Black box approximation of tensors in hierarchical Tucker format

We derive and analyse a scheme for the approximation of order d tensors A∈Rn1×⋯×nd in the hierarchical (H-) Tucker format, a dimension-multilevel variant of the Tucker format and strongly related to the TT (tensor train) format. For a fixed rank parameter k, the storage complexity of a tensor in H-Tucker format is and we present a (heuristic) algorithm that finds an approximation to a tensor in the H-Tucker format in by inspection of only entries. Under mild assumptions, tensors in the H-Tucker format are reconstructed. For general tensors we derive error bounds that are based on the approximability of matrices (matricizations of the tensor) by few outer products of its rows and columns. The construction parallelizes with respect to the order d and we also propose an adaptive approach that aims at finding the rank parameter for a given target accuracy ε automatically.