Open dvirginz opened 4 years ago
Currently we only support sparse tensors using numpy and the PyData Sparse library. We are planning to add others but currently pytorch's support for sparse tensors is quite minimal.
If you want to use sparse tensors in pytorch it would probably be more efficient to use their builtin datastructure than creating your own.
I'm a bit lost in this awesome library, and could not find a suitable example, hence not sure if and how to use tensorly for a "simple" SVD decomposition for PyTorch tensors (if possible).
I.e
Given two tensors
edge_index
of dimension(2,E)
represents theX,Y
entries of a matrix, andedge_value
of dimension(E)
represents the values of those entries, am I able to create the sparse SVD decomposition of such matrix using tensorly sparse API?