A parallel approach for determining confidence intervals of variable statistics in large and sparse linear equations with RHS ranges

Abstract

This study proposes an algorithm capable of working in parallel for solving variable statistics with large and sparse linear equations under given right hand side ranges. A comparative study to the direct linear programming method is conducted under a main central processor and up to four parallel processors. The studied results are reported computationally and discussed. Moreover, the approach can be adapted for the system under domain decompositions structure leading to a better efficiency experimentally in a case example.

Keywords:

• Large and sparse systems
• statistical confidence intervals
• covariance matrix
• parallel computing

2000 AMS Subject Classification :

• 15A06
• 90C05
• 90C06
• 65F50
• 65F05

Acknowledgements

The author would like to express his sincere appreciations to all reviewers. The final version of this manuscript has been drastically improved by their critical comments and recommendations.