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

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http://hdl.handle.net/2183/23895
Atribución 4.0 Internacional
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional
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Title
Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs
Author(s)
Gobet, Emmanuel
López-Salas, José Germán
Vázquez, Carlos
Date
2019-08-06
Citation
GOBET, Emmanuel; SALAS, José Germán López; VÁZQUEZ, Carlos. Quasi-Regression Monte-Carlo Method for Semi-Linear PDEs and BSDEs. En Multidisciplinary Digital Publishing Institute Proceedings. 2019. p. 44.
Abstract
[Abstract] 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
 
Editor version
https://doi.org/10.3390/proceedings2019021044
Rights
Atribución 4.0 Internacional
ISSN
2504-3900

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