Hi,

On 28 May 2011 15:56, 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 ?
>

btw. mpmath has support for matrices with inexact coefficients.


> 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.
>

Look into heurish(), ratint(), gosper_sum(), apart(), rsolve_*() (maybe
other). They all use solver of linear systems. A few print statements will
give you a clue how big and dense are those matrices and what are the
typical types of coefficients. Running sympy.integrals tests this way
reveals that the largest matrix was 100 x 71 and there were very many
matrices of size around 20 x 20 and larger. It's up to you to perform
detailed analysis, but the larger symbolic matrices we will be able to
support the better. As to coefficients I would assume that it will be
rationals in most cases, but from private discussions and a few issues (e.g.
1880) I know that support for large matrices with polynomial or rational
function coefficients will be appreciated.


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Mateusz

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