Show simple item record

dc.contributor.author
Lygeros, John
dc.contributor.author
Margellos, Kostas
dc.contributor.author
Prandini, Maria
dc.contributor.editor
Muñoz de la Peña, David
dc.contributor.editor
Limón, Daniel
dc.date.accessioned
2021-07-28T08:01:45Z
dc.date.available
2017-06-11T23:55:10Z
dc.date.available
2018-09-11T08:48:56Z
dc.date.available
2021-07-28T08:01:45Z
dc.date.issued
2015-10
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2015.11.297
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/112166
dc.description.abstract
Motivated by chance constrained optimisation problems that arise in stochastic model predictive control we investigate the connections between compression learning and scenario based optimisation. We discuss how compression learning provides powerful insight into a fundamental property that ensures optimal solutions to optimisation problems formulated using a finite number of realisations of the uncertainty will also be feasible for other, unseen instances of the uncertainty. This property, known as -consistency-, roughly translates to the requirement that a fixed cardinality subset of the scenarios used to generate the optimal solution are enough to encode all the information needed to reconstruct the solution; all remaining scenarios are in a sense redundant. Computationally the catch of course is it is impossible to know a-priori which of the scenarios will be essential and which not. Moreover, the -unnecessary- scenarios are not wasted even in theory: Their presence is what provides the confidence level with which we can make the statement that the solution is feasible for unseen uncertainty instances. We demonstrate this connection through chance constrained optimisation programs based on a combination of scenarios and robust optimisation.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
Model predictive control
en_US
dc.subject
Stochastic systems
en_US
dc.subject
Machine learning
en_US
dc.subject
Optimisation Problems
en_US
dc.subject
Randomised optimisation
en_US
dc.title
Compression learning for chance constrained stochastic MPC
en_US
dc.type
Conference Paper
dc.date.published
2015-12-17
ethz.book.title
5th IFAC Conference on Nonlinear Model Predictive Control, NMPC 2015. Proceedings
en_US
ethz.journal.title
IFAC-PapersOnLine
ethz.journal.volume
48
en_US
ethz.journal.issue
23
en_US
ethz.pages.start
286
en_US
ethz.pages.end
293
en_US
ethz.event
5th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2015)
en_US
ethz.event.location
Seville, Spain
en_US
ethz.event.date
September 17-20, 2015
en_US
ethz.identifier.scopus
ethz.publication.place
Kidlington
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::03751 - Lygeros, John / Lygeros, John
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::03751 - Lygeros, John / Lygeros, John
ethz.date.deposited
2017-06-11T23:58:32Z
ethz.source
ECIT
ethz.identifier.importid
imp5936541023f9438423
ethz.ecitpid
pub:173675
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2017-07-19T01:33:43Z
ethz.rosetta.lastUpdated
2022-03-29T10:45:56Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Compression%20learning%20for%20chance%20constrained%20stochastic%20MPC&rft.jtitle=IFAC-PapersOnLine&rft.date=2015-10&rft.volume=48&rft.issue=23&rft.spage=286&rft.epage=293&rft.issn=2405-8963&rft.au=Lygeros,%20John&Margellos,%20Kostas&Prandini,%20Maria&rft.genre=proceeding&rft_id=info:doi/10.1016/j.ifacol.2015.11.297&rft.btitle=5th%20IFAC%20Conference%20on%20Nonlinear%20Model%20Predictive%20Control,%20NMPC%202015.%20Proceedings
 Search print copy at ETH Library

Files in this item

FilesSizeFormatOpen in viewer

There are no files associated with this item.

Publication type

Show simple item record