The mosaic-skeleton method was bred in a simple observation that rather large blocks in very large matrices coming from integral formulations can be approximated accurately by a sum of just few rank-one matrices (skeletons). These blocks might correspond to a region where the kernel is smooth enough, and anyway it can be a region where the kernel is approximated by a short sum of separable functions (functional skeletons). Since the effect of approximations is like that of having small-rank matrices, we find it pertinent to say about mosaic ranks of a matrix which turn out to be pretty small for many nonsingular matrices.
On the first stage, the method builds up an appropriate mosaic partitioning using the concept of a tree of clusters and some extra information rather than the matrix entries (related to the mesh). On the second stage, it approximates every allowed block by skeletons using the entries of some rather small cross which is chosen by an adaptive procedure. We focus chiefly on some aspects of practical implementation and numerical examples on which the approximation time was found to grow almost linearly in the matrix size.
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