Low-rank methods for high-dimensional approximation and model order reduction

TL;DR

This paper discusses low-rank tensor approximation methods for high-dimensional problems, focusing on algorithms for tensor compression, completion, and model reduction, especially in stochastic and parametric contexts.

Contribution

It introduces new algorithms for low-rank tensor approximation, analyzing their connection to model reduction and applying them to high-dimensional and stochastic problems.

Findings

Algorithms for low-rank tensor approximation are effective for high-dimensional problems.

Constructive greedy algorithms improve tensor compression and completion.

Applications include stochastic models and parametric analyses.

Abstract

Tensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated. These methods exploit the tensor structure of function spaces and apply to many problems in computational science which are formulated in tensor spaces, such as problems arising in stochastic calculus, uncertainty quantification or parametric analyses. Here, we present complexity reduction methods based on low-rank approximation methods. We analyze the problem of best approximation in subsets of low-rank tensors and discuss its connection with the problem of optimal model reduction in low-dimensional reduced spaces. We present different algorithms for computing approximations of a function in low-rank formats. In particular, we present constructive algorithms which are based either on a greedy construction of an approximation (with successive corrections in subsets of low-rank tensors) or on the greedy construction of tensor subspaces (for subspace-based low-rank formats). These algorithms can be applied for tensor compression, tensor completion or for the numerical solution of equations in low-rank tensor formats. A special emphasis is given to the solution of stochastic or parameter-dependent models. Different approaches are presented for the approximation of vector-valued or multivariate functions (identified with tensors), based on samples of the functions (black-box approaches) or on the models equations which are satisfied by the functions.