Skip to main content
Log in

Strong convergence rate of principle of averaging for jump-diffusion processes

  • Research Article
  • Published:
Frontiers of Mathematics in China Aims and scope Submit manuscript

Abstract

We study jump-diffusion processes with two well-separated time scales. It is proved that the rate of strong convergence to the averaged effective dynamics is of order O(ɛ 1/2), where ɛ ≪ 1 is the parameter measuring the disparity of the time scales in the system. The convergence rate is shown to be optimal through examples. The result sheds light on the designing of efficient numerical methods for multiscale stochastic dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. E W, Engquist B. The heterogeneous multiscale methods. Commun Math Sci, 2003, 1(1): 87–133

    MathSciNet  MATH  Google Scholar 

  2. E W, Liu D, Vanden-Eijnden E. Analysis of multiscale methods for stochastic differential equations. Commun Pure Appl Math, 2005, 58(11): 1544–1585

    Article  MathSciNet  MATH  Google Scholar 

  3. Freidlin M I, Wentzell A D. Random Perturbations of Dynamical Systems. 2nd ed. New York: Springer-Verlag, 1998

    Book  MATH  Google Scholar 

  4. Givon D. Strong convergence rate for two-time-scale jump-diffusion stochastic differential systems. SIAM Mul Mod Simu, 2007, 6: 577–594

    Article  MathSciNet  MATH  Google Scholar 

  5. Givon D, Kevrekidis I G, Kupferman R. Strong convergence of projective integration schemes for singularly perturbed stochastic differential systems. Commun Math Sci, 2006, 4(4): 707–729

    MathSciNet  MATH  Google Scholar 

  6. Khasminskii R Z. Principle of averaging for parabolic and elliptic differential equations and for Markov processes with small diffusion. Theory Probab Appl, 1963, 8: 1–21

    Article  Google Scholar 

  7. Khasminskii R Z. Stochastic Stability of Differential Equations. Alphen aan den Rijn: Sijthoff & Noordhoff, 1980

  8. Khasminskii R Z, Yin G. On averaging principles: An asymptotic expansion approach. SIAM J Math Anal, 2004, 35(6): 1534–1560, 2004

    Article  MathSciNet  MATH  Google Scholar 

  9. Kifer Y. Stochastic versions of Anosov and Neistadt theorems on averaging. Stoch Dyn, 2001, 1(1): 1–21

    Article  MathSciNet  MATH  Google Scholar 

  10. Kushner H J. Weak Convergence Methods and Singularly Perturbed Stochastic Control and Filtering Problems, 3, Systems & Control: Foundations & Applications. Boston: Birkhäuser, 1 1990

    Book  Google Scholar 

  11. Liu D. Strong convergence of principle of averaging for multiscale stochastic dynamical systems. Commun Math Sci, 2010, 8: 999–1020

    MathSciNet  MATH  Google Scholar 

  12. Liu D. Analysis of multiscale methods for stochastic dynamical systems with multiple time scales. SIAM Mul Mod Simu, 2010, 8: 944–964

    Article  MATH  Google Scholar 

  13. Menaldi J L, Robin M. Invariant measure for diffusions with jumps. Appl Math Optim, 1999, 40: 105–140

    Article  MathSciNet  MATH  Google Scholar 

  14. Meyn S P, Tweedie R L. Stability of Markovian processes, I. Adv Appl Probab, 1992, 24(3): 542–574

    Article  MathSciNet  MATH  Google Scholar 

  15. Meyn S P, Tweedie R L. Stability of Markovian processes, II. Adv Appl Probab, 1993, 25(3): 487–517

    Article  MathSciNet  MATH  Google Scholar 

  16. Meyn S P, Tweedie R L. Stability of Markovian processes, III. Adv Appl Probab, 1993, 25(3): 518–548

    Article  MathSciNet  MATH  Google Scholar 

  17. Vanden-Eijnden E. Numerical techniques for multi-scale dynamical systems with stochastic effects. Commun Math Sci, 2003, 1: 377–384

    MathSciNet  Google Scholar 

  18. Veretennikov A Yu. On an averaging principle for systems of stochastic differential equations. Math Sbornik, 1990, 181(2): 256–268

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Di Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, D. Strong convergence rate of principle of averaging for jump-diffusion processes. Front. Math. China 7, 305–320 (2012). https://doi.org/10.1007/s11464-012-0193-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11464-012-0193-6

Keywords

MSC

Navigation