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A step size control algorithm for the weak approximation of stochastic differential equations

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Abstract

A vriable step size control algorithm for the weak approximation of stochastic differential equations is introduced. The algorithm is based on embedded Runge–Kutta methods which yield two approximations of different orders with a negligible additional computational effort. The difference of these two approximations is used as an estimator for the local error of the less precise approximation. Some numerical results are presented to illustrate the effectiveness of the introduced step size control method.

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References

  1. Burrage, K., Burrage, P.M.: A variable stepsize implementation for stochastic differential equations. SIAM J. Sci. Comput. 24(3), 848–864 (2002)

    Article  MATH  Google Scholar 

  2. Burrage, K., Burrage, P.M.: Order conditions of stochastic Runge–Kutta methods by B-series. SIAM J. Numer. Anal. 38(5), 1626–1646 (2000)

    Article  MATH  Google Scholar 

  3. Butcher, J.C.: Numerical Methods for Ordinary Differential Equations. Wiley, Chichester (England) (2003)

    MATH  Google Scholar 

  4. Gaines, J.G., Lyons T.J.: Variable step size control in the numerical solution of stochastic differential equations. SIAM J. Appl. Math. 57(5), 1455–1484 (1997)

    Article  MATH  Google Scholar 

  5. Gänssler, P., Stute, W.: Wahrscheinlichkeitstheorie. Springer, Berlin Heidelberg New York (1977)

    MATH  Google Scholar 

  6. Hairer, E., Nørsett, S.P., Wanner, G.: Solving Ordinary Differential Equations I. Springer, Berlin Heidelberg New York (1993)

    MATH  Google Scholar 

  7. Hofmann, N.: Beiträge zur schwachen Approximation stochastischer Differentialgleichungen. Ph.D. thesis, Humboldt-Universität zu Berlin (1995)

  8. Kloeden, P.E., Platen, E.: Numerical Solution of Stochastic Differential Equations. Springer, Berlin Heidelberg New York (1999)

    Google Scholar 

  9. Kloeden, P.E., Platen, E., Schurz, H.: Numerical Solution of SDE Through Computer Experiments. Springer, Berlin Heidelberg New York (1994)

    MATH  Google Scholar 

  10. Komori, Y., Mitsui, T., Sugiura, H.: Rooted tree analysis of the order conditions of ROW-type scheme for stochastic differential equations. BIT 37(1), 43–66 (1997)

    Article  MATH  Google Scholar 

  11. Lamba, H.: An adaptive timestepping algorithm for stochastic differential equations. J. Comput. Appl. Math. 161(2), 417–430 (2003)

    Article  MATH  Google Scholar 

  12. Mauthner, S.: Schrittweitensteuerung bei der numerischen Lösung stochastischer Differentialgleichungen. Düsseldorf. VDI Verlag GmbH, Reihe 10, Bd. 578 (1999)

  13. Rößler, A.: An adaptive discretization algorithm for the weak approximation of stochastic differential equations. Proc. Appl. Math. Mech. 4(1), 19–22 (2004)

    Article  Google Scholar 

  14. Rößler, A.: Rooted tree analysis for order conditions of stochastic Runge–Kutta methods for the weak approximation of stochastic differential equations. Stoch. Anal. Appl. 24(1), 97–134 (2006)

    Article  MATH  Google Scholar 

  15. Rößler, A.: Runge–Kutta methods for Itô stochastic differential equations with scalar noise. BIT 46(1), 97–110 (2006)

    Article  MATH  Google Scholar 

  16. Rößler, A.: Runge–Kutta methods for the numerical solution of stochastic differential equations. Ph.D. thesis, Darmstadt University of Technology. Aachen. Shaker Verlag (2003)

  17. Szepessy, A., Tempone, R., Zouraris, G.: Adaptive weak approximation of stochastic differential equations. Commun. Pure Appl. Math. 54(10), 1169–1214 (2001)

    Article  MATH  Google Scholar 

  18. Tocino, A., Vigo-Aguiar, J.: Weak second order conditions for stochastic Runge–Kutta methods. SIAM J. Sci. Comput. 24(2), 507–523 (2002)

    Article  MATH  Google Scholar 

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Correspondence to Dominique Küpper.

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Küpper, D., Lehn, J. & Rößler, A. A step size control algorithm for the weak approximation of stochastic differential equations. Numer Algor 44, 335–346 (2007). https://doi.org/10.1007/s11075-007-9108-0

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  • DOI: https://doi.org/10.1007/s11075-007-9108-0

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