Computing the density of states for optical spectra by low-rank and QTT tensor approximation
Peter Benner, Venera Khoromskaia, Boris N. Khoromskij, and Chao Yang
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Submission date: 09. May. 2018
published in: Journal of computational physics, 382 (2019), p. 221-239
DOI number (of the published article): 10.1016/j.jcp.2019.01.011
with the following different title: Computing the density of states for optical spectra of molecules by low-rank and QTT tensor approximation
MSC-Numbers: 65F30, 65F50, 65N35, 65F10
Keywords and phrases: Bethe-Salpeter equation, density of states, absorption spectrum, tensor decompositions, low-rank matrix, QTT tensor approximation, model reduction
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In this paper, we introduce a new interpolation scheme to approximate the density of states (DOS) for a class of rank-structured matrices with application to the Tamm-Dancoﬀ approximation (TDA) of the Bethe-Salpeter equation (BSE). The presented approach for approximating the DOS is based on two main techniques. First, we propose an economical method for calculating the traces of parametric matrix resolvents at interpolation points by taking advantage of the block-diagonal plus low-rank matrix structure described in [6,3] for the BSE/TDA problem. Second, we show that a regularized or smoothed DOS discretized on a ﬁne grid of size N can be accurately represented by a low rank quantized tensor train (QTT) tensor that can be determined through a least squares ﬁtting procedure. The latter provides good approximation properties for strictly oscillating DOS functions with multiple gaps, and requires asymptotically much fewer (O(log N)) functional calls compared with the full grid size N. This approach allows us to overcome the computational diﬃculties of the traditional schemes by avoiding both the need of stochastic sampling and interpolation by problem independent functions like polynomials etc. Numerical tests indicate that the QTT approach yields accurate recovery of DOS associated with problems that contain relatively large spectral gaps. The QTT tensor rank only weakly depends on the size of a molecular system which paves the way for treating large-scale spectral problems.