12 citations · 17 across the 6 of their papers we have counts for
8 papers · 1 filter
Optimal approximation of infinite-dimensional holomorphic functions II: recovery from i.i.d. pointwise samples
Ben Adcock, Nick Dexter, Sebastian Moraga
Infinite-dimensional, holomorphic functions have been studied in detail over the last several decades, due to their relevance to parametric differential equations and computational…
Optimal approximation of infinite-dimensional holomorphic functions
Ben Adcock, Nick Dexter, Sebastian Moraga
Over the last decade, approximating functions in infinite dimensions from samples has gained increasing attention in computational science and engineering, especially in computatio…
Stable and accurate least squares radial basis function approximations on bounded domains
Ben Adcock, Daan Huybrechs, Cécile Piret
The computation of global radial basis function (RBF) approximations requires the solution of a linear system which, depending on the choice of RBF parameters, may be ill-condition…
Towards optimal sampling for learning sparse approximation in high dimensions
Ben Adcock, Juan M. Cardenas, Nick Dexter +1
In this chapter, we discuss recent work on learning sparse approximations to high-dimensional functions on data, where the target functions may be scalar-, vector- or even Hilbert…
Frame approximation with bounded coefficients
Ben Adcock, Mohsen Seifi
Due to their flexibility, frames of Hilbert spaces are attractive alternatives to bases in approximation schemes for problems where identifying a basis is not straightforward or ev…
Near-optimal sampling strategies for multivariate function approximation on general domains
Ben Adcock, Juan M. Cardenas
In this paper, we address the problem of approximating a multivariate function defined on a general domain in dimensions from sample points. We consider weighted least-squares…