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Open AccessProceedings

Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs

Centre de Mathématiques Appliquées, École Polytechnique and CNRS, route de Saclay, 91128 Palaiseau CEDEX, France
Department of Mathematics, Faculty of Informatics, Universidade da Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain
Author to whom correspondence should be addressed.
Presented at the 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 44;
Published: 6 August 2019
(This article belongs to the Proceedings of XoveTIC Conference)
PDF [211 KB, uploaded 6 August 2019]


In this work we design a novel and efficient quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations (BSDEs), and we analyze the convergence of the proposed method. With the challenge of tackling problems in high dimensions we propose suitable projections of the solution and efficient parallelizations of the algorithm taking advantage of powerful many core processors such as graphics processing units (GPUs).
Keywords: BSDEs; semi-linear PDEs; parallel computing; GPUs; CUDA BSDEs; semi-linear PDEs; parallel computing; GPUs; CUDA
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Gobet, E.; Salas, J.G.L.; Vázquez, C. Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs. Proceedings 2019, 21, 44.

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