Algorithmic solutions for optimal switching problems

Hinz, J; Yee, J

Algorithmic solutions for optimal switching problems

Hinz, J Yee, J

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016, 2016, pp. 586 - 590
Issue Date:: 2016-03-11

Closed Access

	Filename	Description	Size
	paper.pdf	Published version	211.62 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

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. 586 - 590	en_US
dc.identifier.isbn	9781467399418	en_US
dc.identifier.uri	http://hdl.handle.net/10453/103405
dc.description.abstract	© 2016 IEEE. In practice, optimal control problems of stochastic switching are notoriously challenging from a computational viewpoint, since typical real-world applications are high dimensional. In this approach, we suggest an algorithmic solution which is based on some convexity assumptions frequently fulfilled in applications. Furthermore, we show how the quality of numerical solution can be assessed. An efficient implementation of our algorithms is discussed.	en_US
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.2016.102	en_US
dc.title	Algorithmic solutions for optimal switching problems	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 practice, optimal control problems of stochastic switching are notoriously challenging from a computational viewpoint, since typical real-world applications are high dimensional. In this approach, we suggest an algorithmic solution which is based on some convexity assumptions frequently fulfilled in applications. Furthermore, we show how the quality of numerical solution can be assessed. An efficient implementation of our algorithms is discussed.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/103405