Hostname: page-component-7479d7b7d-767nl Total loading time: 0 Render date: 2024-07-13T04:23:42.689Z Has data issue: false hasContentIssue false

Itô-Taylor Schemes for Solving Mean-Field Stochastic Differential Equations

Published online by Cambridge University Press:  12 September 2017

Yabing Sun*
Affiliation:
School of Mathematics & Finance Institute, Shandong University, Jinan 250100, China
Jie Yang*
Affiliation:
School of Mathematics & Finance Institute, Shandong University, Jinan 250100, China
Weidong Zhao*
Affiliation:
School of Mathematics & Finance Institute, Shandong University, Jinan 250100, China
*
*Corresponding author. Email addresses:sunybly@163.com (Y. B. Sun), yangjie218@mail.sdu.edu.cn (J. Yang), wdzhao@sdu.edu.cn (W. D. Zhao)
*Corresponding author. Email addresses:sunybly@163.com (Y. B. Sun), yangjie218@mail.sdu.edu.cn (J. Yang), wdzhao@sdu.edu.cn (W. D. Zhao)
*Corresponding author. Email addresses:sunybly@163.com (Y. B. Sun), yangjie218@mail.sdu.edu.cn (J. Yang), wdzhao@sdu.edu.cn (W. D. Zhao)
Get access

Abstract

This paper is devoted to numerical methods for mean-field stochastic differential equations (MSDEs). We first develop the mean-field Itô formula and mean-field Itô-Taylor expansion. Then based on the new formula and expansion, we propose the Itô-Taylor schemes of strong order γ and weak order η for MSDEs, and theoretically obtain the convergence rate γ of the strong Itô-Taylor scheme, which can be seen as an extension of the well-known fundamental strong convergence theorem to the mean-field SDE setting. Finally some numerical examples are given to verify our theoretical results.

