Convergence of Multilevel Stationary Gaussian Quasi-Interpolation
This work addresses error analysis for multilevel quasi-interpolation with analytic basis functions, an incremental advance over existing finite-smoothness approaches.
The paper presents a new multilevel quasi-interpolation algorithm for smooth periodic functions using scaled Gaussians, providing first error estimates for analytic basis functions. Numerical results show faster convergence than theoretically proven.
In this paper we present a new multilevel quasi-interpolation algorithm for smooth periodic functions using scaled Gaussians as basis functions. Recent research in this area has focussed upon implementations using basis function with finite smoothness. In this paper we deliver a first error estimates for the multilevel algorithm using analytic basis functions. The estimate has two parts, one involving the convergence of a low degree polynomial truncation term and one involving the control of the remainder of the truncation as the algorithm proceeds. Thus, numerically one observes a convergent scheme. Numerical results suggest that the scheme converges much faster than the theory shows.