2001
DOI: 10.5802/aif.1832
|View full text |Cite
|
Sign up to set email alerts
|

Exposants caractéristiques de l'algorithme de Jacobi-Perron et de la transformation associée

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
22
0

Year Published

2002
2002
2024
2024

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 27 publications
(23 citation statements)
references
References 1 publication
1
22
0
Order By: Relevance
“…For more details, see for instance [13]. The dynamical system defined by ~ on the unit square has been much studied [40], in particular its invariant measures and its unique invariant ergodic probability measure equivalent to Lebesgue measure [14], [15] (generalization of the classical Gauss measure) .…”
mentioning
confidence: 99%
“…For more details, see for instance [13]. The dynamical system defined by ~ on the unit square has been much studied [40], in particular its invariant measures and its unique invariant ergodic probability measure equivalent to Lebesgue measure [14], [15] (generalization of the classical Gauss measure) .…”
mentioning
confidence: 99%
“…For a proof of the strong convergence of two-dimensional Brun's algorithm for the multiplicative case (cf. Remark 1.1), we refer to BroiseAlamichel and Guivarc'h [1]. For future use, we axiomatize our criterion for algorithms we consider to be strongly convergent.…”
Section: Statements Of Resultsmentioning
confidence: 99%
“…However, this measure is not known explicitly for Jacobi-Perron (the density of the measure is shown to be a piecewise analytical function in [21]). For a thorough study of the Lyapounov exponents of the Jacobi-Perron algorithm, see [22]. Nevertheless, as underlined in [20], concerning the class of so-called vectorial algorithms to which Jacobi-Perron algorithm belongs, "All continued fraction algorithms which have been proposed since the beginning (Jacobi, 1868), and up to about 1970 belong to this class.…”
Section: Toward Multidimensional Expansionsmentioning
confidence: 99%