Type
Research Article
Copyright
Copyright © Global-Science Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Ahdersson, D. and Djehiche, B., A maximum principle for SDEs of mean-field type, Appl. Math. Opt., 63 (2011), pp. 341356.Google Scholar
[2] Bardi, M. and Capuzzo, D., Optimal control and viscosity solutions of Hamilton-Jacobi-Bellman equations, Birkhauser, 7(10) (1997), pp. S237–S244(8).Google Scholar
[3] Bossy, M. and Talay, D., A stochastic particle method for the McKean-Vlasov and the Burgers equation, Math. Comput., 1997 (66), pp. 157192.Google Scholar
[4] Buckdahn, R., Djehiche, B., Li, J. and Peng, S., Mean-field backward stochastic differential equations: a limit approach, Ann. Probab., 2009 (37), pp. 15241565.Google Scholar
[5] Buckdahn, R., Li, J. and Peng, S., Mean-field backward stochastic differential equations and related partial differential equations, Stochastic Pro. Appl., 2009 (119), pp. 31333154.Google Scholar
[6] Feng, S., Large deviations for Markov processes with mean-field interaction and unbounded jumps., Probab. Theory Related Fields, 1994 (100), pp. 227252.Google Scholar
[7] Fu, Y., Zhao, W., and Zhou, T., Multistep schemes for forward backward stochastic differential equations with jumps, J. Sci. Comput., 69 (2016), pp. 122.Google Scholar
[8] Fu, Y., Zhao, W., and Zhou, T., Efficient spectral sparse grid approximations for solving multidimensional forward backward SDEs, Discrete Contin. Dyn. Syst. Ser. B, 22 (2017), pp. 34393458.Google Scholar
[9] Guéant, O., Lasry, J. and Lions, P., Mean-field games and applications, Paris-Princeton lectures on mathematical finance 2010, Springer-Verlag Berlin, 2003, pp. 205266.Google Scholar
[10] Hafayed, M., A mean-field necessary and sufficient conditions for optimal singular stochastic control, Commun. Math. Stat., 1(4) (2013), pp. 417435.Google Scholar
[11] Kloeden, P. and Platen, E., Numerical Solution of Stochastic Differential Equations, Springer-Verlag, Berlin, 1992.Google Scholar
[12] Kong, T., Zhao, W., and Zhou, T., Probabilistic high order numerical schemes for fully nonlinear parabolic PDEs, Commun. Comput. Phys., 18 (2015), pp. 14821503.CrossRefGoogle Scholar
[13] Kotelenez, P., A class of quasilinear stochastic partial differential equations of McKean-Vlasov type with mass conservation, Probab. Theory Related Fields, 1995 (102), pp. 159188.Google Scholar
[14] Lasry, J. and Lions, P., Mean-field games, Japan J. Math., 2007 (2), pp. 229260.Google Scholar
[15] Léonard, C., Une loi des grands nombres pour des systèmes de diffusions avec interaction et à coefficients non bornés, Annales de l'IHP Probabilités et statistiques, 22(2) (1986), pp. 237262.Google Scholar
[16] Li, J., Stochastic maximum principle in the mean-field controls, Automatica, 48(2) (2012), pp. 366373.Google Scholar
[17] McKean, H., Propagation of chaos for a class of non-linear parabolic equations, in Lecture Series in Differential Equations, Catholic University, 1967, pp. 4157.Google Scholar
[18] Méléard, S., Asymptotic behaviour of some interacting particle systems; McKean-Vlasov and Boltzmann models in probabilistic models for nonlinear partial differential equations, Springer-Verlag, Berlin, 1996, pp. 4295.Google Scholar
[19] Mendoza, M., Aguilar, M. and Valle, F., A mean-field approach that combines quantum mechanics and molecular dynamics simulation: the water molecule in liquid water, J. Mol. Struct., 1998 (426), pp. 181190.Google Scholar
[20] Milstein, G. and Tretyakov, M., Stochastic Numerics for Mathematical Physics, Springer-Verlag, Berlin, 2004.Google Scholar
[21] Ni, Y., Li, X. and Zhang, J., Mean-field stochastic linear-quadratic optimal control with Markov jump parameters, Syst. Control Lett., 93 (2016), pp. 6976.Google Scholar
[22] Øksendal, B., Stochastic Differential Equations, Springer-Verlag, Berlin, 2003.Google Scholar
[23] Stevenson, P., Stone, J. and Strayer, M., Hartree-Fock mean-field models using separable interactions, Office of Scientific & Technical Information Technical Reports, 217 (1999), U8.Google Scholar
[24] Sznitmann, AS., A fluctuation result for nonlinear diffusions, Pitman Research Notes in Math, 124 (1985), pp. 145160.Google Scholar
[25] Talay, D. and Vaillant, O., A stochastic particle method with random weights for the computation of statistical solutions of McKean-Vlasov equations, Ann. Appl. Probab, 13 (2003), pp. 140180.CrossRefGoogle Scholar
[26] Tanaka, H., Limit theorems for certain diffusion processes with interaction, North-Holland Mathematical Library, 32 (1984), pp. 469488.Google Scholar
[27] Tang, T., Zhao, W., and Zhou, T., Deferred correction methods for forward backward stochastic differential equations, Numer. Math. Theor. Meth. Appl., 10 (2017), pp. 222242.Google Scholar
[28] Wang, B. and Zhang, J., mean-field games for large population multi-agent systems with Markov jump parameters, SIAM J. Control Optim., 50 (2012), pp. 23082334.Google Scholar
[29] Wang, G., Zhang, C. and Zhang, W., Stochastic maximum principle for mean-field type optimal control under partial information, IEEE T. Automat. Contr., 59(2) (2014), pp. 522528.Google Scholar
[30] Yang, J. and Zhao, W., Convergence of recent multistep schemes for a forward-backward stochastic differential equation, East Asian J. Appl. Math., 5 (2015), pp. 387404.Google Scholar
[31] Zhao, W., Chen, L., and Peng, S., A new kind of accurate numerical method for backward stochastic differential equations, SIAM J. Sci. Comput., 28 (2006), pp. 15631581.Google Scholar
[32] Zhao, W., Fu, Y., and Zhou, T., New kinds of high-order multistep schemes for coupled forward backward stochastic differential equations, SIAM J. Sci. Comput., 36 (2014), pp. A1731A1751.Google Scholar
[33] Zhao, W., Li, Y., and Fu, Y., Second-order schemes for solving decoupled forward backward stochastic differential equations, Sci. China Math., 57 (2014), pp. 665686.Google Scholar
[34] Zhao, W., Tao, Z., and Tao, K., High order numerical schemes for second-order FBSDEs with applications to stochastic optimal control, Commun. Comput. Phys., 21 (2017), pp. 808834.CrossRefGoogle Scholar
[35] Zhao, W., Wang, J., and Peng, S., Error estimates of the θ-scheme for backward stochastic differential equations, Discrete Contin. Dyn. Syst. Ser. B, 12 (2009), pp. 905924.Google Scholar
[36] Zhao, W., Zhang, G., and Ju, L., A stable multistep scheme for solving backward stochastic differential equations, SIAM J. Numer. Anal., 48 (2010), pp. 13691394.Google Scholar
[37] Zhao, W., Zhang, W., and Ju, L., A numerical method and its error estimates for the decoupled forward-backward stochastic differential equations, Commun. Comput. Phys., 15 (2014), pp. 618646.Google Scholar
[38] Zhao, W., Zhang, W., and Ju, L., A multistep scheme for decoupled forward-backward stochastic differential equations, Numer. Math. Theor. Meth. Appl., 9 (2016), pp. 262288.Google Scholar