[SciPy-User] [SciPy-user] Matrix Exponentials For Very Large Sparse Matrices
Mon Jan 25 16:04:53 CST 2010
Thanks for the expokit suggestion. I seem to have gotten it installed
on my Mac OS X Leopard system. The presentation you linked indicated
that I need to change the call to matvec in expokit.f to include n, so
I first replaced all instances of 'call matvec(' in expokit.f with
'call matvec(n,', but I'm not sure exactly when this should be done. I
f2py -m expokit -h expokit.pyf expokit.f
f2py -c expokit.pyf expokit.f --link-lapack-opt
which produced the expokit.so file.
However, now I'm trying to reproduce the example given in the
presentation you linked, and the call to dmexpv() does not seem to
work. It wants a lot of arguments:
>>> from scipy import *
>>> from expokit import dmexpv
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: expokit.dmexpv() takes at least 11 arguments (0 given)
The most confusing thing it wants me to pass is matvec, which is
apparently an external matrix-vector multiplication function. I can't
seem to figure out how to get this function to work. Tthe presentation
uses a much simpler call:
dmexpv(m, t, v, wsp, iwsp, A)
Can you offer any insight into this problem?
On Jan 22, 2010, at 6:47 PM, Joshua Stults wrote:
> On Fri, Jan 22, 2010 at 8:58 PM, Burak1327
> <email@example.com> wrote:
>> The most simple approximations are polynomial expansions.
>> Chebychev polynomials are GREAT, even a 2nd order
>> Taylor expansion is good enough in a lot of cases, specific to
>> your type of problem.
>> Which leads to actual scipy discussion. I'm no scipy expert, but
>> the above mentioned methods are probably in the library.
> Here's an example of using f2py to compile expokit (see slides 15 -
> Expokit website: http://www.maths.uq.edu.au/expokit/
> Uses Krylov methods for sparse matrices; these will use more memory
> than the polynomial expansion methods that Burak mentioned.
> Joshua Stults
> Website: http://j-stults.blogspot.com
> SciPy-User mailing list
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