Numerical Linear Algebra in arbitrary precision

[Ken]

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.

Appreciate any comments.

Ken

 

[Robert Kern]

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

 

[Albert van der Horst]

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.

Arbitrary precision? As in automatically increasing precision to
stay exact? You will find this impractical as the number of decimals
will explode, or you will find it not at all.

If you mean that you want to be able to select something with larger
precision than single or double floats, numpy is the starting point.

Appreciate any comments.

Ken

Groetjes Albert