On Mon, Apr 4, 2011 at 2:15 AM, Aaron S. Meurer <asmeu...@gmail.com> wrote:
> Well, how sparse does a matrix have to be for a sparse implementation to be 
> faster than the dense representation, in general?

I haven't really tested this but most of the matrices I use in my day
job only have a few percentage of non-zeros (for example on the order
of 10 - 20 entries for rows at 100K).

>
> There's also the line somewhere in between numerical matrices and symbolic 
> matrices, which is matrices with exact numeric values (instances of 
> Rational), to consider.

Not sure what you are getting at here.

>
> I remember people posting to this list with symbolic matrices in the past.  
> Maybe a search will show how people are using them.

The only things I could find were with small matrices.

-- Andy

>
> Aaron Meurer
>
> On Apr 3, 2011, at 9:10 AM, Andy Ray Terrel wrote:
>
>> Hello,
>>
>> We currently have several students interested in symbolic sparse
>> matrix calculations for GSOC.  IMHO, we could use a linear algebra
>> interface more compatible with scipy.linalg but I don't have a use for
>> symbolic sparse matrices.
>>
>> Since I believe in not adding unnecessary features to a library, I
>> would like to know who the customers of a symbolic sparse matrix
>> format would be and what applications, point to code please, we need
>> to speed up.
>>
>> Also how big are the matrices you are working with.  You have to
>> remember that sparse matrices usually don't make sense on typical
>> numerical algorithms until you hit about 1000x1000, because optimized
>> BLAS can stream so much better.  I would guess the number is much
>> lower with symbolics, but the storage will not be an issue until that
>> size since we only have a pointer to S(0).
>>
>> Having this information would be quite beneficial in organizing what
>> the student projects should implement.
>>
>> -- Andy
>>
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