Kaplan–Yorke conjecture

In applied mathematics, the Kaplan–Yorke conjecture concerns the dimension of an attractor, using Lyapunov exponents.[1][2] By arranging the Lyapunov exponents in order from largest to smallest λ 1 λ 2 λ n {\displaystyle \lambda _{1}\geq \lambda _{2}\geq \dots \geq \lambda _{n}} , let j be the largest index for which

i = 1 j λ i 0 {\displaystyle \sum _{i=1}^{j}\lambda _{i}\geqslant 0}

and

i = 1 j + 1 λ i < 0. {\displaystyle \sum _{i=1}^{j+1}\lambda _{i}<0.}

Then the conjecture is that the dimension of the attractor is

D = j + i = 1 j λ i | λ j + 1 | . {\displaystyle D=j+{\frac {\sum _{i=1}^{j}\lambda _{i}}{|\lambda _{j+1}|}}.}

This idea is used for the definition of the Lyapunov dimension.[3]

Examples

Especially for chaotic systems, the Kaplan–Yorke conjecture is a useful tool in order to estimate the fractal dimension and the Hausdorff dimension of the corresponding attractor.[4][3]

  • The Hénon map with parameters a = 1.4 and b = 0.3 has the ordered Lyapunov exponents λ 1 = 0.603 {\displaystyle \lambda _{1}=0.603} and λ 2 = 2.34 {\displaystyle \lambda _{2}=-2.34} . In this case, we find j = 1 and the dimension formula reduces to
D = j + λ 1 | λ 2 | = 1 + 0.603 | 2.34 | = 1.26. {\displaystyle D=j+{\frac {\lambda _{1}}{|\lambda _{2}|}}=1+{\frac {0.603}{|{-2.34}|}}=1.26.}
  • The Lorenz system shows chaotic behavior at the parameter values σ = 16 {\displaystyle \sigma =16} , ρ = 45.92 {\displaystyle \rho =45.92} and β = 4.0 {\displaystyle \beta =4.0} . The resulting Lyapunov exponents are {2.16, 0.00, −32.4}. Noting that j = 2, we find
D = 2 + 2.16 + 0.00 | 32.4 | = 2.07. {\displaystyle D=2+{\frac {2.16+0.00}{|-32.4|}}=2.07.}

References

  1. ^ Kaplan, J.; Yorke, J. (1979). "Chaotic behavior of multidimensional difference equations" (PDF). In Peitgen, H. O.; Walther, H. O. (eds.). Functional Differential Equations and the Approximation of Fixed Points. Lecture Notes in Mathematics. Vol. 730. Berlin: Springer. pp. 204–227. ISBN 978-0-387-09518-9. MR 0547989.
  2. ^ Frederickson, P.; Kaplan, J.; Yorke, E.; Yorke, J. (1983). "The Lyapunov Dimension of Strange Attractors". J. Diff. Eqs. 49 (2): 185–207. Bibcode:1983JDE....49..185F. doi:10.1016/0022-0396(83)90011-6.
  3. ^ a b Kuznetsov, Nikolay; Reitmann, Volker (2020). Attractor Dimension Estimates for Dynamical Systems: Theory and Computation. Cham: Springer.
  4. ^ Wolf, A.; Swift, A.; Jack, B.; Swinney, H. L.; Vastano, J. A. (1985). "Determining Lyapunov Exponents from a Time Series". Physica D. 16 (3): 285–317. Bibcode:1985PhyD...16..285W. CiteSeerX 10.1.1.152.3162. doi:10.1016/0167-2789(85)90011-9. S2CID 14411384.