TY  - CONF
Y1  - 2015///
A1  - Sampathirao, Ajay Kumar
A1  - Sopasakis, Pantelis
A1  - Bemporad, Alberto
A1  - Patrinos, Panagiotis
T2  - 54th IEEE Conference on Decision and Control
PB  - IEEE
UR  - http://eprints.imtlucca.it/2779/
N2  - Stochastic optimal control problems arise in many applications and are, in principle, large-scale involving up to millions of decision variables. Their applicability in control applications is often limited by the availability of algorithms that can solve them efficiently and within  the sampling time of the controlled system.

In this paper we propose a dual accelerated proximal gradient algorithm which is amenable to parallelization and demonstrate that its GPU implementation affords high speed-up values (with respect to a CPU implementation) and greatly outperforms well-established commercial optimizers such as Gurobi.
AV  - public
TI  - Distributed solution of stochastic optimal control problems on GPUs
M2  - Osaka, Japan
ID  - eprints2779
ER  -