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I'm looking for a way to implement block-diagonal matrices in Tensorflow. Specifically, I have block-diagonal matrix A with N blocks of size S x S each. Further, I have a vector v of length N*S. I want to calculate A dot v. Is there any efficient way to do it in Tensorflow?

Also, I would prefer the implementation which supports a batch dimension of v (e.g. its real dimension is batch_size x (N*S)) and which is memory efficient, keeping in memory only block-diagonal parts of A.

Thanks for any help!

Dimka Kopitkov
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You can simply convert your tensor to a sparse tensor since a block-diagonal matrix is just a special case of it. Then, the operations are done in a efficient way. If you already have a dense representation of the tensor you can just cast it using sparse_tensor = tf.contrib.layers.dense_to_sparse(dense_tensor). Otherwise, you can construct it with the tf.SparseTensor(...) function. To get the indices, you might use tf.strided_slice, see this post for more information.

Vincent Stimper
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