Moving Least Squares Approximation using Variably Scaled Discontinuous Weight Function

Functions with discontinuities appear in many applications such as image reconstruction, signal processing, optimal control problems, interface problems, engineering applications and so on. Accurate approximation and interpolation of these functions are therefore of great importance. In this paper, we design a moving least-squares approach for scattered data approximation that incorporates the discontinuities in the weight functions. The idea is to control the influence of the data sites on the approximant, not only with regards to their distance from the evaluation point, but also with respect to the discontinuity of the underlying function. We also provide an error estimate on a suitable piecewise Sobolev Space. The numerical experiments are in compliance with the convergence rate derived theoretically.

READ FULL TEXT

page 13

page 15

research
04/20/2021

Meshfree Approximation for Stochastic Optimal Control Problems

In this work, we study the gradient projection method for solving a clas...
research
12/14/2018

Bernstein approximation of optimal control problems

Bernstein polynomial approximation to a continuous function has a slower...
research
03/29/2021

An image-incorporated immersed boundary method for diffusion equations

A novel sharp interface ghost-cell based immersed boundary method has be...
research
08/24/2019

Analysis of a time-stepping discontinuous Galerkin method for fractional diffusion-wave equation with nonsmooth data

This paper analyzes a time-stepping discontinuous Galerkin method for fr...
research
10/10/2015

Optimal Piecewise Linear Function Approximation for GPU-based Applications

Many computer vision and human-computer interaction applications develop...
research
06/12/2023

Randomized least-squares with minimal oversampling and interpolation in general spaces

In approximation of functions based on point values, least-squares metho...
research
04/10/2019

The Weight Function in the Subtree Kernel is Decisive

Tree data are ubiquitous because they model a large variety of situation...

Please sign up or login with your details

Forgot password? Click here to reset