7 papers · 1 filter
Low-rank kernel methods for American option pricing
Michael Multerer, Paul Schneider, Chiara Segala
We propose a scalable and theoretically grounded low-rank conditional expectation model for recursive Monte Carlo optimal stopping problems, in particular American option pricing.…
Kernel interpolation on generalized sparse grids
Michael Griebel, Helmut Harbrecht, Michael Multerer
We consider scattered data approximation on product regions of equal and different dimensionality. On each of these regions, we assume quasi-uniform but unstructured data sites and…
Samplet limits and multiwavelets
Gianluca Giacchi, Michael Multerer, Jacopo Quizi
Samplets are data adapted multiresolution analyses of localized discrete signed measures. They can be constructed on scattered data sites in arbitrary dimension such that they exhi…
Tree-Adaptive Multiscale Kernel Lasso in Samplet Coordinates
Sara Avesani, Gaia Fumagalli, Michael Multerer +1
We develop a novel framework for sparse multiscale kernel approximation of large scattered data problems based on a samplet representation. Samplets form a multiresolution analysis…
Multiresolution local smoothness detection in non-uniformly sampled multivariate signals
Sara Avesani, Gianluca Giacchi, Michael Multerer
Inspired by edge detection based on the decay behavior of wavelet coefficients, we introduce a (near) linear-time algorithm for detecting the local regularity in non-uniformly samp…
Samplets: Wavelet concepts for scattered data
Helmut Harbrecht, Michael Multerer
This chapter is dedicated to recent developments in the field of wavelet analysis for scattered data. We introduce the concept of samplets, which are signed measures of wavelet typ…