Multivariate approximation by polynomial and generalised rational functions

TL;DR

This paper introduces a quasiconvex optimisation approach for multivariate polynomial and rational approximation on finite grids, demonstrating efficient solutions and comparing their performance.

Contribution

It shows that multivariate rational approximation problems are quasiconvex and applies a bisection method with linear programming to solve them efficiently.

Findings

Quasiconvexity of multivariate rational approximation problems.

Bisection method effectively finds optimal solutions.

Comparison of approximation error and computational time.

Abstract

In this paper we develop an optimisation based approach to multivariate Chebyshev approximation on a finite grid. We consider two models: multivariate polynomial approximation and multivariate generalised rational approximation. In the second case the approximations are ratios of linear forms and the basis functions are not limited to monomials. It is already known that in the case of multivariate polynomial approximation on a finite grid the corresponding optimisation problems can be reduced to solving a linear programming problem, while the area of multivariate rational approximation is not so well understood.In this paper we demonstrate that in the case of multivariate generalised rational approximation the corresponding optimisation problems are quasiconvex. This statement remains true even when the basis functions are not limited to monomials. Then we apply a bisection method, which is a general method for quasiconvex optimisation. This method converges to an optimal solution with given precision. We demonstrate that the convex feasibility problems appearing in the bisection method can be solved using linear programming. Finally, we compare the deviation error and computational time for multivariate polynomial and generalised rational approximation with the same number of decision variables.