[SciPy-user] Monte Carlo & sparse.linsolve for medium size arrays - any strategies?

[Christopher Mutel]

Hello-

I am working with medium size sparse matrices (4000 by 4000, coverage
of ~1%), and need to do uncertainty analysis using a Monte Carlo
approach. For each element in the matrix, I have an average value, the
distribution (mostly lognormal, some normal and uniform), and the
relevant uncertainty parameters. I am solving the classic Ax = B
matrix problem, where the large matrix is A; B is a constant array. I
anticipate needing to do on the order of 1000 iterations.

SciPy is a fantastic library, but I am not creative enough to come up
with an efficient way to store the relevant uncertainty information so
that I am not iteratively generating the large matrix for each Monte
Carlo run. Is there a clever way to construct or sublcass a sparse
matrix as a generator, so that each time it is referenced a new matrix
is generated? Or is there a better approach (i.e. I am sure there is a
better approach that I haven't thought of)? I know that this is a
rather general question, but I have been thinking about this off and
on for quite a while, and have had great luck in the past getting help
on this list.

Thanks in advance!

-Chris

-- 
############################
Chris Mutel
Ökologisches Systemdesign - Ecological Systems Design
Institut f.Umweltingenieurwissenschaften - Institute for Environmental
Engineering
ETH Zürich - HIF C 42 - Schafmattstr. 6
8093 Zürich

Telefon: +41 44 633 71 45 - Fax: +41 44 633 10 61
############################