Backend-agnostic is one of my major goals too. One of its cons is that being unaware of the type can sometimes lead to lack of performance. I don't know if I mentioned this before, but I faced a dilemma with sum and Add. Add is faster but only works for Sympy objects. sum is general but slower.
This maybe the wrong thread to say this, but maybe sum could be modified to use Add more smartly when the objects are all Sympy objects. Views ? On May 28, 10:10 pm, Matthew Rocklin <[email protected]> wrote: > 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 <[email protected]>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. > > > -- > > You received this message because you are subscribed to the Google Groups > > "sympy" group. > > To post to this group, send email to [email protected]. > > To unsubscribe from this group, send email to > > [email protected]. > > For more options, visit this group at > >http://groups.google.com/group/sympy?hl=en. -- You received this message because you are subscribed to the Google Groups "sympy" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
