relation: http://eprints.imtlucca.it/3544/
title: GPU-accelerated stochastic predictive control of drinking water networks
creator: Sampathirao, Ajay Kumar
creator: Sopasakis, Pantelis
creator: Bemporad, Alberto
creator: Patrinos, Panagiotis
subject: QA75 Electronic computers. Computer science
description: Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona.
publisher: arXiv
date: 2016
type: Working Paper
type: NonPeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/3544/1/1604.01074v1.pdf
identifier:   Sampathirao, Ajay Kumar and Sopasakis, Pantelis and Bemporad, Alberto and Patrinos, Panagiotis  GPU-accelerated stochastic predictive control of drinking water networks.  Working Paper   arXiv       (Submitted)   
relation: https://arxiv.org/abs/1604.01074
relation: arXiv:1604.01074