Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage677es_ES
UDC.grupoInvModelos e Métodos Numéricos en Enxeñaría e Ciencias Aplicadas (M2NICA)es_ES
UDC.issue6es_ES
UDC.journalTitleSIAM Journal on Scientific Computinges_ES
UDC.startPage652es_ES
UDC.volume38es_ES
dc.contributor.authorGobet, Emmanuel
dc.contributor.authorLópez-Salas, José Germán
dc.contributor.authorTurkedjiev, Plamen
dc.contributor.authorVázquez, Carlos
dc.date.accessioned2024-07-18T07:57:13Z
dc.date.available2024-07-18T07:57:13Z
dc.date.issued2016-06
dc.description©2016 This version of the article has been accepted for publication, after peer review, but is not the version of record and does not reflect postacceptance improvements, or any corrections. The version of record is available online at: https://doi.org/10.1137/16M106371Xes_ES
dc.description.abstract[Abstract]: In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to approximate the solution of discrete time Backward Stochastic Differential Equations (BSDEs). Our algorithm allows massive parallelization of the computations on multicore devices such as graphics processing units (GPUs). Our approach consists of a novel method of stratification which appears to be crucial for large scale parallelization.es_ES
dc.description.sponsorshipThis research is part of the Chair Financial Risks of the Risk Foundation, the FiME Laboratory and the ANR project CAESARS (ANR-15-CE05-0024). This research is supported the Chair Financial Risks of the Risk Foundation and of the FiME Laboratory. J. G. López-Salas has been partially funded by Spanish Grant MTM2013- 47800-C2-1-P.es_ES
dc.description.sponsorshipFrance. Agence Nationale de la Recherche; ANR-15-CE05-0024es_ES
dc.identifier.citationE. Gobet, J. G. López-Salas, P. Turkedjiev, y C. Vázquez, «Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs», SIAM J. Sci. Comput., vol. 38, n.o 6, pp. C652-C677, ene. 2016, doi: 10.1137/16M106371Xes_ES
dc.identifier.issn1064-8275
dc.identifier.issn1095-7197
dc.identifier.urihttp://hdl.handle.net/2183/38128
dc.language.isoenges_ES
dc.publisherSociety for Industrial and Applied Mathematics (SIAM)es_ES
dc.relation.projectIDInfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2013-47800-C2-1-P/ES/MODELADO MATEMATICO, ANALISIS Y SIMULACION NUMERICA DE PROBLEMAS EN FINANZAS Y SEGUROS, PROCESOS INDUSTRIALES, BIOTECNOLOGIA Y MEDIOAMBIENTEes_ES
dc.relation.urihttps://doi.org/10.1137/16M106371Xes_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectBackward stochastic differential equationses_ES
dc.subjectDynamic programming equationes_ES
dc.subjectEm-pirical regressionses_ES
dc.subjectParallel computinges_ES
dc.subjectGPUses_ES
dc.subjectCUDAes_ES
dc.titleStratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication7879649b-7a9b-41cd-92df-f8e4c60d215f
relation.isAuthorOfPublicationdbc2be8e-6741-46b3-a22e-b648eae643d4
relation.isAuthorOfPublication.latestForDiscovery7879649b-7a9b-41cd-92df-f8e4c60d215f

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