical linear algebra – the singular value decomposition (SVD) of a general dense matrix. It is an unfortunate fact that the numerically most accurate one–sided Jacobi SVD algorithm is several times slower than generally less accu...

One sided block Jacobi algorithm for the singular value decomposition (SVD) of matrix can be a method of choice to compute SVD efficiently and accurately in parallel. A given matrix is logically partitioned into block columns and is subjected to an iteration process. In each iteration step, for given two block columns, their Gram matrix is generated, its symmetric eigenvalue decomposition (EVD)...

We discuss a new method for the iterative computation of a portion of the singular values and vectors of a large sparse matrix. Similar to the Jacobi–Davidson method for the eigenvalue problem, we compute in each step a correction by (approximately) solving a correction equation. We give a few variants of this Jacobi–Davidson SVD (JDSVD) method with their theoretical properties. It is shown tha...

Singular value decomposition has been used in signal processing, image processing, principal component analysis, robotics and my other real time applications. These applications demand fast processing of large datasets. SVD needs large amount of computation. In this paper, we present the parallel implementation of Singular Value Decomposition in FGPA. SVD is implemented using two sided Jacobi a...

In latent semantic indexing, the addition of documents (or the addition of terms) to some already processed text collection leads to the updating of the best rank-k approximation of the term-document matrix. The computationally most intensive task in this updating is the computation of the singular value decomposition (SVD) of certain square matrix, which is upper or lower triangular, and conta...

The reduction of parasitic linear subcircuits is one of many issues in model order reduction (MOR) for VLSI design. This issue is well explored, but the structure of these subcircuits has been changing recently. So far, the number of elements in these subcircuits was significantly larger than the number of connections to the whole circuit, the so called pins or terminals. This assumption is no ...