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Lyapunov Exponents of Linear Cocycles = Continuity via Large Deviations /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Lyapunov Exponents of Linear Cocycles/ by Pedro Duarte, Silvius Klein.
其他題名:	Continuity via Large Deviations /
作者:	Duarte, Pedro.
其他作者:	Klein, Silvius.
面頁冊數:	XIII, 263 p. 4 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Dynamics. -
電子資源:	https://doi.org/10.2991/978-94-6239-124-6
ISBN:	9789462391246

Lyapunov Exponents of Linear Cocycles = Continuity via Large Deviations /
Duarte, Pedro.

Lyapunov Exponents of Linear CocyclesContinuity via Large Deviations /[electronic resource] :by Pedro Duarte, Silvius Klein. - 1st ed. 2016. - XIII, 263 p. 4 illus.online resource. - Atlantis Studies in Dynamical Systems ;3. - Atlantis Studies in Dynamical Systems ;5.

Introduction -- Estimates on Grassmann Manifolds -- Abstract Continuity of Lyapunov Exponents -- The Oseledets Filtration and Decomposition -- Large Deviations for Random Cocycles -- Large Deviations for Quasi-Periodic Cocycles -- Further Related Problems.

The aim of this monograph is to present a general method of proving continuity of Lyapunov exponents of linear cocycles. The method uses an inductive procedure based on a general, geometric version of the Avalanche Principle. The main assumption required by this method is the availability of appropriate large deviation type estimates for quantities related to the iterates of the base and fiber dynamics associated with the linear cocycle. We establish such estimates for various models of random and quasi-periodic cocycles. Our method has its origins in a paper of M. Goldstein and W. Schlag. Our present work expands upon their approach in both depth and breadth. We conclude this monograph with a list of related open problems, some of which may be treated using a similar approach.

ISBN: 9789462391246

Standard No.: 10.2991/978-94-6239-124-6doiSubjects--Topical Terms:

592238
Dynamics.

LC Class. No.: QA313

Dewey Class. No.: 515.39

Lyapunov Exponents of Linear Cocycles = Continuity via Large Deviations /
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