Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data

Autores
Rebollo Neira, Laura; Plastino, Ángel Luis
Año de publicación
2002
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A recursive approach for computing the q = 1/2 nonextensive maximum entropy distribution of the previously introduced formalism for data subset selection is proposed. Such an approach is based on an iterative biorthogonalization technique, which allows for the incorporation of the Lagrange multipliers that determine the distribution to the workings of the algorithm devised for selecting relevant data subsets. This technique circumvents the necessity of inverting operators and yields a recursive procedure to appropriately modify the Lagrange multipliers so as to account for each new constraint.
Instituto de Física La Plata
Materia
Física
Joint entropy
Algorithm
Mathematical optimization
Mathematics
Joint quantum entropy
Binary entropy function
Maximum entropy thermodynamics
Maximum entropy probability distribution
Lagrange multiplier
Maximum entropy spectral estimation
Principle of maximum entropy
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125907
Acceder
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network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant dataRebollo Neira, LauraPlastino, Ángel LuisFísicaJoint entropyAlgorithmMathematical optimizationMathematicsJoint quantum entropyBinary entropy functionMaximum entropy thermodynamicsMaximum entropy probability distributionLagrange multiplierMaximum entropy spectral estimationPrinciple of maximum entropyA recursive approach for computing the q = 1/2 nonextensive maximum entropy distribution of the previously introduced formalism for data subset selection is proposed. Such an approach is based on an iterative biorthogonalization technique, which allows for the incorporation of the Lagrange multipliers that determine the distribution to the workings of the algorithm devised for selecting relevant data subsets. This technique circumvents the necessity of inverting operators and yields a recursive procedure to appropriately modify the Lagrange multipliers so as to account for each new constraint.Instituto de Física La Plata2002info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/125907enginfo:eu-repo/semantics/altIdentifier/issn/1063-651Xinfo:eu-repo/semantics/altIdentifier/issn/1095-3787info:eu-repo/semantics/altIdentifier/doi/10.1103/physreve.66.032102info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:19Zoai:sedici.unlp.edu.ar:10915/125907Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:19.805SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
title Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
spellingShingle Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
Rebollo Neira, Laura
Física
Joint entropy
Algorithm
Mathematical optimization
Mathematics
Joint quantum entropy
Binary entropy function
Maximum entropy thermodynamics
Maximum entropy probability distribution
Lagrange multiplier
Maximum entropy spectral estimation
Principle of maximum entropy
title_short Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
title_full Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
title_fullStr Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
title_full_unstemmed Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
title_sort Recursive approach for constructing the q = 1/2 maximum entropy distribution from redundant data
dc.creator.none.fl_str_mv Rebollo Neira, Laura
Plastino, Ángel Luis
author Rebollo Neira, Laura
author_facet Rebollo Neira, Laura
Plastino, Ángel Luis
author_role author
author2 Plastino, Ángel Luis
author2_role author
dc.subject.none.fl_str_mv Física
Joint entropy
Algorithm
Mathematical optimization
Mathematics
Joint quantum entropy
Binary entropy function
Maximum entropy thermodynamics
Maximum entropy probability distribution
Lagrange multiplier
Maximum entropy spectral estimation
Principle of maximum entropy
topic Física
Joint entropy
Algorithm
Mathematical optimization
Mathematics
Joint quantum entropy
Binary entropy function
Maximum entropy thermodynamics
Maximum entropy probability distribution
Lagrange multiplier
Maximum entropy spectral estimation
Principle of maximum entropy
dc.description.none.fl_txt_mv A recursive approach for computing the q = 1/2 nonextensive maximum entropy distribution of the previously introduced formalism for data subset selection is proposed. Such an approach is based on an iterative biorthogonalization technique, which allows for the incorporation of the Lagrange multipliers that determine the distribution to the workings of the algorithm devised for selecting relevant data subsets. This technique circumvents the necessity of inverting operators and yields a recursive procedure to appropriately modify the Lagrange multipliers so as to account for each new constraint.
Instituto de Física La Plata
description A recursive approach for computing the q = 1/2 nonextensive maximum entropy distribution of the previously introduced formalism for data subset selection is proposed. Such an approach is based on an iterative biorthogonalization technique, which allows for the incorporation of the Lagrange multipliers that determine the distribution to the workings of the algorithm devised for selecting relevant data subsets. This technique circumvents the necessity of inverting operators and yields a recursive procedure to appropriately modify the Lagrange multipliers so as to account for each new constraint.
publishDate 2002
dc.date.none.fl_str_mv 2002
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/125907
url http://sedici.unlp.edu.ar/handle/10915/125907
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1063-651X
info:eu-repo/semantics/altIdentifier/issn/1095-3787
info:eu-repo/semantics/altIdentifier/doi/10.1103/physreve.66.032102
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.13397

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