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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