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dc.contributor.authorBouchard, Bruno*
dc.contributor.authorTan, Xiaolu*
dc.contributor.authorWarin, Xavier*
dc.date.accessioned2018-09-03T13:19:00Z
dc.date.available2018-09-03T13:19:00Z
dc.date.issued2019
dc.identifier.issn2267-3059
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17929
dc.language.isoenen
dc.subjectBSDE
dc.subjectMonte-Carlo methods
dc.subjectbranching process
dc.subject.ddc519en
dc.titleNumerical approximation of general Lipschitz BSDEs with branching processes
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe extend the branching process based numerical algorithm of Bouchard et al. [3], that is dedicated to semilinear PDEs (or BSDEs) with Lipschitz nonlinearity, to the case where the nonlinearity involves the gradient of the solution. As in [3], this requires a localization procedure that uses a priori estimates on the true solution, so as to ensure the well-posedness of the involved Picard iteration scheme, and the global convergence of the algorithm. When, the nonlinearity depends on the gradient, the later needs to be controlled as well. This is done by using a face-lifting procedure. Convergence of our algorithm is proved without any limitation on the time horizon. We also provide numerical simulations to illustrate the performance of the algorithm.
dc.relation.isversionofjnlnameESAIM: Proceedings and Surveys
dc.relation.isversionofjnlvol65
dc.relation.isversionofjnldate2019
dc.relation.isversionofjnlpages309-329
dc.relation.isversionofdoi10.1051/proc/201965309
dc.relation.isversionofjnlpublisherEDP Sciences
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
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dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2019-07-16T12:48:20Z
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