Numerical Linear Algebra in arbitrary precision

[Robert Kern]

On 2/17/12 6:09 AM, Tim Roberts wrote:
> Ken<ken.allen at sbcglobal.net>  wrote:
>>
>> Brand new Python user and a bit overwhelmed with the variety of
>> packages available.  Any recommendation for performing numerical
>> linear algebra (specifically least squares and generalized least
>> squares using QR or SVD) in arbitrary precision?  I've been looking at
>> mpmath but can't seem to find much info on built in functions except
>> for LU decomposition/solve.
>
> It is been my experience that numpy is the best place to start with
> requests like this, although I don't know whether it will actually solve
> your specific tasks:
>
> http://docs.scipy.org/doc/numpy/reference/routines.linalg.html

This will not do arbitrary-precision, though. We use the double- and 
single-precision routines from LAPACK.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco