relation: http://eprints.imtlucca.it/3645/
title: Stochastic gradient methods for stochastic model predictive control
creator: Themelis, A.
creator: Villa, S.
creator: Patrinos, Panagiotis
creator: Bemporad, Alberto
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: IEEE
date: 2016-06
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
rights: cc_by_nc
identifier: http://eprints.imtlucca.it/3645/1/ecc16-sg-mpc.pdf
identifier:   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)  
relation: https://doi.org/10.1109/ECC.2016.7810279
relation: 10.1109/ECC.2016.7810279