Approximate iterations for structured matrices

Important matrix-valued functions f (A) are, e.g., the inverse A −1 , the square root √ A and the sign function. Their evaluation for large matrices arising from pdes is not an easy task and needs techniques exploiting appropriate structures of the matrices A and f (A) (often f (A) possesses this structure only approximately). However, intermediate matrices arising during the evaluation may lose the structure of the initial matrix. This would make the computations inefficient and even infeasible. However, the main result of this paper is that an iterative fixed-point like process for the evaluation of f (A) can be transformed, under certain general assumptions, into another process which preserves the convergence rate and benefits from the underlying structure. It is shown how this result applies to matrices in a tensor format with a bounded tensor rank and to the structure of the hierarchical matrix technique. We demonstrate our results by verifying all requirements in the case of the iterative computation of A −1 and √ A

Problems with Jumping Coefficients

We study separability properties of solutions of elliptic equations with piecewise constant coefficients in R d, d ≥ 2. Besides that, we develop efficient tensor-structured preconditioner for the diffusion equation with variable coefficients. It is based only on rank structured decomposition of the tensor of reciprocal coefficient and on the decomposition of the inverse of the Laplacian operator. It can be applied to full vector with linear-logarithmic complexity in the number of unknowns N. It also allows lowrank tensor representation, which has linear complexity in dimension d, hence, it gets rid of the “curse of dimensionality ” and can be used for large values of d. Extensive numerical tests are presented. AMS Subject Classification: 65F30, 65F50, 65N35, 65F10 Key words: structured matrices, elliptic operators, Poisson equation, matrix approximations

Superfast solution of linear convolutional Volterra equations using QTT approximation

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Computational and Applied Mathematics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Applied and Computational Mathematics, 260, 2014, doi: 10.1016/j.cam.2013.10.025This article address a linear fractional differential equation and develop effective solution methods using algorithms for the inversion of triangular Toeplitz matrices and the recently proposed QTT format. The inverses of such matrices can be computed by the divide and conquer and modified Bini’s algorithms, for which we present the versions with the QTT approximation. We also present an efficient formula for the shift of vectors given in QTT format, which is used in the divide and conquer algorithm. As a result, we reduce the complexity of inversion from the fast Fourier level O(nlogn) to the speed of superfast Fourier transform, i.e., O(log^2n). The results of the paper are illustrated by numerical examples.During this work D. V. Savostyanov and E. E. Tyrtyshnikov were supported by the Leverhulme Trust to visit, stay and work at the University of Chester, as the Visiting Research Fellow and the Visiting Professor, respectively. Their work was also supported in part by RFBR grants 11-01-00549, 12-01-91333-nnio-a, 13-01-12061, and Russian Federation Government Contracts 14.740.11.0345, 14.740.11.1067, 16.740.12.0727

Numerical Analysis Reports DEPARTMENTS OF MATHEMATICS

Solving linear systems using wavelet compression combined with Kronecker product approximatio