QuasiRegression MonteCarlo Method for SemiLinear PDEs and BSDEs^{ †}
Abstract
:1. Introduction
2. Results
Algorithm 1. Global QuasiRegression Multistepforward Dynamical Programming (GQRMDP) algorithm 

3. Discussion
Conflicts of Interest
References
 NVIDIA cuRAND Web Page. Available online: https://developer.nvidia.com/curand (accessed on 5 October 2018).
 L’Ecuyer, P. Good parameters and implementations for combined multiple recursive random number generators. Oper. Res. 1999, 47, 159–164. [Google Scholar] [CrossRef]
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Gobet, E.; Salas, J.G.L.; Vázquez, C. QuasiRegression MonteCarlo Method for SemiLinear PDEs and BSDEs. Proceedings 2019, 21, 44. https://doi.org/10.3390/proceedings2019021044
Gobet E, Salas JGL, Vázquez C. QuasiRegression MonteCarlo Method for SemiLinear PDEs and BSDEs. Proceedings. 2019; 21(1):44. https://doi.org/10.3390/proceedings2019021044
Chicago/Turabian StyleGobet, Emmanuel, José Germán López Salas, and Carlos Vázquez. 2019. "QuasiRegression MonteCarlo Method for SemiLinear PDEs and BSDEs" Proceedings 21, no. 1: 44. https://doi.org/10.3390/proceedings2019021044