Automatic adjoint differentiation for gradient descent and model calibration

Detalhes bibliográficos
Autor(a) principal: Goloubentsev, Dmitri
Data de Publicação: 2020
Outros Autores: Lakshtanov, Evgeny
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10773/29044
Resumo: In this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions of the form G=12∑m1(Eyi−Ci)2, which often appear in the calibration of stochastic models. We demonstrate that it allows a perfect SIMDa parallelization and provides its relative computational cost. In addition, we demonstrate that this theoretical result is in concordance with numerical experiments. a Single Input Multiple Data.
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spelling Automatic adjoint differentiation for gradient descent and model calibrationAutomatic adjoint differentiationAutomatic vectorizationSingle instruction multiple dataAAD-compilerIn this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions of the form G=12∑m1(Eyi−Ci)2, which often appear in the calibration of stochastic models. We demonstrate that it allows a perfect SIMDa parallelization and provides its relative computational cost. In addition, we demonstrate that this theoretical result is in concordance with numerical experiments. a Single Input Multiple Data.World Scientific Publishing2020-08-13T10:14:31Z2020-07-14T00:00:00Z2020-07-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/29044eng0219-691310.1142/S0219691320400044Goloubentsev, DmitriLakshtanov, Evgenyinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:56:09Zoai:ria.ua.pt:10773/29044Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:27.846599Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Automatic adjoint differentiation for gradient descent and model calibration
title Automatic adjoint differentiation for gradient descent and model calibration
spellingShingle Automatic adjoint differentiation for gradient descent and model calibration
Goloubentsev, Dmitri
Automatic adjoint differentiation
Automatic vectorization
Single instruction multiple data
AAD-compiler
title_short Automatic adjoint differentiation for gradient descent and model calibration
title_full Automatic adjoint differentiation for gradient descent and model calibration
title_fullStr Automatic adjoint differentiation for gradient descent and model calibration
title_full_unstemmed Automatic adjoint differentiation for gradient descent and model calibration
title_sort Automatic adjoint differentiation for gradient descent and model calibration
author Goloubentsev, Dmitri
author_facet Goloubentsev, Dmitri
Lakshtanov, Evgeny
author_role author
author2 Lakshtanov, Evgeny
author2_role author
dc.contributor.author.fl_str_mv Goloubentsev, Dmitri
Lakshtanov, Evgeny
dc.subject.por.fl_str_mv Automatic adjoint differentiation
Automatic vectorization
Single instruction multiple data
AAD-compiler
topic Automatic adjoint differentiation
Automatic vectorization
Single instruction multiple data
AAD-compiler
description In this work, we discuss the Automatic Adjoint Differentiation (AAD) for functions of the form G=12∑m1(Eyi−Ci)2, which often appear in the calibration of stochastic models. We demonstrate that it allows a perfect SIMDa parallelization and provides its relative computational cost. In addition, we demonstrate that this theoretical result is in concordance with numerical experiments. a Single Input Multiple Data.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-13T10:14:31Z
2020-07-14T00:00:00Z
2020-07-14
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10773/29044
url http://hdl.handle.net/10773/29044
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0219-6913
10.1142/S0219691320400044
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv World Scientific Publishing
publisher.none.fl_str_mv World Scientific Publishing
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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