High-Order Parametrization of the Hypergeometric-Meijer Approximants
Abouzeid M. Shalaby

TL;DR
This paper develops a high-order parametrization method for hypergeometric approximants, enabling efficient analytical continuation of divergent series and incorporating non-perturbative data to improve convergence in quantum and statistical models.
Contribution
It introduces an extended algorithm for high-order hypergeometric approximants that efficiently integrates non-perturbative information, enhancing convergence and accuracy in complex series analysis.
Findings
Accurate ground state energy calculations for anharmonic oscillators.
Effective incorporation of non-perturbative data accelerates convergence.
Precise predictions of critical exponents and temperatures in lattice models.
Abstract
In previous articles, we showed that, based on large-order asymptotic behavior, one can approximate a divergent series via the parametrization of a specific hypergeometric approximant. The analytical continuation is then carried out through a Mellin-Barnes integral representation of the hypergeometric approximant or equivalently using an equivalent form of the Meijer G-Function. The parametrization process involves the solution of a non-linear set of coupled equations which is hard to achieve (might be impossible) for high orders using normal PCs. In this work, we extend the approximation algorithm to accommodate any order (high or low) of the given series in a short time. The extension also allows us to employ non-perturbative information like strong-coupling and large-order asymptotic data which are always used to accelerate the convergence. We applied the algorithm for different…
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Taxonomy
TopicsMathematical functions and polynomials · Scientific Research and Discoveries
