eprintid: 3645
rev_number: 8
eprint_status: archive
userid: 69
dir: disk0/00/00/36/45
datestamp: 2017-01-26 14:46:16
lastmod: 2017-01-26 14:46:16
status_changed: 2017-01-26 14:46:16
type: conference_item
metadata_visibility: show
creators_name: Themelis, A.
creators_name: Villa, S.
creators_name: Patrinos, Panagiotis
creators_name: Bemporad, Alberto
creators_id: 
creators_id: 
creators_id: 
creators_id: alberto.bemporad@imtlucca.it
title: Stochastic gradient methods for stochastic model predictive control
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
pres_type: paper
keywords: Context;Convergence;Gradient methods;Indexes;Measurement;Predictive control;Stochastic processes
abstract: We introduce a new stochastic gradient algorithm, SAAGA, and investigate its employment for solving Stochastic MPC problems and multi-stage stochastic optimization programs in general. The method is particularly attractive for scenario-based formulations that involve a large number of scenarios, for which “batch” formulations may become inefficient due to high computational costs. Benefits of the method include cheap computations per iteration and fast convergence due to the sparsity of the proposed problem decomposition.
date: 2016-06
date_type: published
publisher: IEEE
pagerange: 154-159
event_title: 2016 European Control Conference (ECC)
event_location: Aalborg, Denmark
event_dates: June 29 - July 1, 2016
event_type: conference
id_number: 10.1109/ECC.2016.7810279
refereed: TRUE
isbn: 978-1-5090-2591-6
book_title: European Control Conference (ECC), 2016
official_url: https://doi.org/10.1109/ECC.2016.7810279
citation:   Themelis, A. and Villa, S. and Patrinos, Panagiotis and Bemporad, Alberto  Stochastic gradient methods for stochastic model predictive control.  In: 2016 European Control Conference (ECC), June 29 - July 1, 2016, Aalborg, Denmark pp. 154-159.  ISBN 978-1-5090-2591-6.      (2016)  
document_url: http://eprints.imtlucca.it/3645/1/ecc16-sg-mpc.pdf