A PVM-based library for sparse matrix factorizations

1998 ◽  
pp. 304-311
Author(s):  
Juan Touriño ◽  
Ramón Doallo
Keyword(s):  
Sparse Matrix ◽  
Matrix Factorizations

scholarly journals Sparse Matrix Factorizations for Fast Linear Solvers with Application to Laplacian Systems

2017 ◽  
Vol 38 (2) ◽  
pp. 505-529 ◽  
Author(s):  
Michael T. Schaub ◽  
Maguy Trefois ◽  
Paul van Dooren ◽  
Jean-Charles Delvenne
Keyword(s):  
Sparse Matrix ◽  
Matrix Factorizations ◽  
Linear Solvers

scholarly journals Sparse matrix factorizations of transfer matrices

1987 ◽  
Vol 28 (4) ◽  
pp. 462-475 ◽  
Author(s):  
Murray T. Batchelor
Keyword(s):  
Ising Model ◽  
Sparse Matrix ◽  
Matrix Factorizations ◽  
Nearest Neighbour ◽  
Transfer Matrices ◽  
Face Model

AbstractSparse matrix factorizations of transfer matrices for the interactions round a face model are reviewed. The sparse factors of a more general Ising model containing first, second and third nearest neighbour interactions are also presented. For both models the factorizations are achieved by considering the required auxiliary spin sets as a hierarchy of interacting spins.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

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