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.
https://hal-polytechnique.archives-ouvertes.fr/hal-01186000
Contributeur : Emmanuel Gobet
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Soumis le : jeudi 27 août 2015 - 11:21:12
Dernière modification le : jeudi 10 mai 2018 - 02:04:31
Document(s) archivé(s) le : samedi 28 novembre 2015 - 10:15:01
Emmanuel Gobet, Jose Lopez-Salas, Plamen Turkedjiev, Carlos Vasquez. Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs. 2015. 〈hal-01186000〉
