In my work I would like to use SymPy Matrices to represent matrices before I
turn them into large numeric matrices for computation. I.e. I probably
wouldn't use symbolic matrices for actual computation, I would love to use
them to manipulate the math as much as possible beforehand.

To this end I would like to see SymPy matrices implement pluggable backends.
It would be nice to be able to represent mathematical matrices and then,
once everything has been optimized, to substitute in a Numpy Matrix, Sparse
Matrix, Linear Operator, C-Code, External program, etc.... Others will
certainly want to substitute in pure SymPy matrices or pure SymPy sparse
matrices.

Regarding your previous post about Sparse matrix algorithms I think the same
thinking could apply. It would be nice to have the algorithms be
backend-agnostic. I'm sure some people will want sparse matrices of pure
SymPy objects, others might want speed. Others might have a new datatype
that you haven't anticipated. If it's possible it would be nice to let the
type of the elements be switchable.

-matt

On Sat, May 28, 2011 at 8:56 AM, SherjilOzair <sherjiloz...@gmail.com>wrote:

> I would like to know how and where Sympy's matrices are used.
> Is Sympy matrices used for numeric computing anywhere ?
> Are Sympy Matrices expected to offer any advantage that matrices in
> numpy/scipy or other libraries cannot offer ?
>
> Is its use limited to symbolic ? What size of Matrices with symbolic
> content is used ?
> Operations on Expr are way costlier than operations on numerics. So,
> knowing the size of the symbolic matrices that are required would help
> me in optimization when writing algorithms for sparse matrices, and
> also when refactoring Matrix.
>
> I expect that one cannot use too large symbolic matrices, as solving/
> inversing/etc. would result in expression blowup.
>
> I would be glad if you could also tell what running time you would
> expect from the matrices that you use.
>
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