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Weak backward error analysis for stochastic Hamiltonian Systems

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Abstract

This paper presents a study of the approximation error corresponding to a symplectic scheme of weak order one for a stochastic autonomous Hamiltonian system. A backward error analysis is done at the level of the Kolmogorov equation associated with the initial stochastic Hamiltonian system. An expansion of the weak error and expansions of the ergodic averages and of the invariant measures associated with the numerical scheme are obtained in terms of powers of the discretization step size and the solutions of the modified Kolmogorov equation.

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References

  1. Burrage, K., Burrage, P.: Low rank Runge–Kutta methods, symplecticity and stochastic Hamiltonian problems with additive noise. J. Comput. Appl. Math. 236, 3920–3930 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Milstein, G.N., Repin, Y.M., Tretyakov, M.V.: Symplectic integration of Hamiltonian systems with additive noise. SIAM J. Numer. Anal. 39(6), 2066–2088 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Milstein, G.N., Repin, Y.M., Tretyakov, M.V.: Numerical methods for stochastic systems preserving symplectic structure. SIAM J. Numer. Anal. 40(4), 1583–1604 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  4. Milstein, G.N., Tretyakov, M.V.: Stochastic Numerics for Mathematical Physics. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  5. Hong, J., Sun, L., Wang, X.: High order conformal symplectic and ergodic schemes for the stochastic Langevin equation via generating functions. SIAM J. Numer. Anal. 55(6), 3006–3029 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  6. Sun, L., Wang, L.: Stochastic symplectic methods based on the Pade approximations for linear stochastic Hamiltonian systems. J. Comput. Appl. Math. 311, 439–456 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  7. Anton, C., Deng, J., Wong, Y.: Weak symplectic schemes for stochastic Hamiltonian equations. Electron. Trans. Numer. Anal. 43, 1–20 (2014)

    MathSciNet  MATH  Google Scholar 

  8. Deng, J., Anton, C., Wong, Y.: High-order symplectic schemes for stochastic Hamiltonian systems. Commun. Comput. Phys. 16(1), 169–200 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Sanz-Serna, J.M., Calvo, M.: Numerical Hamiltonian problems. Chapman and Hall, London (1994)

    Book  MATH  Google Scholar 

  10. Reich, S.: Backward error analysis for numerical integrators. SIAM J. Numer. Anal. 36, 1549–1570 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  11. Hairer, E., Lubich, C., Wanner, G.: Geometric Numerical Integration: Structure-Preserving Algorithms for Ordinary Differential Equations. Springer, Berlin (2006)

    MATH  Google Scholar 

  12. Debussche, A., Faou, E.: Weak backward error analysis for SDEs. SIAM J. Numer. Anal. 50(3), 1735–1752 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Shardlow, T.: Modified equations for stochastic differential equations. BIT Numer. Math. 46(1), 111–125 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  14. Abdulle, A., Cohen, D., Vilmart, G., Zygalakis, K.: High weak order methods for stochastic differential equations based on modified equations. SIAM J. Sci. Comput. 34(3), 1800–1823 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Zygalakis, K.: On the existence and applications of modified equations for stochastic differential equations. SIAM J. Sci. Comput. 33(1), 102–130 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Abdulle, A., Vilmart, G., Zygalakis, K.: Long time accuracy of Lie–Trotter splitting methods for Langevin dynamics. SIAM J. Sci. Comput. 53(1), 1–16 (2015)

    MathSciNet  MATH  Google Scholar 

  17. Kopec, M.: Weak backward error analysis for Langevin process. BIT Numer. Math. 55(4), 1057–1103 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Kopec, M.: Weak backward error analysis for overdamped Langevin processes. IMA J. Numer. Anal. 35(2), 583–614 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang, L., Hong, J., Sun, L.: Modified equations for weakly convergent stochastic symplectic schemes via their generating functions. BIT Numer. Math. 56, 1131–1162 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Milstein, G.N., Tretyakov, M.V.: Quasi-symplectic methods for Langevin-type equations. IMA J. Numer. Anal. 23, 593–626 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  21. Talay, D.: Second order discretization schemes of stochastic differential systems for the computation of the invariant law. Stoch. Stoch. Rep. 29(1), 13–36 (1990)

    Article  MATH  Google Scholar 

  22. Talay, D.: Stochastic Hamiltonian systems: exponential convergence to the invariant measure, and discretization by the implicit Euler scheme. Markov Proc. Relat. Fields 8, 1–36 (2002)

    MathSciNet  MATH  Google Scholar 

  23. Mattingly, J., Stuart, A., Higham, D.J.: Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise. Stoch. Process. Appl. 2(101), 185–232 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  24. Talay, D., Tubaro, L.: Expansion of the global error for numerical schemes solving stochastic differential equations. Stoch. Anal. Appl. 8(4), 483–509 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  25. Anton, C., Wong, Y., Deng, J.: On global error of symplectic schemes for stochastic Hamiltonian systems. Int. J. Numer. Anal. Model. Ser. B 4(1), 80–93 (2013)

    MathSciNet  MATH  Google Scholar 

  26. Mattingly, J., Stuart, A., Tretyakov, M.: Convergence of numerical time-averaging and stationary measures via Poisson equations. SIAM J. Numer. Anal. 28(2), 552–577 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Pardoux, E., Veretennikov, Y.: On the Poisson equation and diffusion approximation. Ann. Probab. 29(3), 1061–1085 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  28. Meyn, S.P., Tweedie, R.: Markov Chains and Stochastic Stability. Springer, London (1993)

    Book  MATH  Google Scholar 

  29. Hutzenthaler, M., Jentzen, A., Kloeden, P.: Strong and weak divergence in finite time of Euler’s method for stochastic differential equations with nonglobally Lipschitz continuous coefficients. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 467, 1563–1576 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hutzenthaler, M., Jentzen, A., Kloeden, P.: Strong convergence of an explicit numerical method for SDEs with non-globally Lipschitz continuous coefficients. Ann. Appl. Probab. 22(4), 1611–1641 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Tretyakov, M.V., Zhang, Z.: A fundamental mean-square convergence theorem for SDEs with locally Lipschitz coefficients and its applications. SIAM J. Numer. Anal. 51(6), 3135–3162 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  32. Mao, X., Szpruch, L.: Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients. J. Comput. App. Math. 238, 14–28 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  33. Kunita, H.: Stochastic differential equations and stochastic flows of diffeomorphisms. In: Hennequin, P. (ed.) École d’Été de Probabilités de Saint-Flour XII-1982. Lecture Notes in Mathematics, pp. 143–303. Springer, Berlin (1984)

    Chapter  Google Scholar 

  34. Hasminskii, R.Z.: Stochastic Stability of Differential Equations, 2nd edn. Springer, Berlin (2012)

    Book  Google Scholar 

  35. Zhu, C., Yin, G.: Asymptotic properties of hybrid diffusion systems. SIAM J. Control Optim. 46(4), 1155–1179 (2007)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Cristina Anton.

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Communicated by David Cohen.

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This research was supported by the NSERC grant DDG-2015-00041.

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Anton, C. Weak backward error analysis for stochastic Hamiltonian Systems. Bit Numer Math 59, 613–646 (2019). https://doi.org/10.1007/s10543-019-00747-6

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  • DOI: https://doi.org/10.1007/s10543-019-00747-6

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