Application of the substructure-frontal method for repeated solution of large sparse matrix equations to field problems

1988 ◽  
Vol 24 (1) ◽  
pp. 326-329 ◽  
Author(s):  
Z.-Q. You ◽  
Z.-W. Jiang ◽  
Y.-S. Sun ◽  
L. Udpa
Keyword(s):  
Sparse Matrix ◽  
Matrix Equations ◽  
Frontal Method

Numerical Solution of Large Scale Sparse Matrix Equations in Python

PAMM ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 959-960
Author(s):  
Björn Baran ◽  
Martin Köhler ◽  
Nitin Prasad ◽  
Jens Saak
Keyword(s):  
Numerical Solution ◽  
Large Scale ◽  
Sparse Matrix ◽  
Matrix Equations

scholarly journals An Experimental Analysis of Three Pseudo-peripheral Vertex Finders in conjunction with the Reverse Cuthill-McKee Method for Bandwidth Reduction

2019 ◽  
Vol 20 (3) ◽  
pp. 497
Author(s):  
Sanderson L. Gonzaga de Oliveira ◽  
Alexandre A. A. M. Abreu
Keyword(s):  
Experimental Analysis ◽  
Sparse Matrix ◽  
Matrix Equations ◽  
Application Area ◽  
Suitable Alternative ◽  
Bandwidth Reduction ◽  
Theoretical Approaches ◽  
Modified Algorithm

The need to determine pseudoperipheral vertices arises from several graph-theoretical approaches for ordering sparse matrix equations. Results of two algorithms for finding such vertices, namely, the George-Liu and Kaveh-Bondarabady algorithms, are evaluated in this work along with a variant of the Kaveh-Bondarabady algorithm. Experiments among these three algorithms in conjunction with the Reverse Cuthill-McKee method suggest that the modified algorithm is a suitable alternative for reducing bandwidth of matrices that arise from specific application area, but it is dominated by the well-know George-Liu algorithm mainly when considering the computational times of the algorithms.

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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