Research article Special Issues

Numerical contractivity preserving implicit balanced Milstein-type schemes for SDEs with non-global Lipschitz coefficients

  • Received: 19 September 2023 Revised: 08 December 2023 Accepted: 18 December 2023 Published: 29 December 2023
  • MSC : 60H10, 60H35, 65C30

  • Stability analysis, which was investigated in this paper, is one of the main issues related to numerical analysis for stochastic dynamical systems (SDS) and has the same important significance as the convergence one. To this end, we introduced the concept of $ p $-th moment stability for the $ n $-dimensional nonlinear stochastic differential equations (SDEs). Specifically, if $ p = 2 $ and the $ p $-th moment stability constant $ \bar{K} < 0 $, we speak of strict mean square contractivity. The present paper put the emphasis on systematic analysis of the numerical mean square contractivity of two kinds of implicit balanced Milstein-type schemes, e.g., the drift implicit balanced Milstein (DIBM) scheme and the semi-implicit balanced Milstein (SIBM) scheme (or double-implicit balanced Milstein scheme), for SDEs with non-global Lipschitz coefficients. The requirement in this paper allowed the drift coefficient $ f(x) $ to satisfy a one-sided Lipschitz condition, while the diffusion coefficient $ g(x) $ and the diffusion function $ L^{1}g(x) $ are globally Lipschitz continuous, which includes the well-known stochastic Ginzburg Landau equation as an example. It was proved that both of the mentioned schemes can well preserve the numerical counterpart of the mean square contractivity of the underlying SDEs under appropriate conditions. These outcomes indicate under what conditions initial perturbations are under control and, thus, have no significant impact on numerical dynamic behavior during the numerical integration process. Finally, numerical experiments intuitively illustrated the theoretical results.

    Citation: Jinran Yao, Zhengwei Yin. Numerical contractivity preserving implicit balanced Milstein-type schemes for SDEs with non-global Lipschitz coefficients[J]. AIMS Mathematics, 2024, 9(2): 2766-2780. doi: 10.3934/math.2024137

    Related Papers:

  • Stability analysis, which was investigated in this paper, is one of the main issues related to numerical analysis for stochastic dynamical systems (SDS) and has the same important significance as the convergence one. To this end, we introduced the concept of $ p $-th moment stability for the $ n $-dimensional nonlinear stochastic differential equations (SDEs). Specifically, if $ p = 2 $ and the $ p $-th moment stability constant $ \bar{K} < 0 $, we speak of strict mean square contractivity. The present paper put the emphasis on systematic analysis of the numerical mean square contractivity of two kinds of implicit balanced Milstein-type schemes, e.g., the drift implicit balanced Milstein (DIBM) scheme and the semi-implicit balanced Milstein (SIBM) scheme (or double-implicit balanced Milstein scheme), for SDEs with non-global Lipschitz coefficients. The requirement in this paper allowed the drift coefficient $ f(x) $ to satisfy a one-sided Lipschitz condition, while the diffusion coefficient $ g(x) $ and the diffusion function $ L^{1}g(x) $ are globally Lipschitz continuous, which includes the well-known stochastic Ginzburg Landau equation as an example. It was proved that both of the mentioned schemes can well preserve the numerical counterpart of the mean square contractivity of the underlying SDEs under appropriate conditions. These outcomes indicate under what conditions initial perturbations are under control and, thus, have no significant impact on numerical dynamic behavior during the numerical integration process. Finally, numerical experiments intuitively illustrated the theoretical results.



