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Olsson.wl : a Mathematica package for the computation of linear transformations of multivariable hypergeometric functions

B. Ananthanarayan, Souvik Bera, S. Friot, Tanay Pathak

TL;DR

The paper introduces Olsson.wl, a Mathematica package that automates the derivation of linear transformations and analytic continuations for multivariable hypergeometric functions by leveraging Olsson's method, which builds higher-variable continuations from transformations of $_2F_1$. It also presents ROC2.wl, a companion tool that automatically determines regions of convergence for double hypergeometric series using Horn-type theorems, enabling robust numerical evaluation. The authors demonstrate new analytic continuations for functions such as $F_2$, $F_4$, and Horn series $H_1$ and $H_5$, and validate these against known results and numerical checks, with extensions to Lauricella cases discussed. Collectively, these tools automate a broad class of transformations and convergence analyses, providing a practical framework for analytic and numerical work on multivariable hypergeometric functions in physics and mathematics, with plans to extend to higher-variable cases and to address multivaluedness and branch-cut complexities.

Abstract

We present the Olsson$.$wl Mathematica package which aims to find linear transformations for some classes of multivariable hypergeometric functions. It is based on a well-known method developed by P. O. M. Olsson in J. Math. Phys. 5, 420 (1964) in order to derive the analytic continuations of the Appell $F_1$ double hypergeometric series from the linear transformations of the Gauss $_2F_1$ hypergeometric function. We provide a brief description of Olsson's method and demonstrate the commands of the package, along with examples. We also provide a companion package, called ROC2$.$wl and dedicated to the derivation of the regions of convergence of double hypergeometric series. This package can be used independently of Olsson$.$wl.

Olsson.wl : a Mathematica package for the computation of linear transformations of multivariable hypergeometric functions

TL;DR

The paper introduces Olsson.wl, a Mathematica package that automates the derivation of linear transformations and analytic continuations for multivariable hypergeometric functions by leveraging Olsson's method, which builds higher-variable continuations from transformations of . It also presents ROC2.wl, a companion tool that automatically determines regions of convergence for double hypergeometric series using Horn-type theorems, enabling robust numerical evaluation. The authors demonstrate new analytic continuations for functions such as , , and Horn series and , and validate these against known results and numerical checks, with extensions to Lauricella cases discussed. Collectively, these tools automate a broad class of transformations and convergence analyses, providing a practical framework for analytic and numerical work on multivariable hypergeometric functions in physics and mathematics, with plans to extend to higher-variable cases and to address multivaluedness and branch-cut complexities.

Abstract

We present the Olssonwl Mathematica package which aims to find linear transformations for some classes of multivariable hypergeometric functions. It is based on a well-known method developed by P. O. M. Olsson in J. Math. Phys. 5, 420 (1964) in order to derive the analytic continuations of the Appell double hypergeometric series from the linear transformations of the Gauss hypergeometric function. We provide a brief description of Olsson's method and demonstrate the commands of the package, along with examples. We also provide a companion package, called ROC2wl and dedicated to the derivation of the regions of convergence of double hypergeometric series. This package can be used independently of Olssonwl.
Paper Structure (25 sections, 2 theorems, 35 equations, 1 figure)

This paper contains 25 sections, 2 theorems, 35 equations, 1 figure.

Key Result

Theorem 1

The region of convergence of a hypergeometric series is independent of its parameters, exceptional parameter values being excluded.

Figures (1)

  • Figure 1: Flow chart of the ROC2 algorithm

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2