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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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.
| Item Type: | Conference or Workshop Item (Paper) |
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| Identification Number: | https://doi.org/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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