[NumPy-Tickets] [NumPy] #1655: Poor precision using linalg.solve for certain matrices

[NumPy Trac]

#1655: Poor precision using linalg.solve for certain matrices
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 Reporter:  zoof          |       Owner:  pv   
     Type:  defect        |      Status:  new  
 Priority:  normal        |   Milestone:  2.0.0
Component:  numpy.linalg  |     Version:  1.5.0
 Keywords:                |  
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 There appears to be a bug in either the linear system solver or in the way
 than linalg computes LU decompositions.  On a number of different
 distributions, using somewhat different versions of numpy, I get much
 better precision by inverting the matrix (linalg.inv) and multiplying than
 by either linalg.solve alone or linalg.cholesky in combination with
 linalg.solve.  An example has been posted to: http://pastebin.com/KpXdTVh7

 numpy 1.3.0 on Ubuntu Lucid
 numpy 1.5.0 on Arch
 numpy 1.5.1rc1 on Centos

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Ticket URL: <http://projects.scipy.org/numpy/ticket/1655>
NumPy <http://projects.scipy.org/numpy>
My example project