    加载中


    [1] X. R. Mao, Stochastic differential equations and applications, Chichester: Horwood Publishing Limited, 2007.
    [2] S. Yin, B. Z. Li, A stochastic differential game of low carbon technology sharing in collaborative innovation system of superior enterprises and inferior enterprises under uncertain environment, Open Math., 16 (2018), 607–622. https://doi.org/10.1515/math-2018-0056 doi: 10.1515/math-2018-0056
    [3] S. Yin, N. Zhang, Prevention schemes for future pandemic cases: Mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention, Nonlinear Dyn., 104 (2021), 2865–2900. https://doi.org/10.1007/s11071-021-06385-4 doi: 10.1007/s11071-021-06385-4
    [4] M. Hutzenthaler, A. Jentzen, P. E. Kloeden, Strong and weak divergence in finite time of Eulers method for stochastic differential equations with non-globally Lipschitz continuous coefficients, Proc. R. Soc. A, 467 (2011), 1563–1576. https://doi.org/10.1098/rspa.2010.0348 doi: 10.1098/rspa.2010.0348
    [5] A. Alfonsi, Strong order one convergence of a drift implicit Euler scheme: Application to the CIR process, Stat. Probabil. Lett., 83 (2013), 602–607. https://doi.org/10.1016/j.spl.2012.10.034 doi: 10.1016/j.spl.2012.10.034
    [6] A. Andersson, R. Kruse, Mean-square convergence of the BDF2-Maruyama and backward Euler schemes for SDE satisfying a global monotonicity condition, BIT Numer. Math., 57 (2017), 21–53. https://doi.org/10.1007/s10543-016-0624-y doi: 10.1007/s10543-016-0624-y
    [7] W. J. Beyn, E. Isaak, R. Kruse, Stochastic C-stability and B-consistency of explicit and implicit Euler-type schemes, J. Sci. Comput., 67 (2016), 955–987. https://doi.org/10.1007/s10915-015-0114-4 doi: 10.1007/s10915-015-0114-4
    [8] W. J. Beyn, E. Isaak, R. Kruse, Stochastic C-stability and B-consistency of explicit and implicit Milstein-type schemes, J. Sci. Comput., 70 (2017), 1042–1077. https://doi.org/10.1007/s10915-016-0290-x doi: 10.1007/s10915-016-0290-x
    [9] D. J. Higham, X. R. Mao, L. Szpruch, Convergence, non-negativity and stability of a new Milstein scheme with applications to finance, Discrete Cont. Dyn. B, 18 (2013), 2083–2100. https://doi.org/10.3934/dcdsb.2013.18.2083 doi: 10.3934/dcdsb.2013.18.2083
    [10] X. R. Mao, L. Szpruch, Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients, J. Comput. Appl. Math., 238 (2013), 14–28. https://doi.org/10.1016/j.cam.2012.08.015 doi: 10.1016/j.cam.2012.08.015
    [11] X. R. Mao, L. Szpruch, Strong convergence rates for backward Euler-Maruyama method for non-linear dissipative-type stochastic differential equations with super-linear diffusion coefficients, Stochastics, 85 (2013), 144–171. https://doi.org/10.1080/17442508.2011.651213 doi: 10.1080/17442508.2011.651213
    [12] A. Neuenkirch, L. Szpruch, First order strong approximations of scalar SDEs defined in a domain, Numer. Math., 128 (2014), 103–136. https://doi.org/10.1007/s00211-014-0606-4 doi: 10.1007/s00211-014-0606-4
    [13] X. J. Wang, J. Y. Wu, B. Z. Dong, Mean-square convergence rates of stochastic theta methods for SDEs under a coupled monotonicity condition, BIT Numer. Math., 60 (2020), 759–790. https://doi.org/10.1007/s10543-019-00793-0 doi: 10.1007/s10543-019-00793-0
    [14] X. F. Zong, F. K. Wu, G. P. Xu, Convergence and stability of two classes of theta-Milstein schemes for stochastic differential equations, J. Comput. Appl. Math., 336 (2018), 8–29. https://doi.org/10.1016/j.cam.2017.12.025 doi: 10.1016/j.cam.2017.12.025
    [15] X. J. Wang, S. Q. Gan, D. S. Wang, A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise, BIT Numer. Math., 52 (2012), 741–772. https://doi.org/10.1007/s10543-012-0370-8 doi: 10.1007/s10543-012-0370-8
    [16] J. F. Chassagneux, A. Jacquier, I. Mihaylov, An explicit Euler scheme with strong rate of convergence for financial SDEs with non-lipschitz coefficients, SIAM J. Financ. Math., 7 (2016), 993–1021. https://doi.org/10.1137/15M1017788 doi: 10.1137/15M1017788
