Affects Version/s: 2.0
Fix Version/s: 2.1
Linux (Ubuntu 9.10) java version "1.6.0_16"
The following jython 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)
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.