28,009 research outputs found
Low-rank approximate inverse for preconditioning tensor-structured linear systems
In this paper, we propose an algorithm for the construction of low-rank
approximations of the inverse of an operator given in low-rank tensor format.
The construction relies on an updated greedy algorithm for the minimization of
a suitable distance to the inverse operator. It provides a sequence of
approximations that are defined as the projections of the inverse operator in
an increasing sequence of linear subspaces of operators. These subspaces are
obtained by the tensorization of bases of operators that are constructed from
successive rank-one corrections. In order to handle high-order tensors,
approximate projections are computed in low-rank Hierarchical Tucker subsets of
the successive subspaces of operators. Some desired properties such as symmetry
or sparsity can be imposed on the approximate inverse operator during the
correction step, where an optimal rank-one correction is searched as the tensor
product of operators with the desired properties. Numerical examples illustrate
the ability of this algorithm to provide efficient preconditioners for linear
systems in tensor format that improve the convergence of iterative solvers and
also the quality of the resulting low-rank approximations of the solution
Block-adaptive Cross Approximation of Discrete Integral Operators
In this article we extend the adaptive cross approximation (ACA) method known
for the efficient approximation of discretisations of integral operators to a
block-adaptive version. While ACA is usually employed to assemble hierarchical
matrix approximations having the same prescribed accuracy on all blocks of the
partition, for the solution of linear systems it may be more efficient to adapt
the accuracy of each block to the actual error of the solution as some blocks
may be more important for the solution error than others. To this end, error
estimation techniques known from adaptive mesh refinement are applied to
automatically improve the block-wise matrix approximation. This allows to
interlace the assembling of the coefficient matrix with the iterative solution
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