Solving control problems with linear state dynamics - A practical user guide

Hinz, J; Yee, J

Solving control problems with linear state dynamics - A practical user guide

Hinz, J Yee, J

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016, 2016, pp. 591 - 596
Issue Date:: 2016-03-11

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Field	Value	Language
dc.contributor.author	Hinz, J	en_US
dc.contributor.author	Yee, J	en_US
dc.date.issued	2016-03-11	en_US
dc.identifier.citation	Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016, 2016, pp. 591 - 596	en_US
dc.identifier.isbn	9781467399418	en_US
dc.identifier.uri	http://hdl.handle.net/10453/72035
dc.description.abstract	© 2016 IEEE. In industrial applications, practitioners usually facea considerable complexity when optimizing operating strategiesunder uncertainty. Typical real-world problems arising in practiceare notoriously challenging from a computational viewpoint, requiring solutions to Markov Decision problems in high dimensions. In this work, we address a novel approach to obtain anapproximate solution to a certain class of problems, whose stateprocess follows a controlled linear dynamics. Our techniques isillustrated by an implementation within the statistical languageR, which we discuss by solving a typical problem arising in practice.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP130103315
dc.relation.ispartof	Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016	en_US
dc.relation.isbasedon	10.1109/SMRLO.2016.103	en_US
dc.title	Solving control problems with linear state dynamics - A practical user guide	en_US
dc.type	Conference Proceeding
utslib.for	0102 Applied Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In industrial applications, practitioners usually facea considerable complexity when optimizing operating strategiesunder uncertainty. Typical real-world problems arising in practiceare notoriously challenging from a computational viewpoint, requiring solutions to Markov Decision problems in high dimensions. In this work, we address a novel approach to obtain anapproximate solution to a certain class of problems, whose stateprocess follows a controlled linear dynamics. Our techniques isillustrated by an implementation within the statistical languageR, which we discuss by solving a typical problem arising in practice.

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http://hdl.handle.net/10453/72035