Understanding non-convex optimization problems and stochastic optimization algorithms

This thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different mach...

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Detalles Bibliográficos
Autor: Arza, E.
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Basque Center for Applied Mathematics (BCAM)
Repositorio:BIRD. BCAM's Institutional Repository Data
OAI Identifier:oai:bird.bcamath.org:20.500.11824/1836
Acceso en línea:http://hdl.handle.net/20.500.11824/1836
http://hdl.handle.net/10810/66155
Access Level:acceso abierto
Palabra clave:artificial intelligence
informatics
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spelling Understanding non-convex optimization problems and stochastic optimization algorithmsArza, E.artificial intelligenceinformaticsThis thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different machines, ensuring an equitable allocation of computational resources. Additionally, a methodology based on stochastic dominance is introduced to compare the performance of optimisation algorithms as random variables. Furthermore, the relationship between Hamming distance and the quadratic assignment problem is analysed. A general early stopping method for learning policies in episodic problems, which does not require specific problem information, is developed. In summary, this thesis contributes to the understanding and improvement of iterative stochastic heuristics in the field of optimisation.202420242023info:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/20.500.11824/1836http://hdl.handle.net/10810/66155reponame:BIRD. BCAM's Institutional Repository Datainstname:Basque Center for Applied Mathematics (BCAM)InglésReconocimiento-NoComercial-CompartirIgual 3.0 Españahttp://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:bird.bcamath.org:20.500.11824/18362026-06-19T12:47:47Z
dc.title.none.fl_str_mv Understanding non-convex optimization problems and stochastic optimization algorithms
title Understanding non-convex optimization problems and stochastic optimization algorithms
spellingShingle Understanding non-convex optimization problems and stochastic optimization algorithms
Arza, E.
artificial intelligence
informatics
title_short Understanding non-convex optimization problems and stochastic optimization algorithms
title_full Understanding non-convex optimization problems and stochastic optimization algorithms
title_fullStr Understanding non-convex optimization problems and stochastic optimization algorithms
title_full_unstemmed Understanding non-convex optimization problems and stochastic optimization algorithms
title_sort Understanding non-convex optimization problems and stochastic optimization algorithms
dc.creator.none.fl_str_mv Arza, E.
author Arza, E.
author_facet Arza, E.
author_role author
dc.subject.none.fl_str_mv artificial intelligence
informatics
topic artificial intelligence
informatics
description This thesis presents significant contributions in the field of iterative stochastic heuristics. Various aspects related to the comparison and improvement of optimisation algorithms are addressed. Firstly, a methodology is proposed to fairly compare the performance of algorithms run on different machines, ensuring an equitable allocation of computational resources. Additionally, a methodology based on stochastic dominance is introduced to compare the performance of optimisation algorithms as random variables. Furthermore, the relationship between Hamming distance and the quadratic assignment problem is analysed. A general early stopping method for learning policies in episodic problems, which does not require specific problem information, is developed. In summary, this thesis contributes to the understanding and improvement of iterative stochastic heuristics in the field of optimisation.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
info:eu-repo/semantics/publishedVersion
format doctoralThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.11824/1836
http://hdl.handle.net/10810/66155
url http://hdl.handle.net/20.500.11824/1836
http://hdl.handle.net/10810/66155
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento-NoComercial-CompartirIgual 3.0 España
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:BIRD. BCAM's Institutional Repository Data
instname:Basque Center for Applied Mathematics (BCAM)
instname_str Basque Center for Applied Mathematics (BCAM)
reponame_str BIRD. BCAM's Institutional Repository Data
collection BIRD. BCAM's Institutional Repository Data
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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