Grid-based lattice summation of electrostatic potentials by assembled rank-structured tensor approximation

Our recent method for low-rank tensor representation of sums of the arbitrarily positioned electrostatic potentials discretized on a 3D Cartesian grid reduces the 3D tensor summation to operations involving only 1D vectors however retaining the linear complexity scaling in the number of potentials. Here, we introduce and study a novel tensor approach for fast and accurate assembled summation of a large number of lattice-allocated potentials represented on 3D N×N×NN×N×N grid with the computational requirements only weakly dependent   on the number of summed potentials. It is based on the assembled low-rank canonical tensor representations of the collected potentials using pointwise sums of shifted canonical vectors representing the single generating function, say the Newton kernel. For a sum of electrostatic potentials over L×L×LL×L×L lattice embedded in a box the required storage scales linearly in the 1D grid-size, O(N)O(N), while the numerical cost is estimated by O(NL)O(NL). For periodic boundary conditions, the storage demand remains proportional to the 1D grid-size of a unit cell, n=N/Ln=N/L, while the numerical cost reduces to O(N)O(N), that outperforms the FFT-based Ewald-type summation algorithms of complexity O(N3logN)O(N3logN). The complexity in the grid parameter NN can be reduced even to the logarithmic scale O(logN)O(logN) by using data-sparse representation of canonical NN-vectors via the quantics tensor approximation. For justification, we prove an upper bound on the quantics ranks for the canonical vectors in the overall lattice sum. The presented approach is beneficial in applications which require further functional calculus with the lattice potential, say, scalar product with a function, integration or differentiation, which can be performed easily in tensor arithmetics on large 3D grids with 1D cost. Numerical tests illustrate the performance of the tensor summation method and confirm the estimated bounds on the tensor ranks.