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Stochastic gradient methods for stochastic model predictive control

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)

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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.

Item Type: Conference or Workshop Item (Paper)
Identification Number: 10.1109/ECC.2016.7810279
Uncontrolled Keywords: Context;Convergence;Gradient methods;Indexes;Measurement;Predictive control;Stochastic processes
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 26 Jan 2017 14:46
Last Modified: 26 Jan 2017 14:46
URI: http://eprints.imtlucca.it/id/eprint/3645

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