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An exploration of matrix equilibration Liu, Paul
Abstract
We review three algorithms that scale the infinity-norm of each row and column in a matrix to 1. The first algorithm applies to unsymmetric matrices, and uses techniques from graph theory to scale the matrix. The second algorithm applies to symmetric matrices, and is notable for being asymptotically optimal in terms of operation counts. The third is an iterative algorithm that works for both symmetric and unsymmetric matrices. Experimental evidence of the effectiveness of all three algorithms in improving the conditioning of the matrix is demonstrated.
Item Metadata
Title |
An exploration of matrix equilibration
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Creator | |
Date Issued |
2015-09
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Description |
We review three algorithms that scale the infinity-norm of each row and column in a matrix to 1. The first algorithm applies to unsymmetric matrices, and uses techniques from graph theory to scale the matrix. The second algorithm applies to symmetric matrices, and is notable
for being asymptotically optimal in terms of operation counts. The third is an iterative algorithm that works for both symmetric and unsymmetric matrices. Experimental evidence of the effectiveness of
all three algorithms in improving the conditioning of the matrix is demonstrated.
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Language |
eng
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Series | |
Date Available |
2015-10-24
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution 2.5 Canada
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DOI |
10.14288/1.0103601
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URI | |
Affiliation | |
Campus | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Graduate
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DSpace
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Attribution 2.5 Canada