Learning to steer nonlinear interior-point methods

Interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters. The specific barrier update rule strongly influences the theoretical convergence properties as well as the practical efficiency. While many global...

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Autor principal:	Renke Kuhlmann
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2019
Materias:	49M37 68T05 60J20 90C30 90C51 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/9c6ab9943b5b419ab8777f61af05c91c
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id	oai:doaj.org-article:9c6ab9943b5b419ab8777f61af05c91c
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spelling	oai:doaj.org-article:9c6ab9943b5b419ab8777f61af05c91c2021-12-02T05:01:13ZLearning to steer nonlinear interior-point methods2192-440610.1007/s13675-019-00118-4https://doaj.org/article/9c6ab9943b5b419ab8777f61af05c91c2019-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2192440621001222https://doaj.org/toc/2192-4406Interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters. The specific barrier update rule strongly influences the theoretical convergence properties as well as the practical efficiency. While many global and local convergence analyses consider a monotone update that decreases the barrier parameter for every approximately solved subprogram, computational studies show a superior performance of more adaptive strategies. In this paper we interpret the adaptive barrier update as a reinforcement learning task. A deep Q-learning agent is trained by both imitation and random action selection. Numerical results based on an implementation within the nonlinear programming solver WORHP show that the agent successfully learns to steer the barrier parameter and additionally improves WORHP’s performance on the CUTEst test set.Renke KuhlmannElsevierarticle49M3768T0560J2090C3090C51Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 7, Iss 4, Pp 381-419 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	49M37 68T05 60J20 90C30 90C51 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	49M37 68T05 60J20 90C30 90C51 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 Renke Kuhlmann Learning to steer nonlinear interior-point methods
description	Interior-point or barrier methods handle nonlinear programs by sequentially solving barrier subprograms with a decreasing sequence of barrier parameters. The specific barrier update rule strongly influences the theoretical convergence properties as well as the practical efficiency. While many global and local convergence analyses consider a monotone update that decreases the barrier parameter for every approximately solved subprogram, computational studies show a superior performance of more adaptive strategies. In this paper we interpret the adaptive barrier update as a reinforcement learning task. A deep Q-learning agent is trained by both imitation and random action selection. Numerical results based on an implementation within the nonlinear programming solver WORHP show that the agent successfully learns to steer the barrier parameter and additionally improves WORHP’s performance on the CUTEst test set.
format	article
author	Renke Kuhlmann
author_facet	Renke Kuhlmann
author_sort	Renke Kuhlmann
title	Learning to steer nonlinear interior-point methods
title_short	Learning to steer nonlinear interior-point methods
title_full	Learning to steer nonlinear interior-point methods
title_fullStr	Learning to steer nonlinear interior-point methods
title_full_unstemmed	Learning to steer nonlinear interior-point methods
title_sort	learning to steer nonlinear interior-point methods
publisher	Elsevier
publishDate	2019
url	https://doaj.org/article/9c6ab9943b5b419ab8777f61af05c91c
work_keys_str_mv	AT renkekuhlmann learningtosteernonlinearinteriorpointmethods
_version_	1718400843510710272

Learning to steer nonlinear interior-point methods

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