Probabilistic High Order Numerical Schemes for Fully Nonlinear Parabolic PDEs

Probabilistic High Order Numerical Schemes for Fully Nonlinear Parabolic PDEs

Year:    2015

Communications in Computational Physics, Vol. 18 (2015), Iss. 5 : pp. 1482–1503

Abstract

In this paper, we are concerned with probabilistic high order numerical schemes for Cauchy problems of fully nonlinear parabolic PDEs. For such parabolic PDEs, it is shown by Cheridito, Soner, Touzi and Victoir [4] that the associated exact solutions admit probabilistic interpretations, i.e., the solution of a fully nonlinear parabolic PDE solves a corresponding second order forward backward stochastic differential equation (2FBSDEs). Our numerical schemes rely on solving those 2FBSDEs, by extending our previous results [W. Zhao, Y. Fu and T. Zhou, SIAM J. Sci. Comput., 36 (2014), pp. A1731-A1751.]. Moreover, in our numerical schemes, one has the flexibility to choose the associated forward SDE, and a suitable choice can significantly reduce the computational complexity. Various numerical examples including the HJB equations are presented to show the effectiveness and accuracy of the proposed numerical schemes.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.240515.280815a

Communications in Computational Physics, Vol. 18 (2015), Iss. 5 : pp. 1482–1503

Published online:    2015-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    22

Keywords:   

  1. Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

    Beck, Christian | E, Weinan | Jentzen, Arnulf

    Journal of Nonlinear Science, Vol. 29 (2019), Iss. 4 P.1563

    https://doi.org/10.1007/s00332-018-9525-3 [Citations: 174]
  2. Optimal Error Estimates for a Fully Discrete Euler Scheme for Decoupled Forward Backward Stochastic Differential Equations

    Gong, Bo | Zhao, Weidong

    East Asian Journal on Applied Mathematics, Vol. 7 (2017), Iss. 3 P.548

    https://doi.org/10.4208/eajam.110417.070517a [Citations: 1]
  3. Stochastic control of systems with control multiplicative noise using second order FBSDEs

    Bakshi, Kaivalya S. | Fan, David D. | Theodorou, Evangelos A.

    2017 American Control Conference (ACC), (2017), P.424

    https://doi.org/10.23919/ACC.2017.7962990 [Citations: 2]
  4. A fully nonlinear Feynman–Kac formula with derivatives of arbitrary orders

    Nguwi, Jiang Yu | Penent, Guillaume | Privault, Nicolas

    Journal of Evolution Equations, Vol. 23 (2023), Iss. 1

    https://doi.org/10.1007/s00028-023-00873-3 [Citations: 4]
  5. On some neural network architectures that can represent viscosity solutions of certain high dimensional Hamilton–Jacobi partial differential equations

    Darbon, Jérôme | Meng, Tingwei

    Journal of Computational Physics, Vol. 425 (2021), Iss. P.109907

    https://doi.org/10.1016/j.jcp.2020.109907 [Citations: 35]
  6. Nonlocality, nonlinearity, and time inconsistency in stochastic differential games

    Lei, Qian | Pun, Chi Seng

    Mathematical Finance, Vol. 34 (2024), Iss. 1 P.190

    https://doi.org/10.1111/mafi.12420 [Citations: 1]
  7. An efficient numerical method for forward-backward stochastic differential equations driven by G-Brownian motion

    Hu, Mingshang | Jiang, Lianzi

    Applied Numerical Mathematics, Vol. 165 (2021), Iss. P.578

    https://doi.org/10.1016/j.apnum.2021.03.012 [Citations: 6]
  8. Overcoming the curse of dimensionality in the approximative pricing of financial derivatives with default risks

    Hutzenthaler, Martin | Jentzen, Arnulf | Wurstemberger, von Wurstemberger

    Electronic Journal of Probability, Vol. 25 (2020), Iss. none

    https://doi.org/10.1214/20-EJP423 [Citations: 25]
  9. Nonlocal Fully Nonlinear Parabolic Differential Equations Arising in Time-Inconsistent Problems

    Lei, Qian | Pun, Chi Seng

    SSRN Electronic Journal , Vol. (2021), Iss.

    https://doi.org/10.2139/ssrn.3938846 [Citations: 1]
  10. Numerical methods for backward stochastic differential equations: A survey

    Chessari, Jared | Kawai, Reiichiro | Shinozaki, Yuji | Yamada, Toshihiro

    Probability Surveys, Vol. 20 (2023), Iss. none

    https://doi.org/10.1214/23-PS18 [Citations: 8]
  11. Efficient spectral sparse grid approximations for solving multi-dimensional forward backward SDEs

    Fu, Yu | Zhao, Weidong | Zhou, Tao

    Discrete & Continuous Dynamical Systems - B, Vol. 22 (2017), Iss. 9 P.3439

    https://doi.org/10.3934/dcdsb.2017174 [Citations: 12]
  12. Numerical approximation based on deep convolutional neural network for high‐dimensional fully nonlinear merged PDEs and 2BSDEs

    Xiao, Xu | Qiu, Wenlin | Nikan, Omid

    Mathematical Methods in the Applied Sciences, Vol. 47 (2024), Iss. 7 P.6184

    https://doi.org/10.1002/mma.9915 [Citations: 0]
  13. Nonlocal fully nonlinear parabolic differential equations arising in time-inconsistent problems

    Lei, Qian | Pun, Chi Seng

    Journal of Differential Equations, Vol. 358 (2023), Iss. P.339

    https://doi.org/10.1016/j.jde.2023.02.025 [Citations: 8]
  14. Nonlocality, Nonlinearity, and Time Inconsistency in Stochastic Differential Games

    Lei, Qian | Pun, Chi Seng

    SSRN Electronic Journal , Vol. (2022), Iss.

    https://doi.org/10.2139/ssrn.4001102 [Citations: 0]
  15. Itô-Taylor Schemes for Solving Mean-Field Stochastic Differential Equations

    Sun, Yabing | Yang, Jie | Zhao, Weidong

    Numerical Mathematics: Theory, Methods and Applications, Vol. 10 (2017), Iss. 4 P.798

    https://doi.org/10.4208/nmtma.2017.0007 [Citations: 4]