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Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs
dc.contributor.author | Gobet, Emmanuel | |
dc.contributor.author | López Salas, José Germán | |
dc.contributor.author | Vázquez, Carlos | |
dc.date.accessioned | 2024-07-19T12:31:21Z | |
dc.date.available | 2024-07-19T12:31:21Z | |
dc.date.issued | 2019-04-04 | |
dc.identifier.citation | Gobet, E., López-Salas, J.G. & Vázquez, C. Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs. Arch Computat Methods Eng 27, 889–921 (2020). https://doi.org/10.1007/s11831-019-09335-x | es_ES |
dc.identifier.issn | 1886-1784 | |
dc.identifier.issn | 1134-3060 | |
dc.identifier.uri | http://hdl.handle.net/2183/38173 | |
dc.description | ©2019 This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11831-019-09335-x | es_ES |
dc.description.abstract | [Abstract]: In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the solution of discrete time backward stochastic differential equations, and we analyze the convergence of the proposed method. The algorithm also approximates the solution to the related semi-linear parabolic partial differential equation obtained through the well known Feynman–Kac representation. For the sake of enriching the algorithm with high order convergence a weighted approximation of the solution is computed and appropriate conditions on the parameters of the method are inferred. 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. | es_ES |
dc.description.sponsorship | The frst author research is part of the Finance for Energy Markets (FiME) lab, of the Chair Financial Risks of the Risk Foundation and of the ANR project CAESARS (ANR-15-CE05-0024). The second author has been fnancially supported by the Chair Financial Risks of the Risk Foundation, the Spanish Grant MTM2016-76497-R and the Xunta de Galicia 2018 postdoctoral grant. The third author was partially supported by Spanish Grant MTM2016-76497-R. | es_ES |
dc.description.sponsorship | France. Agence National de la Recherche; ANR-15-CE05-0024 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-76497-R/ES/METODOS MATEMATICOS Y SIMULACION NUMERICA PARA RETOS EN FINANZAS CUANTITATIVAS, MEDIOAMBIENTE, BIOTECNOLOGIA Y EFICIENCIA INDUSTRIAL | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11831-019-09335-x | es_ES |
dc.subject | Backward stochastic differential equations | es_ES |
dc.subject | High-order convergence | es_ES |
dc.subject | Linear-parabolic | es_ES |
dc.subject | Many-core processors | es_ES |
dc.subject | Monte carlo algorithms | es_ES |
dc.subject | Monte carlo schemes | es_ES |
dc.subject | Parallel computing | es_ES |
dc.subject | GPUs | es_ES |
dc.title | Quasi-Regression Monte-Carlo Scheme for Semi-Linear PDEs and BSDEs with Large Scale Parallelization on GPUs | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Archives of Computational Methods in Engineering | es_ES |
UDC.volume | 27 | es_ES |
UDC.startPage | 889 | es_ES |
UDC.endPage | 921 | es_ES |
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