[SciPy-user] fsolve with sparse matrices

[Jan Wicijowski]

Hi all!

I would like to ask you, if anyone was ever confronted with solving  
nonlinear system with scipy.optimize.fsolve, with huge no. of equations,  
say 10000.

The usual approach fails, because the size of jacobian matrix is  
proportional to the square of equations number. The paging kills my OS.

In my problem, which is solving a nonlinear electrical circuit, the matrix  
is mostly zeros, which has immediately led me to trying sparse matrices.

The first obstacle was numpy.atleast_1d contained in  
scipy.optimize.minpack.check_func - instead of preserving sparse matrix as  
is, it has a suprising behaviour of boxing the matrix in a list. This may  
be a bug, but I may post it elsewhere.

Injecting custom function instead of scipy.optimize.minpack.check_func  
didn't work as well with fsolve. This wasn't surprising, as I guessed,  
that FORTRAN hybrj won't be able to deal with interfacing with scipy  
matrices.

So, am I left with rewriting the original FORTRAN hybrj source to python,  
or is there somebody, who dealt with such problem?

Regards,
Jan Wicijowski