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SVD algorithms to approximate spectra of dynamical systems

Author

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  • Dieci, L.
  • Elia, C.

Abstract

In this work we consider algorithms based on the singular value decomposition (SVD) to approximate Lyapunov and exponential dichotomy spectra of dynamical systems. We review existing contributions, and propose new algorithms of the continuous SVD method. We present implementation details for the continuous SVD method, and illustrate on several examples the behavior of continuous (and also discrete) SVD method. This paper is the companion paper of [L. Dieci, C. Elia, The singular value decomposition to approximate spectra of dynamical systems. Theoretical aspects, J. Diff. Equat., in press].

Suggested Citation

  • Dieci, L. & Elia, C., 2008. "SVD algorithms to approximate spectra of dynamical systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(4), pages 1235-1254.
    • Handle: RePEc:eee:matcom:v:79:y:2008:i:4:p:1235-1254
    DOI: 10.1016/j.matcom.2008.03.005
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    Cited by:

    1. Zhou, Shuang & Wang, Xingyuan, 2020. "Simple estimation method for the second-largest Lyapunov exponent of chaotic differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    2. Klaus Neusser, 2018. "The New Keynesian Model with Stochastically Varying Policies," Diskussionsschriften dp1801, Universitaet Bern, Departement Volkswirtschaft.
    3. Neusser, Klaus, 2019. "Time–varying rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.

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