Ubiquitous Nature of the Reduced Higher Order SVD in Tensor-Based Scientific Computing

Abstract

Tensor numerical methods, based on the rank-structured tensor representation of d-variate functions and operators discretized on large n⊗d grids, are designed to provide O(dn) complexity of numerical calculations contrary to O(nd) scaling by conventional grid-based methods. However, multiple tensor operations may lead to enormous increase in the tensor ranks (curse of ranks) of the target data, making calculation intractable. Therefore, one of the most important steps in tensor calculations is the robust and efficient rank reduction procedure which should be performed many times in the course of various tensor transforms in multi-dimensional operator and function calculus. The rank reduction scheme based on the Reduced Higher Order SVD (RHOSVD) introduced by the authors, played a significant role in the development of tensor numerical methods. Here, we briefly survey the essentials of RHOSVD method and then focus on some new theoretical and computational aspects of the RHOSVD and demonstrate that this rank reduction technique constitutes the basic ingredient in tensor computations for real-life problems. In particular, the stability analysis of RHOSVD is presented. We introduce the multi-linear algebra of tensors represented in the range-separated (RS) tensor format. This allows to apply the RHOSVD rank-reduction techniques to non-regular functional data with many singularities, for example, to the rank-structured computation of the collective multi-particle interaction potentials in bio-molecular modeling, as well as to complicated composite radial functions. The new theoretical and numerical results on application of the RHOSVD in scattered data modeling are presented. We underline that RHOSVD proved to be the efficient rank reduction technique in numerous applications ranging from numerical treatment of multi-particle systems in material sciences up to a numerical solution of PDE constrained control problems in ℝd.