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Strong error analysis of Euler methods for overdamped generalized Langevin equations with fractional noise: nonlinear case. (English) Zbl 1525.65011

Summary: This paper considers the strong error analysis of the Euler and fast Euler methods for nonlinear overdamped generalized Langevin equations driven by the fractional noise. The main difficulty lies in handling the interaction between the fractional Brownian motion and the singular kernel, which is overcome by means of the Malliavin calculus and fine estimates of several multiple singular integrals. Consequently, these two methods are proved to be strongly convergent with order nearly \(\min \{2(H + \alpha - 1), \alpha\}\), where \(H \in (1/2, 1)\) and \(\alpha \in (1 - H, 1)\) respectively characterize the singularity levels of fractional noises and singular kernels in the underlying equation. This result improves the existing convergence order \(H + \alpha - 1\) of Euler methods for the nonlinear case, and gives a positive answer to the open problem raised in [D. Fang and L. Li, ESAIM, Math. Model. Numer. Anal. 54, No. 2, 431–463 (2020; Zbl 07201590)]. As an application of the theoretical findings, we further investigate the complexity of the multilevel Monte Carlo simulation based on the fast Euler method, which turns out to behave better performance than the standard Monte Carlo simulation when computing the expectation of functionals of the considered equation. Finally, numerical experiments are carried out to support the theoretical results.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
60H35 Computational methods for stochastic equations (aspects of stochastic analysis)
65C05 Monte Carlo methods
60H07 Stochastic calculus of variations and the Malliavin calculus

Citations:

Zbl 07201590

References:

[1] X. Dai and A. Xiao, Lévy-driven stochastic Volterra integral equations with doubly singular kernels: existence, uniqueness, and a fast EM method. Adv. Comput. Math. 46 (2020) 29. · Zbl 1457.60103 · doi:10.1007/s10444-020-09780-4
[2] X. Dai and A. Xiao, A note on Euler method for the overdamped generalized Langevin equation with fractional noise. Appl. Math. Lett. 111 (2021) 106669. · Zbl 1450.65002 · doi:10.1016/j.aml.2020.106669
[3] R.C. Dalang, Extending the martingale measure stochastic integral with applications to spatially homogeneous S.P.D.E’.s. Electron. J. Probab. 4 (1999) 1-29. · Zbl 0922.60056 · doi:10.1214/EJP.v4-43
[4] G. Didier and H. Nguyen, Asymptotic analysis of the mean squared displacement under fractional memory kernels. SIAM J. Math. Anal. 52 (2020) 3818-3842. · Zbl 1457.60059 · doi:10.1137/19M1238113
[5] D. Fang and L. Li, Numerical approximation and fast evaluation of the overdamped generalized Langevin equation with fractional noise. ESAIM Math. Model. Numer. Anal. 54 (2020) 431-463. · Zbl 07201590 · doi:10.1051/m2an/2019067
[6] M.B. Giles, Multilevel Monte Carlo path simulation. Oper. Res. 56 (2008) 607-617. · Zbl 1167.65316 · doi:10.1287/opre.1070.0496
[7] M.B. Giles, Multilevel Monte Carlo methods. Acta Numer. 24 (2015) 259-328. · Zbl 1316.65010 · doi:10.1017/S096249291500001X
[8] D.J. Higham, An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Rev. 43 (2001) 525-546. · Zbl 0979.65007
[9] D.J. Higham, X. Mao and A.M. Stuart, Strong convergence of Euler-type methods for nonlinear stochastic differential equations. SIAM J. Numer. Anal. 40 (2002) 1041-1063. · Zbl 1026.65003 · doi:10.1137/S0036142901389530
[10] J. Hong, C. Huang, M. Kamrani and X. Wang, Optimal strong convergence rate of a backward Euler type scheme for the Cox-Ingersoll-Ross model driven by fractional Brownian motion. Stochastic Process. Appl. 130 (2020) 2675-2692. · Zbl 1451.60076 · doi:10.1016/j.spa.2019.07.014
[11] M. Hutzenthaler, A. Jentzen and P.E. Kloeden, Strong and weak divergence in finite time of Euler’s method for stochastic differential equations with non-globally Lipschitz continuous coefficients. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 467 (2011) 1563-1576. · Zbl 1228.65014
[12] S. Jiang, J. Zhang, Q. Zhang and Z. Zhang, Fast evaluation of the Caputo fractional derivative and its applications to fractional diffusion equations. Commun. Comput. Phys. 21 (2017) 650-678. · Zbl 1488.65247
[13] P.E. Kloeden, A. Neuenkirch and R. Pavani, Multilevel Monte Carlo for stochastic differential equations with additive fractional noise. Ann. Oper. Res. 189 (2011) 255-276. · Zbl 1235.60064 · doi:10.1007/s10479-009-0663-8
[14] S.C. Kou, Stochastic modeling in nanoscale biophysics: subdiffusion within proteins. Ann. Appl. Stat. 2 (2008) 501-535. · Zbl 1400.62272
[15] S.C. Kou and X. Sunney Xie, Generalized Langevin equation with fractional Gaussian noise: subdiffusion within a single protein molecule. Phys. Rev. Lett. 93 (2004) 180603. · doi:10.1103/PhysRevLett.93.180603
[16] R. Kubo, The fluctuation-dissipation theorem. Rep. Prog. Phys. 29 (1966) 255-284. · Zbl 0163.23102 · doi:10.1088/0034-4885/29/1/306
[17] L. Li and J.-G. Liu, A discretization of Caputo derivatives with application to time fractional SDEs and gradient flows. SIAM J. Numer. Anal. 57 (2019) 2095-2120. · Zbl 07105241 · doi:10.1137/19M123854X
[18] L. Li, J.-G. Liu and J. Lu, Fractional stochastic differential equations satisfying fluctuation-dissipation theorem. J. Stat. Phys. 169 (2017) 316-339. · Zbl 1386.82053
[19] X. Mao, Stochastic Differential Equations and Applications, 2nd edition. Horwood Publishing Limited, Chichester (2008).
[20] S.A. McKinley and H.D. Nguyen, Anomalous diffusion and the generalized Langevin equation. SIAM J. Math. Anal. 50 (2018) 5119-5160. · Zbl 1409.60055 · doi:10.1137/17M115517X
[21] H. Mori, Transport, collective motion, and Brownian motion. Progr. Theoret. Phys. 33 (1965) 423-455. · Zbl 0127.45002 · doi:10.1143/PTP.33.423
[22] D. Nualart, The Malliavin Calculus and Related Topics, 2nd edition. Springer-Verlag, Berlin (2006). · Zbl 1099.60003
[23] V. Pipiras and M.S. Taqqu, Integration questions related to fractional Brownian motion. Probab. Theory Relat. Fields 118 (2000) 251-291. · doi:10.1007/s440-000-8016-7
[24] A. Richard, X. Tan and F. Yang, Discrete-time simulation of stochastic Volterra equations. Stochastic Process. Appl. 141 (2021) 109-138. · Zbl 1480.60188 · doi:10.1016/j.spa.2021.07.003
[25] M. Sanz-Solé, Malliavin Calculus with Applications to Stochastic Partial Differential Equations. EPFL Press, distributed by CRC Press (2005). · Zbl 1098.60050 · doi:10.1201/9781439818947
[26] H. Ye, J. Gao and Y. Ding, A generalized Gronwall inequality and its application to a fractional differential equation. J. Math. Anal. Appl. 328 (2007) 1075-1081. · Zbl 1120.26003
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