"iza...@t-..." <iza...@t-...> writes:
> Dear Sirs,
>
> Im using pysparse with the eigenvalue solver jdsym. It works great !!. The
> only problem I have at the moment is the conversion time from the original
> format of my matrix. Here is a description of my process:
>
> - Read a file wit around 20000 points
> - Process this points and get a very big array (20000*20000) (this part is done
> with pyrex)
> - I reshape the array into a matrix (20000,20000)
> - I do some math ( matrix transpose, matrix scaling, dot product, transpose,
> etc.)
> - I get a matrix from which I would like to obtain a couple of eigenvectors
>
> In order to use jdsm I need the matrix in linked list format. I have tried
> several things:
>
> - write the matrix in matrix market format (using scipy) and the read with
> spmatrix.ll_mat_from_mtx
> (it takes very very long)
> - convert directly to sparse coo format and then into ll format
> (I get an error that this format is not supported for conversion)
> - use the internal routine put(V,index1,index2)
I suggest you use nonzero() method of numpy arrays in case you do not.
> (this is faster but still takes a long time compared with the eigenvalue
> problem)
> - I also use
> for ii in range(N):
> for jj in range(N):
> Al[ii,jj]=K[ii,jj]
> (this is almost the same as using put)
>
>
> Is there any way to have it faster in the right format? This will improve a lot
> the all process of obtaining the eigenvalues. If would be great to have
> something like:
>
> spmatrix.ll_mat(K)
>
> where K is a full matrix.
>
> I appreciate any help with this problem.
>
> Best regards
>
> Rodrigo
>
>
>
>
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