Hierarchical Kronecker tensor-product approximation to a class of nonlocal operators in high dimensions
Wolfgang Hackbusch and Boris N. Khoromskij
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Submission date: 13. Apr. 2004 (revised version: July 2004)
paper submitted to: Computing
MSC-Numbers: 65F50, 65F30, 46B28, 47A80
Keywords and phrases: hierarchical matrices, kronecker tensor-product, high spatial dimension
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The class of -matrices allows an approximate matrix arithmetic with almost linear complexity. The combination of the hierarchical and tensor-product format offers the opportunity for efficient data-sparse representations of integral operators and the inverse of elliptic operators in higher dimensions. In the present paper, we apply the -matrix techniques combined with the Kronecker tensor-product approximation to represent integral operators as well as certain functions (A) of a discrete elliptic operator A in a hypercube d ∈ ℝd in the case of a high spatial dimension d. In particular, we approximate the functions A-1 and sign(A) of a finite difference discretisations A ∈ ℝN×N with rather general location of the spectrum. The asymptotic complexity of our data-sparse representations can be estimated by (np log qn), p = 1,2, with q independent of d, where n = N1∕d is the dimension of the discrete problem in one space direction.