scholarly journals Monte Carlo Methods for Controller Approximation and Stabilization in Nonlinear Stochastic Optimal Control**This work was supported by AFOSR/AOARD via AOARD- 144042. A.N. Bishop is also supported by NICTA and the Australian Research Council (ARC) via a Discovery Early Career Researcher Award (DE-120102873). He is also an adjunct Fellow at the Australian National University. [email protected].

2015 ◽  
Vol 48 (28) ◽  
pp. 811-816
Author(s):  
Francesco Bertoli ◽  
Adrian N. Bishop
Author(s):  
Ajay Jasra ◽  
Arnaud Doucet

In this paper, we show how to use sequential Monte Carlo methods to compute expectations of functionals of diffusions at a given time and the gradients of these quantities w.r.t. the initial condition of the process. In some cases, via the exact simulation of the diffusion, there is no time discretization error, otherwise the methods use Euler discretization. We illustrate our approach on both high- and low-dimensional problems from optimal control and establish that our approach substantially outperforms standard Monte Carlo methods typically adopted in the literature. The methods developed here are appropriate for solving a certain class of partial differential equations as well as for option pricing and hedging.


Author(s):  
Ranjan S. Mehta ◽  
Anquan Wang ◽  
Michael F. Modest ◽  
Daniel C. Haworth

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