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Columns n and nz' give the order of the matrix and its number of nonzeros as read from the input file.
1 provides data on the size, the number of nonzeros and the application area.
We observed that reordering the approximate Schur complement matrix causes ILUTH to take too much time (presumably due to the need for pivoting), but reduces the number of nonzeros in the factors only by a small amount.
Its complexity is O(nm), where m is the number of nonzeros in A, but in practice it runs at least as fast as a symbolic factorization code.
2) V + n' = nz(A'), because the number of communications caused by a column in A' equals its number of nonzeros minus 1.
The number of nonzeros in them, and in the factor L, is m ([2.
1, where n is the matrix size and nnz is the total number of nonzeros in a full matrix.
For general sparse SPDDD matrices with a constant bound on the number of nonzeros per row, the theoretically optimal value of t is around [n.