Results 251 to 260 of about 25,655 (286)
Some of the next articles are maybe not open access.
Computational methods for sparse matrices
Computer Physics Communications, 1980Abstract This paper is a survey of methods currently available for processing sparse matrices in a digital computer; specifically in the solution of linear algebraic equations and the eigenproblem.
openaire +3 more sources
Generalizations of Davidson’s Method for Computing Eigenvalues of Sparse Symmetric Matrices
SIAM Journal on Scientific and Statistical Computing, 1986The method of \textit{E. R. Davidson} [J. Comput. Phys. 17, 87-94 (1975; Zbl 0293.65022)] for computing a few eigenpairs of large sparse symmetric matrices is analyzed as a method for using diagonal preconditioning (i.e. using an approximate inverse).
Morgan, Ronald B., Scott, David S.
openaire +2 more sources
A hybrid method for the solution of sparse power system matrices on vector computers
38th Midwest Symposium on Circuits and Systems. Proceedings, 2002This paper describes a methodology for solving a linear system of equations on vector computer. The methodology combines direct and inverse factors. The decomposition and implementation of the direct solution in a CRAY Y-MPZE/232, and the performance results are discussed.
A. Padilha, A.R. Basso
openaire +1 more source
SIAM Journal on Scientific Computing, 2019
Summary: Obtaining high accuracy singular triplets for large sparse matrices is a significant challenge, especially when searching for the smallest triplets. Due to the difficulty and size of these problems, efficient methods must function iteratively, with preconditioners, and under strict memory constraints. In this research, we present a Golub-Kahan
Steven Goldenberg +2 more
openaire +2 more sources
Summary: Obtaining high accuracy singular triplets for large sparse matrices is a significant challenge, especially when searching for the smallest triplets. Due to the difficulty and size of these problems, efficient methods must function iteratively, with preconditioners, and under strict memory constraints. In this research, we present a Golub-Kahan
Steven Goldenberg +2 more
openaire +2 more sources
On the Method by Rostami for Computing the Real Stability Radius of Large and Sparse Matrices
SIAM Journal on Scientific Computing, 2016Summary: In a recent paper, \textit{M. W. Rostami} [SIAM J. Sci. Comput. 37, No. 5, S447--S471 (2015; Zbl 1325.65067)] has presented an interesting algorithm for the computation of the real pseudospectral abscissa and the real stability radius (aka the distance to instability) of a square matrix \(A \in \mathbb R^{n,n}\) in the spectral norm.
openaire +2 more sources
A New Method for Computing $φ$-functions and Their Condition Numbers of Large Sparse Matrices
201621 pages, 1 ...
Wu, Gang, Zhang, Lu
openaire +1 more source
Ordering Methods for Sparse Matrices and Vector Computers
1988Abstract : Direct factorization methods for solving large sparse linear equations are used as fundamental building blocks for the numerical solution of many scientific and computational problems. It is well known that reordering the variables and equations is crucial in reducing the cost of performing direct solution techniques.
openaire +1 more source
Ordering Methods for Sparse Matrices and Vector Computers.
1986Abstract : This report summarizes the activities at Boeing Computer Service Company from April 15, 1985 until August 15, 1986. Five tasks are defined in our analysis of quotient tree algorithms and frontal methods: analysis of multifrontal methods, creation of symmetric indefinite out - of-core minimal storage elimination schemes, analyses of quotient ...
openaire +1 more source