Publicação
General purpose optimization library (Gpol)
| Resumo: | Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction). |
|---|---|
| Autores principais: | Bakurov, Illya |
| Outros Autores: | Buzzelli, Marco; Castelli, Mauro; Vanneschi, Leonardo; Schettini, Raimondo |
| Assunto: | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868984104854224896 |
|---|---|
| author | Bakurov, Illya |
| author2 | Buzzelli, Marco Castelli, Mauro Vanneschi, Leonardo Schettini, Raimondo |
| author2_role | author author author author |
| author_facet | Bakurov, Illya Buzzelli, Marco Castelli, Mauro Vanneschi, Leonardo Schettini, Raimondo |
| author_role | author |
| contributor_name_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School MDPI - Multidisciplinary Digital Publishing Institute RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Bakurov, Illya\"},{\"Person.name\":\"Buzzelli, Marco\"},{\"Person.name\":\"Castelli, Mauro\"},{\"Person.name\":\"Vanneschi, Leonardo\"},{\"Person.name\":\"Schettini, Raimondo\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School MDPI - Multidisciplinary Digital Publishing Institute RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Bakurov, Illya Buzzelli, Marco Castelli, Mauro Vanneschi, Leonardo Schettini, Raimondo |
| datacite.date.Accepted.fl_str_mv | 2021-06-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-06-14T22:16:30Z |
| datacite.date.embargoed.fl_str_mv | 2021-06-14T22:16:30Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| datacite.titles.title.fl_str_mv | General purpose optimization library (Gpol) A flexible and efficient multi-purpose optimization library in python |
| dc.contributor.none.fl_str_mv | NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School MDPI - Multidisciplinary Digital Publishing Institute RUN |
| dc.creator.none.fl_str_mv | Bakurov, Illya Buzzelli, Marco Castelli, Mauro Vanneschi, Leonardo Schettini, Raimondo |
| dc.date.Accepted.fl_str_mv | 2021-06-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-06-14T22:16:30Z |
| dc.date.embargoed.fl_str_mv | 2021-06-14T22:16:30Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/119248 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| dc.title.fl_str_mv | General purpose optimization library (Gpol) A flexible and efficient multi-purpose optimization library in python |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Several interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction). |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/ece38c64-c576-442a-b713-f7720cab564e/download |
| funder_facet_str_mv | FCT{{{_:::_}}}Fundação para a Ciência e a Tecnologia |
| funding.funder.alternateName_str_mv | FCT |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100001871 |
| funding.funder.name_str_mv | Fundação para a Ciência e a Tecnologia |
| funding.name_str_mv | 3599-PPCDT |
| id | run_35eb91ed8a55fdee00d50a0ea2b8b18e |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/119248 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/119248 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Bakurov, Illya Buzzelli, Marco Castelli, Mauro Vanneschi, Leonardo Schettini, Raimondo |
| publishDate | 2021 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engenSeveral interesting libraries for optimization have been proposed. Some focus on individual optimization algorithms, or limited sets of them, and others focus on limited sets of problems. Frequently, the implementation of one of them does not precisely follow the formal definition, and they are difficult to personalize and compare. This makes it difficult to perform comparative studies and propose novel approaches. In this paper, we propose to solve these issues with the General Purpose Optimization Library (GPOL): a flexible and efficient multipurpose optimization library that covers a wide range of stochastic iterative search algorithms, through which flexible and modular implementation can allow for solving many different problem types from the fields of continuous and combinatorial optimization and supervised machine learning problem solving. Moreover, the library supports full-batch and mini-batch learning and allows carrying out computations on a CPU or GPU. The package is distributed under an MIT license. Source code, installation instructions, demos and tutorials are publicly available in our code hosting platform (the reference is provided in the Introduction).application/pdfenGeneral purpose optimization library (Gpol)SubtitleenA flexible and efficient multi-purpose optimization library in pythonBakurov, IllyaBuzzelli, MarcoCastelli, MauroVanneschi, LeonardoSchettini, RaimondoNOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolMDPI - Multidisciplinary Digital Publishing InstituteHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptISSNIsPartOf2076-3417URNIsPartOfPURE: 31944364URNIsPartOfPURE UUID: d1d33036-22f7-400a-98a1-00764bff1d7dURNIsPartOfScopus: 85107299638URNIsPartOfORCID: /0000-0002-8793-1451/work/95498739URNIsPartOfWOS: 000659555700001URNIsPartOfORCID: /0000-0003-4732-3328/work/151426785DOIIsPartOf10.3390/app111147742021-06-14T22:16:30Z2021-06-012021-06-01T00:00:00ZHandlehttp://hdl.handle.net/10362/119248http://purl.org/coar/access_right/c_abf2open accessCombinatorial optimizationContinuous optimizationEvolutionary computationInductive programmingLocal searchOptimizationSupervised machine learningSwarm intelligenceGeneral Materials ScienceInstrumentationGeneral EngineeringProcess Chemistry and TechnologyComputer Science ApplicationsFluid Flow and Transfer Processes663852 bytesFundação para a Ciência e a TecnologiaData Science and Over-Indebtedness: Use of Artificial Intelligence Algorithms in Credit Consumption and Indebtedness Conciliation in Portugal3599-PPCDTCrossref Funder IDhttp://doi.org/10.13039/501100001871literaturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/ece38c64-c576-442a-b713-f7720cab564e/download |
| spellingShingle | General purpose optimization library (Gpol) Bakurov, Illya Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| status | SINGLETON |
| subject.fl_str_mv | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| title | General purpose optimization library (Gpol) |
| title_full | General purpose optimization library (Gpol) |
| title_fullStr | General purpose optimization library (Gpol) |
| title_full_unstemmed | General purpose optimization library (Gpol) |
| title_short | General purpose optimization library (Gpol) |
| title_sort | General purpose optimization library (Gpol) |
| topic | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| topic_facet | Combinatorial optimization Continuous optimization Evolutionary computation Inductive programming Local search Optimization Supervised machine learning Swarm intelligence General Materials Science Instrumentation General Engineering Process Chemistry and Technology Computer Science Applications Fluid Flow and Transfer Processes |
| url | http://hdl.handle.net/10362/119248 |
| visible | 1 |