    [17] W. Fang, M. B. Giles, Adaptive Euler-Maruyama method for SDEs with nonglobally Lipschitz drift, Ann. Appl. Probab., 30 (2020), 526–560. https://doi.org/10.1214/19-AAP1507 doi: 10.1214/19-AAP1507
    [18] S. Q. Gan, Y. Z. He, X. J. Wang, Tamed Runge-Kutta methods for SDEs with super-linearly growing drift and diffusion coefficients, Appl. Numer. Math., 152 (2020), 379–402. https://doi.org/10.1016/j.apnum.2019.11.014 doi: 10.1016/j.apnum.2019.11.014
    [19] Q. Guo, W. Liu, X. R. Mao, R. X. Yue, The truncated Milstein method for stochastic differential equations with commutative noise, J. Comput. Appl. Math., 338 (2018), 298–310. https://doi.org/10.1016/j.cam.2018.01.014 doi: 10.1016/j.cam.2018.01.014
    [20] M. Hutzenthaler, A. Jentzen, Numerical approximation of stochastic differential equations with non-globally Lipschitz continuous coefficients, Mem. Am. Math. Soc., 236 (2012), 1112. https://doi.org/10.1090/memo/1112 doi: 10.1090/memo/1112
    [21] M. Hutzenthaler, A. Jentzen, On a perturbation theory and on strong convergence rates for stochastic ordinary and partial differential equations with nonglobally monotone coefficients, Ann. Probab., 48 (2020), 53–93. https://doi.org/10.1214/19-AOP1345 doi: 10.1214/19-AOP1345
    [22] C. Kelly, G. J. Lord, Adaptive time-stepping strategies for nonlinear stochastic systems, IMA J. Numer. Anal., 38 (2018), 1523–1549. https://doi.org/10.1093/imanum/drx036 doi: 10.1093/imanum/drx036
    [23] C. Kumar, S. Sabanis, On Milstein approximations with varying coefficients: the case of super-linear diffusion coefficients, BIT Numer. Math., 59 (2019), 929–968. https://doi.org/10.1007/s10543-019-00756-5 doi: 10.1007/s10543-019-00756-5
    [24] X. R. Mao, The truncated Euler-Maruyama method for stochastic differential equations, J. Comput. Appl. Math., 290 (2015), 370–384. https://doi.org/10.1016/j.cam.2015.06.002 doi: 10.1016/j.cam.2015.06.002
    [25] S. Sabanis, Euler approximations with varying coefficients: the case of superlinearly growing diffusion coefficients, Ann. Appl. Probab., 26 (2016), 2083–2105. https://doi.org/10.1214/15-AAP1140 doi: 10.1214/15-AAP1140
    [26] X. J. Wang, S. Q. Gan, The tamed Milstein method for commutative stochastic differential equations with non-globally Lipschitz continuous coefficients, J. Differ. Equ. Appl., 19 (2013), 466–490. https://doi.org/10.1080/10236198.2012.656617 doi: 10.1080/10236198.2012.656617
    [27] X. J. Wang, Mean-square convergence rates of implicit Milstein type methods for SDEs with non-Lipschitz coefficients, Adv. Comput. Math., 49 (2023), 37. https://doi.org/10.1007/s10444-023-10034-2 doi: 10.1007/s10444-023-10034-2
    [28] S. Q. Gan, A. G. Xiao, D. S. Wang, Stability of analytical and numerical solutions of nonlinear stochastic delay differential equations, J. Comput. Appl. Math., 268 (2014), 5–22. https://doi.org/10.1016/j.cam.2014.02.033 doi: 10.1016/j.cam.2014.02.033
    [29] S. Henri, Numeric and dynamic B-stability, exact-monotone and asymptotic two-point behavior of theta methods for stochastic differential equations, Journal of Stochastic Analysis, 2 (2021), 7. https://doi.org/10.31390/josa.2.2.07 doi: 10.31390/josa.2.2.07
    [30] D. J. Higham, P. E. Kloeden, Numerical methods for nonlinear stochastic differential equations with jumps, Numer. Math., 101 (2005), 101–119. https://doi.org/10.1007/s00211-005-0611-8 doi: 10.1007/s00211-005-0611-8
    [31] X. J. Wang, S. Q. Gan, Compensated stochastic theta methods for stochastic differential equations with jumps, Appl. Numer. Math., 60 (2010), 877–887. https://doi.org/10.1016/j.apnum.2010.04.012 doi: 10.1016/j.apnum.2010.04.012
    [32] X. J. Wang, S. Q. Gan, The improved split-step backward Euler method for stochastic differential delay equations, Inter. J. Comput. Math., 88 (2011), 2359–2378. https://doi.org/10.1080/00207160.2010.538388 doi: 10.1080/00207160.2010.538388
    [33] C. M. Huang, Exponential mean square stability of numerical methods for systems of stochastic differential equations, J. Comput. Appl. Math., 236 (2012), 4016–4026. https://doi.org/10.1016/j.cam.2012.03.005 doi: 10.1016/j.cam.2012.03.005
