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On Multilevel Picard Numerical Approximations for High-Dimensional Nonlinear Parabolic Partial Differential Equations and High-Dimensional Nonlinear Backward Stochastic Differential Equations

  • Weinan E
  • Martin Hutzenthaler
  • Arnulf JentzenEmail author
  • Thomas Kruse
Article
  • 36 Downloads

Abstract

Parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) are key ingredients in a number of models in physics and financial engineering. In particular, parabolic PDEs and BSDEs are fundamental tools in pricing and hedging models for financial derivatives. The PDEs and BSDEs appearing in such applications are often high-dimensional and nonlinear. Since explicit solutions of such PDEs and BSDEs are typically not available, it is a very active topic of research to solve such PDEs and BSDEs approximately. In the recent article (E et al., Multilevel Picard iterations for solving smooth semilinear parabolic heat equations, arXiv:1607.03295) we proposed a family of approximation methods based on Picard approximations and multilevel Monte Carlo methods and showed under suitable regularity assumptions on the exact solution of a semilinear heat equation that the computational complexity is bounded by \(O( d \, {\varepsilon }^{-(4+\delta )})\) for any \(\delta \in (0,\infty )\) where d is the dimensionality of the problem and \({\varepsilon }\in (0,\infty )\) is the prescribed accuracy. In this paper, we test the applicability of this algorithm on a variety of 100-dimensional nonlinear PDEs that arise in physics and finance by means of numerical simulations presenting approximation accuracy against runtime. The simulation results for many of these 100-dimensional example PDEs are very satisfactory in terms of both accuracy and speed. Moreover, we also provide a review of other approximation methods for nonlinear PDEs and BSDEs from the scientific literature.

Keywords

Curse of dimensionality High-dimensional PDEs High-dimensional nonlinear BSDEs Multilevel Picard approximations Multilevel Monte Carlo method 

Mathematics Subject Classification

65M75 

Notes

Acknowledgements

This project has been partially supported through the research Grants ONR N00014-13-1-0338 and DOE DE-SC0009248 and through the German Research Foundation via RTG 2131 High-dimensional Phenomena in Probability—Fluctuations and Discontinuity and via research Grant HU 1889/6-1.

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Authors and Affiliations

  • Weinan E
    • 1
  • Martin Hutzenthaler
    • 2
  • Arnulf Jentzen
    • 3
    Email author
  • Thomas Kruse
    • 2
  1. 1.Department of Mathematics and Program in Applied and Computational MathematicsPrinceton UniversityPrincetonUSA
  2. 2.Faculty of MathematicsUniversity of Duisburg-EssenEssenGermany
  3. 3.Seminar für Angewandte MathematikETH ZurichZurichSwitzerland

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