NDLI: An online condition number query system

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An online condition number query system

Content Provider	ACM Digital Library
Author	Han, Dianwei Xu, Shuting Zhang, Jun
Abstract	Condition number of a matrix is an important measure in numerical analysis and linear algebra. It is a measure of stability or sensitivity of a matrix to numerical operations. However, the direct computation of the condition number of a matrix is very expensive in terms of CPU and memory cost, and becomes prohibitive for large size matrices. We propose to use data mining techniques to estimate the condition number of a given sparse matrix. In particular, we will use Support Vector Machine (SVM) to predict the condition numbers. That is, after computing the sparsity pattern features of a matrix, we use support vector regression (SVR) to predict its condition number. This Online Condition Number Query System (OCNQS) allows the users to submit their matrices and to obtain predicted condition numbers for their matrices. The accuracy of our prediction methods may not be as precise as the direct computation methods, but it is much faster. Our online system accepts matrices in Harwell-Boeing (HB) format and in standard MATLAB format. The users can use our system to estimate the condition number of their matrices through LAPACK software as well.
Starting Page	264
Ending Page	267
Page Count	4
File Format	PDF
ISBN	9781605581057
DOI	10.1145/1593105.1593175
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-03-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Acm proceedings Condition number Features of matrices Data mining
Content Type	Text
Resource Type	Article

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