On generating Sobolev orthogonal polynomials

TL;DR

This paper introduces new numerical methods for generating Sobolev orthogonal polynomials by reformulating the problem as a Hessenberg inverse eigenvalue problem and leveraging Krylov subspace techniques.

Contribution

It presents two novel algorithms for computing Sobolev orthogonal polynomials through a matrix inverse eigenvalue problem approach.

Findings

Successfully reformulated polynomial generation as a Hessenberg inverse eigenvalue problem

Connected the problem to Krylov subspaces and spectral information

Proposed two new numerical procedures for computation

Abstract

Sobolev orthogonal polynomials are polynomials orthogonal with respect to a Sobolev inner product, an inner product in which derivatives of the polynomials appear. They satisfy a long recurrence relation that can be represented by a Hessenberg matrix. The problem of generating a finite sequence of Sobolev orthogonal polynomials can be reformulated as a matrix problem. That is, a Hessenberg inverse eigenvalue problem, where the Hessenberg matrix of recurrences is generated from certain known spectral information. Via the connection to Krylov subspaces we show that the required spectral information is the Jordan matrix containing the eigenvalues of the Hessenberg matrix and the normalized first entries of its eigenvectors. Using a suitable quadrature rule the Sobolev inner product is discretized and the resulting quadrature nodes form the Jordan matrix and associated quadrature weights are the first entries of the eigenvectors. We propose two new numerical procedures to compute Sobolev orthonormal polynomials based on solving the equivalent Hessenberg inverse eigenvalue problem.