Uniqueness of solutions in multivariate Chebyshev approximation problems

TL;DR

This paper investigates the solution set structure in multivariate Chebyshev approximation, especially in ill-posed cases, providing bounds, generic nonuniqueness results, and construction methods for maximal solution sets.

Contribution

It introduces bounds on the solution set dimension, demonstrates generic nonuniqueness in ill-posed problems, and constructs functions with maximal solution sets for given points.

Findings

Nonuniqueness is generic in ill-posed problems on discrete domains.

An upper bound on the dimension of the solution set is established.

Methods to construct functions with maximal solution sets are provided.

Abstract

We study the solution set to multivariate Chebyshev approximation problem, focussing on the ill-posed case when the uniqueness of solutions can not be established via strict polynomial separation. We obtain an upper bound on the dimension of the solution set and show that nonuniqueness is generic for the ill-posed problems on discrete domains. Moreover, given a prescribed set of points of minimal and maximal deviation we construct a function for which the dimension of the set of best approximating polynomials is maximal for any choice of domain. We also present several examples that illustrate the aforementioned phenomena, demonstrate practical application of our results and propose a number of open questions.