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Date: Tue, 27 Oct 2009 03:18:14 +0000 (UTC)
From: "Bruce A Johnson (JIRA)" <jira@apache.org>
To: issues@commons.apache.org
Subject: [jira] Issue Comment Edited: (MATH-297) Eigenvector computation
 incorrectly returning vectors of NaNs
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    [ https://issues.apache.org/jira/browse/MATH-297?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12770367#action_12770367 ] 

Bruce A Johnson edited comment on MATH-297 at 10/27/09 3:16 AM:
----------------------------------------------------------------

There still seems to be a problem with the decomposition of some matrices.  For example, the decomposition of the identity matrix '{{1,0},{0,1}}' yields the correct eigenvalues, but NaN for all the eigenvector elements.
Crucially, the "isIncludedColumn" in the EigenDecompositionImplTest.java file always returns true (at least on my system) when the calculated eigenvectors have NaN elements, so is useless as a test for this problem.

Also, I discovered this problem when getting NaN values doing an SVD of certain matrices (where each row has only one non-zero value).  Since the SVD algorithm uses the EigenDecompositionImpl code, this seems to be a result of this current  bug.  (And ironically, I just told my students that one reason people love the SVD is that it essentially never fails).


      was (Author: bjohnson):
    There still seems to be a problem with the decomposition of some matrices.  For example, the decomposition of the identity matrix "{{1,0},{0,1}}" yields the correct eigenvalues, but NaN for all the eigenvector elements.
Crucially, the "isIncludedColumn" in the EigenDecompositionImplTest.java file always returns true (at least on my system) when the calculated eigenvectors have NaN elements, so is useless as a test for this problem.

Also, I discovered this problem when getting NaN values doing an SVD of certain matrices (where each row has only one non-zero value).  Since the SVD algorithm uses the EigenDecompositionImpl code, this seems to be a result of this current  bug.  (And ironically, I just told my students that one reason people love the SVD is that it essentially never fails).


> Eigenvector computation incorrectly returning vectors of NaNs
> -------------------------------------------------------------
>
>                 Key: MATH-297
>                 URL: https://issues.apache.org/jira/browse/MATH-297
>             Project: Commons Math
>          Issue Type: Bug
>    Affects Versions: 2.0
>            Reporter: Phil Steitz
>             Fix For: 2.1
>
>
> As reported by Axel Kramer on commons-dev, the following test case succeeds, but should fail:
> {code}
> public void testEigenDecomposition() {
>     double[][] m = { { 0.0, 1.0, -1.0 }, { 1.0, 1.0, 0.0 }, { -1.0,0.0, 1.0 } };
>     RealMatrix rm = new Array2DRowRealMatrix(m);
>     assertEquals(rm.toString(),
>         "Array2DRowRealMatrix{{0.0,1.0,-1.0},{1.0,1.0,0.0},{-1.0,0.0,1.0}}");
>     EigenDecompositionImpl ed = new EigenDecompositionImpl(rm,
>         MathUtils.SAFE_MIN);
>     RealVector rv0 = ed.getEigenvector(0);
>     assertEquals(rv0.toString(), "{(NaN); (NaN); (NaN)}");
>   }
> {code}
> ed.getRealEigenvalues() returns the correct eigenvalues (2, 1, -1), but all three eigenvectors contain only NaNs.

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