On 02/23/2012 03:50 PM, Jaakko Luttinen wrote: > Hi! > > I was wondering whether it would be easy/possible/reasonable to have > classes for arrays that have special structure in order to use less > memory and speed up some computations? > > For instance: > - symmetric matrix could be stored in almost half the memory required by > a non-symmetric matrix > - diagonal matrix only needs to store the diagonal vector > - Toeplitz matrix only needs to store one or two vectors > - sparse matrix only needs to store non-zero elements (some > implementations in scipy.sparse) > - and so on
Note to self: BLAS has lots of functions for matrices having special structure (symmetric, triangular, banded, ...), so I suppose it would "only" require some Python class wrappers which are compatible with ndarray/matrix. But I don't know how to make these classes compatible with generic numpy functions such as numpy.multiply/numpy.dot/etc.. -Jaakko > > If such classes were implemented, it would be nice if they worked with > numpy functions (dot, diag, ...) and operations (+, *, +=, ...) easily. > > I believe this has been discussed before but google didn't help a lot.. > > Regards, > Jaakko > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion