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From "Dieter Vandenbussche (JIRA)" <>
Subject [jira] Created: (MATH-320) NaN singular value from SVD
Date Tue, 10 Nov 2009 15:43:27 GMT
NaN singular value from SVD

                 Key: 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
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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