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List: jakarta-commons-dev
Subject: [jira] Issue Comment Edited: (MATH-320) NaN singular value from SVD
From: "Axel Kramer (JIRA)" <jira () apache ! org>
Date: 2009-12-31 12:03:29
Message-ID: 401005532.1262261009442.JavaMail.jira () brutus ! apache ! org
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[ https://issues.apache.org/jira/browse/MATH-320?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12795591#action_12795591 \
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Axel Kramer edited comment on MATH-320 at 12/31/09 12:01 PM:
-------------------------------------------------------------
This statement should print the values of the original matrix approximately:
{code:java}
System.out.println(svd.getU().multiply(svd.getS()).multiply(svd.getVT()));
{code}
This is true for
{code:java}
public void testMath320A() {
{code}
but not for
{code:java}
public void testMath320B() {
{code}
For reference valus try wolfram alpha:
N[SingularValueDecomposition[{{1,2},{1,2}}]]
was (Author: axelclk):
This statement should print the values of the original matrix approximately:
{code:java}
System.out.println(svd.getU().multiply(svd.getS()).multiply(svd.getVT()));
{code}
This is true for
{code:java}
public void testMath320A() {
{code}
but not for
{code:java}
public void testMath320B() {
{code}
> NaN singular value from SVD
> ---------------------------
>
> Key: MATH-320
> URL: https://issues.apache.org/jira/browse/MATH-320
> Project: Commons Math
> Issue Type: Bug
> Affects Versions: 2.0
> Environment: Linux (Ubuntu 9.10) java version "1.6.0_16"
> Reporter: Dieter Vandenbussche
>
> The following jython code
> Start code
> from org.apache.commons.math.linear import *
>
> Alist = [[1.0, 2.0, 3.0],[2.0,3.0,4.0],[3.0,5.0,7.0]]
>
> A = Array2DRowRealMatrix(Alist)
>
> decomp = SingularValueDecompositionImpl(A)
>
> print decomp.getSingularValues()
> End code
> prints
> array('d', [11.218599757513008, 0.3781791648535976, nan])
> The last singular value should be something very close to 0 since the matrix
> is rank deficient. When i use the result from getSolver() to solve a system, i end \
> up with a bunch of NaNs in the solution. I assumed i would get back a least \
> squares solution. Does this SVD implementation require that the matrix be full \
> rank? If so, then i would expect an exception to be thrown from the constructor or \
> one of the methods.
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