Generation of Nested Quadrature Rules for Generic Weight Functions via Numerical Optimization: Application to Sparse Grids

TL;DR

This paper introduces a flexible numerical framework for generating nested quadrature rules applicable to various weight functions, enhancing sparse grid construction for high-dimensional integration tasks.

Contribution

It generalizes the Kronrod rule to any continuous probability density function using a bi-level optimization approach, enabling efficient nested quadrature rule generation.

Findings

Successfully generates nested quadrature rules for diverse weight functions.

Demonstrates improved sparse grid accuracy and efficiency in high-dimensional problems.

Extends Gauss-Kronrod-Patterson rules to Chebyshev polynomials.

Abstract

We present a numerical framework for computing nested quadrature rules for various weight functions. The well-known Kronrod method extends the Gauss-Legendre quadrature by adding new optimal nodes to the existing Gauss nodes for integration of higher order polynomials. Our numerical method generalizes the Kronrod rule for any continuous probability density function on real line with finite moments. We develop a bi-level optimization scheme to solve moment-matching conditions for two levels of main and nested rule and use a penalty method to enforce the constraints on the limits of the nodes and weights. We demonstrate our nested quadrature rule for probability measures on finite/infinite and symmetric/asymmetric supports. We generate Gauss-Kronrod-Patterson rules by slightly modifying our algorithm and present results associated with Chebyshev polynomials which are not reported elsewhere. We finally show the application of our nested rules in construction of sparse grids where we validate the accuracy and efficiency of such nested quadrature-based sparse grids on parameterized boundary and initial value problems in multiple dimensions.