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An online condition number query system

Published: 28 March 2008 Publication History

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.

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  • (2009)A novel method for MicroRNA secondary structure prediction using a bottom-up algorithmProceedings of the 47th annual ACM Southeast Conference10.1145/1566445.1566508(1-6)Online publication date: 19-Mar-2009

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  1. An online condition number query system

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    cover image ACM Other conferences
    ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference
    March 2008
    548 pages
    ISBN:9781605581057
    DOI:10.1145/1593105
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    Publication History

    Published: 28 March 2008

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    Author Tags

    1. ACM proceedings
    2. condition number
    3. data mining
    4. features of matrices

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    ACM SE08
    ACM SE08: ACM Southeast Regional Conference
    March 28 - 29, 2008
    Alabama, Auburn

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    • (2009)A novel method for MicroRNA secondary structure prediction using a bottom-up algorithmProceedings of the 47th annual ACM Southeast Conference10.1145/1566445.1566508(1-6)Online publication date: 19-Mar-2009

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