Affiliation:
1. Georgia Institute of Technology
Abstract
Efficient methods for solving linear-programming problems in exact precision rely on the solution of sparse systems of linear equations over the rational numbers. We consider a test set of instances arising from exact-precision linear programming and use this test set to compare the performance of several techniques designed for symbolic sparse linear-system solving. We compare a direct exact solver based on LU factorization, Wiedemann’s method for black-box linear algebra, Dixon’s
p
-adic-lifting algorithm, and the use of iterative numerical methods and rational reconstruction as developed by Wan.
Funder
Division of Civil, Mechanical and Manufacturing Innovation
Office of Naval Research
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Cited by
9 articles.
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