I am trying to use the Assimulo package to solve a set of differential algebraic equations (DAEs). I am trying to use an algorithm similar to that shown here. However, there does not seem to be an option to pass in a sparse matrix. My Jacobian matrix is very large, approximately 3000 x 3000. Do you know if there is a way to solve my DAEs more computationally efficiently?
Python: How to solve DAE with Jacobian efficiently?
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In my experience with sparse ODE systems (more precisely with systems of semi-discretized PDEs), using an iterative linear solver greatly enhances numerical efficiency. As far as I know, Assimulo doesn't allow to provide a jacobian sparsity pattern, but changing the linear solver is another way to tackle this.
You would do something like:
I'm not sure if this also applies for DAE systems, but I think it's worth giving this a try.