Uniformization and Constructive Analytic Continuation of Taylor Series

TL;DR

This paper introduces a novel uniformization-based method for reconstructing functions from truncated power series, significantly improving accuracy especially near singularities, with applications to nonlinear ODEs and special functions.

Contribution

It develops a new uniformization technique for Riemann surfaces and a constructive regularization method, advancing the accuracy of function reconstruction from partial series data.

Findings

Dramatic accuracy improvements over existing methods.

Effective uniformization for solutions of nonlinear ODEs.

New techniques for singularity regularization and detection.

Abstract

We analyze the problem of global reconstruction of functions as accurately as possible, based on partial information in the form of a truncated power series at some point, and additional analyticity properties. This situation occurs frequently in applications. The question of the optimal procedure was open, and we formulate it as a well-posed mathematical problem. Its solution leads to a practical method which provides dramatic accuracy improvements over existing techniques. Our procedure is based on uniformization of Riemann surfaces. As an application, we show that our procedure can be implemented for solutions of a wide class of nonlinear ODEs. We find a new uniformization method, which we use to construct the uniformizing maps needed for special functions, including solution of the Painlev'e equations P_I-P_V. We also introduce a new rigorous and constructive method of regularization, elimination of singularities whose position and type are known. If these are unknown, the same procedure enables a highly sensitive resonance method to determine the position and type of a singularity. In applications where less explicit information is available about the Riemann surface, our approach and techniques lead to new approximate, but still much more precise reconstruction methods than existing ones, especially in the vicinity of singularities, which are the points of greatest interest.