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We present concepts of data-sparse tensor approximations to the functions and operators arising in many-particle models of quantum chemistry.
Our approach is based on the systematic use of structured tensor-product representations where the low-dimensional components are represented in hierarchical or wavelet based matrix formats. The modern methods of tensor-product approximation in higher dimensions are discussed with the focus on analytically based approaches.
We give numerical illustrations which confirm the efficiency of tensor decomposition techniques in electronic structure calculations.