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QR-based methods for computing Lyapunov exponents of stochastic differential equations. (English) Zbl 1246.65011

Summary: Lyapunov exponents (LEs) play a central role in the study of stability properties and asymptotic behavior of dynamical systems. However, explicit formulas for them can be derived for very few systems, therefore numerical methods are required. Such is the case of random dynamical systems described by stochastic differential equations (SDEs), for which there have been reported just a few numerical methods. The first attempts were restricted to linear equations, which have obvious limitations from the applications point of view. A more successful approach deals with nonlinear equation defined over manifolds but is effective for the computation of only the top LE.
In this paper, two numerical methods for the efficient computation of all LEs of nonlinear SDEs are introduced. They are, essentially, a generalization to the stochastic case of the well known QR-based methods developed for ordinary differential equations. Specifically, a discrete and a continuous QR method are derived by combining the basic ideas of the deterministic QR methods with the classical rules of the differential calculus for the Stratanovich representation of SDEs.
Additionally, bounds for the approximation errors are given and the performance of the methods is illustrated by means of numerical simulations.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
60H35 Computational methods for stochastic equations (aspects of stochastic analysis)
34D08 Characteristic and Lyapunov exponents of ordinary differential equations
37M25 Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.)
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