Quadrature Strategies for Constructing Polynomial Approximations

TL;DR

This paper reviews classical and recent quadrature strategies for constructing polynomial approximations, categorizing methods into sampling and optimization approaches, and illustrating their implementation through examples.

Contribution

It provides a comprehensive categorization and comparison of quadrature strategies for polynomial approximation, highlighting recent advances and practical examples.

Findings

Sampling approaches offer favorable domain discretization

Optimization methods effectively select subset samples

Various strategies can be combined for improved polynomial approximation

Abstract

Finding suitable points for multivariate polynomial interpolation and approximation is a challenging task. Yet, despite this challenge, there has been tremendous research dedicated to this singular cause. In this paper, we begin by reviewing classical methods for finding suitable quadrature points for polynomial approximation in both the univariate and multivariate setting. Then, we categorize recent advances into those that propose a new sampling approach and those centered on an optimization strategy. The sampling approaches yield a favorable discretization of the domain, while the optimization methods pick a subset of the discretized samples that minimize certain objectives. While not all strategies follow this two-stage approach, most do. Sampling techniques covered include subsampling quadratures, Christoffel, induced and Monte Carlo methods. Optimization methods discussed range from linear programming ideas and Newton's method to greedy procedures from numerical linear algebra. Our exposition is aided by examples that implement some of the aforementioned strategies.