Stochastic gradient methods for stochastic model predictive control

Andreas Themelis, Silvia Villa, Panagiotis Patrinos, Alberto Bemporad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2016 European Control Conference, ECC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages154-159
Number of pages6
ISBN (Electronic)9781509025916
DOIs
Publication statusPublished - Jan 6 2017
Externally publishedYes
Event2016 European Control Conference, ECC 2016 - Aalborg, Denmark
Duration: Jun 29 2016Jul 1 2016

Publication series

Name2016 European Control Conference, ECC 2016

Other

Other2016 European Control Conference, ECC 2016
Country/TerritoryDenmark
CityAalborg
Period6/29/167/1/16

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Control and Optimization

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