Results 271 to 280 of about 53,148 (301)

Estimation of Sparse Jacobian Matrices

SIAM Journal on Algebraic Discrete Methods, 1983
When finding a numerical solution to a system of nonlinear equations, one often estimates the Jacobian \(J\) by finite differences. \textit{A. R. Curtis}, \textit{M. J. D. Powell} and \textit{J. K. Reid} [J. Inst. Math. Appl. 13, 117--119 (1974; Zbl 0273.65036)] presented an algorithm that reduces the number of function evaluations by taking advantage ...
Newsam, Garry N., Ramsdell, John D.
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On the Estimation of Sparse Hessian Matrices

SIAM Journal on Numerical Analysis, 1979
This paper studies automatic procedures for estimating second derivatives of a real valued function of several variables. The estimates are obtained from differences in first derivative vectors, and it is supposed that the required matrix is sparse and that its sparsity structure is known.
Powell, M. J. D., Toint, Ph. L.
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On the Estimation of Sparse Jacobian Matrices

IMA Journal of Applied Mathematics, 1974
Summary: We show how to use known constant elements in a Jacobian matrix to reduce the work required to estimate the remaining elements by finite differences.
Curtis, A. R., Powell, M. J. D., Reid, J. K.   +2 more
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Dense Graphs and Sparse Matrices

Journal of Chemical Information and Computer Sciences, 1997
We consider rigorous definitions for dense graphs and sparse matrices, thus quantifying these concepts that have been hitherto used in a qualitative manner.
Milan Randic, Luz M. DeAlba
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A Survey of Indexing Techniques for Sparse Matrices

ACM Computing Surveys, 1973
Indexing schemes of main interest are the bit map, address map, row-column, and the threaded list Major variations of the indexing techniques above mentioned are noted, as well as the particular indexing scheme inherent in diagonal or band matrices.
Udo W. Pooch, Al Nieder
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Rank Detection Methods for Sparse Matrices

SIAM Journal on Matrix Analysis and Applications, 1992
The authors propose an accurate method for detecting the numerical rank of a sparse matrix, using orthogonal factorization along with a one-norm incremental condition estimator. The method uses only static data structures and is implemented with an overhead of \(O(n_ U\log n)\) operations where \(n_ U\) is the number of nonzeros in the upper triangular
Jesse L. Barlow, Udaya B. Vemulapati
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A Block Projection Method for Sparse Matrices

SIAM Journal on Scientific and Statistical Computing, 1992
For the solution of systems of linear algebraic equations with sparse matrices the block Cimmino method is employed. Let the system be written as \[ \begin{pmatrix} A^ 1\\A^ 2\\\vdots\\ A^ p\end{pmatrix} x=\begin{pmatrix} b^ 1\\ b^ 2\\ \vdots \\b^ p\end{pmatrix} \] where the \(A^ i\) are matrices and the \(b^ i\) vectors.
Mario Arioli, Iain S. Duff, Joseph Noailles, Daniel Ruiz 0002   +3 more
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Robust sparse recovery with sparse Bernoulli matrices via expanders

Applied and Computational Harmonic Analysis
Sparse binary matrices are of great interest in the field of sparse recovery, nonnegative compressed sensing, statistics in networks, and theoretical computer science. This class of matrices makes it possible to perform signal recovery with lower storage
Abdalla, Pedro
exaly   +1 more source

Sparse matrices

2021
Alexandre Ern, Jean-Luc Guermond
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On optimizing multiplications of sparse matrices

1996
We consider the problem of predicting the nonzero structure of a product of two or more matrices. Prior knowledge of the nonzero structure can be applied to optimize memory allocation and to determine the optimal multiplication order for a chain product of sparse matrices.
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