I am trying to do SVD of a large block-hankel matrix for model order reduction (Low rank approximation). However, I quickly run into memory issues in forming the large Block-Hankel matrix and CPU issues due to running SVD itself.
In theory, it seems like we don't have to form the Hankel matrix itself. As long as we have the elements of either the first row/column of the matrix, we have complete information of all the entries of the Hankel matrix, and we should be able to perform the SVD without even forming this large matrix in memory ?
Is there any algorithm (in netlib or any of the other numerical libraries) or any other user-submitted MATLAB package that takes advantage of the special structure of the block-Hankel matrix ?