relation: http://eprints.imtlucca.it/2779/
title: Distributed solution of stochastic optimal control problems on GPUs
creator: Sampathirao, Ajay Kumar
creator: Sopasakis, Pantelis
creator: Bemporad, Alberto
creator: Patrinos, Panagiotis
subject: QA Mathematics
subject: QA75 Electronic computers. Computer science
subject: T Technology (General)
subject: TK Electrical engineering. Electronics Nuclear engineering
description: 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.
publisher: IEEE
date: 2015
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/2779/1/APG_GPU.pdf
identifier:   Sampathirao, Ajay Kumar and Sopasakis, Pantelis and Bemporad, Alberto and Patrinos, Panagiotis  Distributed solution of stochastic optimal control problems on GPUs.  In: 54th IEEE Conference on Decision and Control, 15-18 December, 2015, Osaka, Japan         (2015)