Inexact restoration approaches to solve mathematical program with complementarity constraints

Bibliographic Details
Main Author: Melo, Teófilo M. M.
Publication Date: 2012
Other Authors: Matias, João, Monteiro, M. Teresa T.
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/37118
Summary: Mathematical Program with Complementarity Constraints (MPCC) finds application in many fields. As the complementarity constraints fail the standard Linear Independence Constraint Qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ), at any feasible point, the nonlinear programming theory may not be directly applied to MPCC. However, the MPCC can be reformulated as NLP problem and solved by nonlinear programming techniques. One of them, the Inexact Restoration (IR) approach, performs two independent phases in each iteration - the feasibility and the optimality phases. This work presents two versions of an IR algorithm to solve MPCC. In the feasibility phase two strategies were implemented, depending on the constraints features. One gives more importance to the complementarity constraints, while the other considers the priority of equality and inequality constraints neglecting the complementarity ones. The optimality phase uses the same approach for both algorithm versions. The algorithms were implemented in MATLAB and the test problems are from MACMPEC collection.
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spelling Inexact restoration approaches to solve mathematical program with complementarity constraintsMathematical Problem with Complementarity ConstraintsInexact RestorationNonLinear ProgrammingMathematical Program with Complementarity Constraints (MPCC) finds application in many fields. As the complementarity constraints fail the standard Linear Independence Constraint Qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ), at any feasible point, the nonlinear programming theory may not be directly applied to MPCC. However, the MPCC can be reformulated as NLP problem and solved by nonlinear programming techniques. One of them, the Inexact Restoration (IR) approach, performs two independent phases in each iteration - the feasibility and the optimality phases. This work presents two versions of an IR algorithm to solve MPCC. In the feasibility phase two strategies were implemented, depending on the constraints features. One gives more importance to the complementarity constraints, while the other considers the priority of equality and inequality constraints neglecting the complementarity ones. The optimality phase uses the same approach for both algorithm versions. The algorithms were implemented in MATLAB and the test problems are from MACMPEC collection.Universidade do MinhoMelo, Teófilo M. M.Matias, JoãoMonteiro, M. Teresa T.20122012-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/37118eng978-84-615-5392-1http://cmmse.usal.es/cmmse2015/images/stories/congreso/3-cmmse-2012.pdfinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T05:09:44Zoai:repositorium.sdum.uminho.pt:1822/37118Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:09:58.604316Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Inexact restoration approaches to solve mathematical program with complementarity constraints
title Inexact restoration approaches to solve mathematical program with complementarity constraints
spellingShingle Inexact restoration approaches to solve mathematical program with complementarity constraints
Melo, Teófilo M. M.
Mathematical Problem with Complementarity Constraints
Inexact Restoration
NonLinear Programming
title_short Inexact restoration approaches to solve mathematical program with complementarity constraints
title_full Inexact restoration approaches to solve mathematical program with complementarity constraints
title_fullStr Inexact restoration approaches to solve mathematical program with complementarity constraints
title_full_unstemmed Inexact restoration approaches to solve mathematical program with complementarity constraints
title_sort Inexact restoration approaches to solve mathematical program with complementarity constraints
author Melo, Teófilo M. M.
author_facet Melo, Teófilo M. M.
Matias, João
Monteiro, M. Teresa T.
author_role author
author2 Matias, João
Monteiro, M. Teresa T.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Melo, Teófilo M. M.
Matias, João
Monteiro, M. Teresa T.
dc.subject.por.fl_str_mv Mathematical Problem with Complementarity Constraints
Inexact Restoration
NonLinear Programming
topic Mathematical Problem with Complementarity Constraints
Inexact Restoration
NonLinear Programming
description Mathematical Program with Complementarity Constraints (MPCC) finds application in many fields. As the complementarity constraints fail the standard Linear Independence Constraint Qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ), at any feasible point, the nonlinear programming theory may not be directly applied to MPCC. However, the MPCC can be reformulated as NLP problem and solved by nonlinear programming techniques. One of them, the Inexact Restoration (IR) approach, performs two independent phases in each iteration - the feasibility and the optimality phases. This work presents two versions of an IR algorithm to solve MPCC. In the feasibility phase two strategies were implemented, depending on the constraints features. One gives more importance to the complementarity constraints, while the other considers the priority of equality and inequality constraints neglecting the complementarity ones. The optimality phase uses the same approach for both algorithm versions. The algorithms were implemented in MATLAB and the test problems are from MACMPEC collection.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
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url http://hdl.handle.net/1822/37118
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-84-615-5392-1
http://cmmse.usal.es/cmmse2015/images/stories/congreso/3-cmmse-2012.pdf
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