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Rigorous estimates for the relegation algorithm

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Abstract

We revisit the relegation algorithm by Deprit et al. (Celest. Mech. Dyn. Astron. 79:157–182, 2001) in the light of the rigorous Nekhoroshev’s like theory. This relatively recent algorithm is nowadays widely used for implementing closed form analytic perturbation theories, as it generalises the classical Birkhoff normalisation algorithm. The algorithm, here briefly explained by means of Lie transformations, has been so far introduced and used in a formal way, i.e. without providing any rigorous convergence or asymptotic estimates. The overall aim of this paper is to find such quantitative estimates and to show how the results about stability over exponentially long times can be recovered in a simple and effective way, at least in the non-resonant case.

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Notes

  1. Precisely, \(\mathcal {G}_{\varrho }=\big \{z\in \mathbb {C}^{n_1}:\max _{1\le j\le n_1}|z_j|<\varrho \big \}\), \(\mathbb {T}^{n_1}_{\sigma }=\big \{q\in \mathbb {C}^{n_1}:\mathrm{Re}\, q_j\in \mathbb {T},\ \max _{1\le j\le n_1}|\mathrm{Im}\, q_j|<\sigma \big \}\,\), \(\mathcal {B}_R=\{z\in \mathbb {C}^{2n_2}: \max _{1\le j\le 2n_2} |z_j|<R\,\}\).

  2. As reported in Giorgilli (2003) (pag. 86, footnote 2), choosing the parameter K by asking \(e^{-K \sigma }\sim \varepsilon \) is not really convenient. As it will be evident from the optimisation of the parameters in the proof of Theorem 1, the best choice is \(K\sim 1/\sigma \).

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Acknowledgments

We warmly thank A. Giorgilli for helpful discussions and useful comments. The work of M. S. have been partially supported by the research program “Teorie geometriche e analitiche dei sistemi Hamiltoniani in dimensioni finite e infinite”, PRIN 2010JJ4KPA 009, financed by MIUR.

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Sansottera, M., Ceccaroni, M. Rigorous estimates for the relegation algorithm. Celest Mech Dyn Astr 127, 1–18 (2017). https://doi.org/10.1007/s10569-016-9711-2

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