Details

    • Type: Bug
    • Status: Closed
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 2.0
    • Fix Version/s: 2.1
    • Labels:
      None
    • Environment:

      Linux (Ubuntu 9.10) java version "1.6.0_16"

      Description

      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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            • Assignee:
              Unassigned
              Reporter:
              dieterv77 Dieter Vandenbussche
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              Dates

              • Created:
                Updated:
                Resolved: