eprintid: 3544
rev_number: 8
eprint_status: archive
userid: 69
dir: disk0/00/00/35/44
datestamp: 2016-10-04 08:44:12
lastmod: 2016-10-04 08:44:12
status_changed: 2016-10-04 08:44:12
type: monograph
metadata_visibility: no_search
creators_name: Sampathirao, Ajay Kumar
creators_name: Sopasakis, Pantelis
creators_name: Bemporad, Alberto
creators_name: Patrinos, Panagiotis
creators_id: 
creators_id: pantelis.sopasakis@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
title: GPU-accelerated stochastic predictive control of drinking water networks
ispublished: submitted
subjects: QA75
divisions: CSA
full_text_status: public
monograph_type: working_paper
abstract: 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.
date: 2016
date_type: published
publisher: arXiv
pages: 11
id_number: arXiv:1604.01074
institution: IMT Institute for Advanced Studies Lucca
official_url: https://arxiv.org/abs/1604.01074
citation:   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)   
document_url: http://eprints.imtlucca.it/3544/1/1604.01074v1.pdf