Add to ORKGIn 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
Our goal is to solve certain dynamic programming equations associated to a given Markov chain X, usi...
The present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial di...
International audienceIn this paper, we present some investigations on the parallelization of stocha...
In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to app...
In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the...
International audienceWe present a parallel algorithm for solving backward stochastic differential e...
The pricing of American style and multiple exercise options is a very challenging problem in mathema...
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Althou...
This article deals with the numerical resolution of backward stochastic differential equations. Firs...
25 pagesWe present a parallel algorithm for solving backward stochastic differential equations (BSDE...
We explore the performance of several algorithms for the solution of stochastic partial differential...
This paper extends the idea of E.Gobet, J.P.Lemor and X.Warin from the setting of Backward Stochasti...
We design a numerical scheme for solving the Multi step-forward Dynamic Programming (MDP) equation a...
The main purpose of this work was to develop a more time efficient solution to the Lotka- Volterra m...
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stoc...
Our goal is to solve certain dynamic programming equations associated to a given Markov chain X, usi...
The present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial di...
International audienceIn this paper, we present some investigations on the parallelization of stocha...
In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to app...
In this article we design a novel quasi-regression Monte Carlo algorithm in order to approximate the...
International audienceWe present a parallel algorithm for solving backward stochastic differential e...
The pricing of American style and multiple exercise options is a very challenging problem in mathema...
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Althou...
This article deals with the numerical resolution of backward stochastic differential equations. Firs...
25 pagesWe present a parallel algorithm for solving backward stochastic differential equations (BSDE...
We explore the performance of several algorithms for the solution of stochastic partial differential...
This paper extends the idea of E.Gobet, J.P.Lemor and X.Warin from the setting of Backward Stochasti...
We design a numerical scheme for solving the Multi step-forward Dynamic Programming (MDP) equation a...
The main purpose of this work was to develop a more time efficient solution to the Lotka- Volterra m...
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stoc...
Our goal is to solve certain dynamic programming equations associated to a given Markov chain X, usi...
The present thesis deals with numerical schemes to solve Markov Decision Problems (MDPs), partial di...
International audienceIn this paper, we present some investigations on the parallelization of stocha...