    [34] D. J. Higham, X. R. Mao, A. M. Stuart, Exponential mean-square stability of numerical solutions to stochastic differential equations, LMS J. Comput. Math., 6 (2003), 297–313. https://doi.org/10.1112/S1461157000000462 doi: 10.1112/S1461157000000462
    [35] P. E. Kloeden, E. Platen, Numerical solution of stochastic differential equations, Berlin: Springer, 1992.
    [36] Z. H. Liu, $L^{p}$-convergence rate of backward Euler schemes for monotone SDEs, BIT Numer. Math., 62 (2022), 1573–1590. https://doi.org/10.1007/s10543-022-00923-1 doi: 10.1007/s10543-022-00923-1
    [37] E. Hairer, G. Wanner, Solving ordinary differential equations Ⅱ Stiff and differential-algebraic problems, 2 Eds., Berlin: Springer, 1996. https://doi.org/10.1007/978-3-642-05221-7
    [38] G. Dahlquist, Error analysis for a class of methods for stiff nonlinear initial value problems, Numerical analysis, Berlin: Springer, 1976. https://doi.org/10.1007/BFb0080115
    [39] J. C. Butcher, A stability property of implicity Runge-Kutta methods, BIT Numer. Math., 15 (1975), 358–361. https://doi.org/10.1007/BF01931672 doi: 10.1007/BF01931672
    [40] J. R. Yao, S. Q. Gan, Stability of the drift-implicit and double-implicit Milstein schemes for nonlinear SDEs, Appl. Math. Comput., 339 (2018), 294–301. https://doi.org/10.1016/j.amc.2018.07.026 doi: 10.1016/j.amc.2018.07.026
    [41] R. D'Ambrosio, S. Di Giovacchino, Nonlinear stability issues for stochastic Runge-Kutta methods, Commun. Nonlinear Sci., 94 (2021), 105549. https://doi.org/10.1016/j.cnsns.2020.105549 doi: 10.1016/j.cnsns.2020.105549
    [42] R. D'Ambrosio, S. Di Giovacchino, Mean-square contractivity of stochastic $\theta$-methods, Commun. Nonlinear Sci., 96 (2021), 105671. https://doi.org/10.1016/j.cnsns.2020.105671 doi: 10.1016/j.cnsns.2020.105671
    [43] E. Buckwar, R. D'Ambrosio, Exponential mean-square stability properties of stochastic linear multistep methods, Adv. Comput. Math., 47 (2021), 55. https://doi.org/10.1007/s10444-021-09879-2 doi: 10.1007/s10444-021-09879-2
    [44] G. N. Milstein, E. Platen, H. Schurz, Balanced implicit methods for stiff stochastic systems, SIAM J. Numer. Anal., 35 (1998), 1010–1019. https://doi.org/10.1137/S0036142994273525 doi: 10.1137/S0036142994273525
    [45] C. Kahl, H. Schurz, Balanced Milstein methods for ordinary SDEs, Monte Carlo Methods, 12 (2006), 143–170. https://doi.org/10.1515/156939606777488842 doi: 10.1515/156939606777488842
    [46] P. Wang, Z. X. Liu, Split-step backward balanced Milstein methods for stiff stochastic systems, Appl. Numer. Math., 59 (2009), 1198–1213. https://doi.org/10.1016/j.apnum.2008.06.001 doi: 10.1016/j.apnum.2008.06.001
    [47] L. Hu, A. N. Chan, X. Z. Bao, Numerical analysis of the balanced methods for stochastic Volterra integro-differential equations, Comp. Appl. Math., 40 (2021), 203. https://doi.org/10.1007/s40314-021-01593-5 doi: 10.1007/s40314-021-01593-5
    [48] J. Alcock, K. Burrage, A note on the balanced method, BIT Numer. Math., 46 (2006), 689–710. https://doi.org/10.1007/s10543-006-0098-4 doi: 10.1007/s10543-006-0098-4
    [49] P. Wang, Z. X. Liu, Stabilized Milstein type methods for stiff stochastic systems, Journal of Numerical Mathematics and Stochastics, 1 (2009), 33–44.
    [50] Y. F. Liu, W. R. Cao, Y. L. Li, Split-step balanced $\theta$-method for SDEs with non-globally Lipschitz continuous coefficients, Appl. Math. Comput., 413 (2022), 126437. https://doi.org/10.1016/j.amc.2021.126437 doi: 10.1016/j.amc.2021.126437
    [51] N. T. Dung, A stochastic Ginzburg-Landau equation with impulsive effects, Physica A, 392 (2013), 1962–1971. https://doi.org/10.1016/j.physa.2013.01.042 doi: 10.1016/j.physa.2013.01.042
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(573) PDF downloads(103) Cited by(0)

Article outline

Figures and Tables

Figures(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog