Polynomial interpolation--regression on the sphere

TL;DR

This paper presents a novel polynomial interpolation-regression method on the sphere that combines data interpolation with residual minimization, ensuring uniqueness and stability under certain conditions, and demonstrates effectiveness through numerical experiments.

Contribution

It introduces a new spherical polynomial approximation operator that unifies interpolation and least squares residual minimization, with analysis of symmetry and stability properties.

Findings

Unique spherical polynomial approximant under rank conditions.

Decomposition into even and odd components for antipodal symmetric nodes.

Explicit spectral condition number for the associated KKT matrix.

Abstract

We introduce an interpolation--regression operator for polynomial approximation on the unit sphere $\mathbb{S}^2$ from discrete samples. The approximant is a spherical polynomial of degree $r$ which interpolates the data on a prescribed subset of nodes and uses the remaining sampling nodes to minimize the residual in a least squares sense. Under natural rank assumptions on the associated Vandermonde matrices, the approximant is unique and is characterized by an orthogonality condition with respect to the discrete inner product on the sampling set. We then focus on the case in which the sampling and interpolation nodes are antipodally symmetric. In this setting, when the polynomial is expressed in real spherical harmonics, the constrained problem can be decomposed into independent even and odd components. In the same framework, we prove equivariance under the antipodal map and, more generally, under orthogonal transformations preserving the node sets. We also consider spherical designs. In this case, the normal matrix is a scalar matrix. Consequently, the spectral condition number of the associated KKT matrix can be written explicitly. Numerical experiments in both antipodal and non-antipodal settings illustrate the effectiveness of the proposed method.