Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions

Abstract

In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tucker format. We develop novel randomized techniques to accomplish the tensor compression, provide a detailed analysis of the computational costs, provide insight into the error of the resulting approximations, and discuss the benefits of the proposed approaches. We also apply the tensor-based function approximation to develop low-rank matrix approximations to kernel matrices that describe pairwise interactions between two sets of points; the resulting low-rank approximations are efficient to compute and store (the complexity is linear in the number of points). We have detailed numerical experiments on example problems involving multivariate function approximation, low-rank matrix approximations of kernel matrices involving well-separated clusters of sources and target points, and a global low-rank approximation of kernel matrices with an application to Gaussian processes.

Decay estimate of bivariate Chebyshev coefficients for functions with limited smoothness

“…low-rank approximation via Tucker decomposition is used to approximate multivariate functions in the context of Chebfun (for trivariate functions, see[4,7]) and for multivariate functions see,[13].…”