Next Article in Journal
Promising Approach to Inhibit E. coli FimH Adhesion by C-Linked Mannosides
Previous Article in Journal
Design of Mutation Operators for Testing Geographic Information Systems
Open AccessProceedings

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

1
Centre de Mathématiques Appliquées, École Polytechnique and CNRS, route de Saclay, 91128 Palaiseau CEDEX, France
2
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; https://doi.org/10.3390/proceedings2019021044
Published: 6 August 2019
(This article belongs to the Proceedings of XoveTIC Conference)
PDF [211 KB, uploaded 6 August 2019]

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 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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Proceedings EISSN 2504-3900 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top