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dc.contributor.authorYamangil, Emre
dc.contributor.authorBent, Russell
dc.contributor.authorLubin, Miles C
dc.contributor.authorVielma Centeno, Juan Pablo
dc.date.accessioned2019-03-22T19:15:58Z
dc.date.available2019-03-22T19:15:58Z
dc.date.issued2016-05
dc.date.submitted2016-03
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/1721.1/121068
dc.description.abstractWe present a unifying framework for generating extended formulations for the polyhedral outer approximations used in algorithms for mixed-integer convex programming (MICP). Extended formulations lead to fewer iterations of outer approximation algorithms and generally faster solution times. First, we observe that all MICP instances from the MINLPLIB2 benchmark library are conic representable with standard symmetric and nonsymmetric cones. Conic reformulations are shown to be effective extended formulations themselves because they encode separability structure. For mixed-integer conic-representable problems, we provide the first outer approximation algorithm with finite-time convergence guarantees, opening a path for the use of conic solvers for continuous relaxations. We then connect the popular modeling framework of disciplined convex programming (DCP) to the existence of extended formulations independent of conic representability. We present evidence that our approach can yield significant gains in practice, with the solution of a number of open instances from the MINLPLIB2 benchmark library.en_US
dc.description.sponsorshipUnited States. Department of Energy. Computational Science Graduate Fellowship Program (Grant DE-FG02-97ER25308)en_US
dc.description.sponsorshipUnited States. National Science Foundation. (Grant CMMI-1351619)en_US
dc.publisherSpringer-Verlagen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/978-3-319-33461-5_9en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleExtended Formulations in Mixed-Integer Convex Programmingen_US
dc.typeArticleen_US
dc.identifier.citationLubin, Miles et al. “Extended Formulations in Mixed-Integer Convex Programming.” International Conference on Integer Programming and Combinatorial Optimization (2016): 102–113. doi:10.1007/978-3-319-33461-5_9. © 2016 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.mitauthorLubin, Miles C
dc.contributor.mitauthorVielma Centeno, Juan Pablo
dc.relation.journalInternational Conference on Integer Programming and Combinatorial Optimizationen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-03-05T16:41:48Z
dspace.orderedauthorsLubin, Miles; Yamangil, Emre; Bent, Russell; Vielma, Juan Pabloen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-6781-9633
dc.identifier.orcidhttps://orcid.org/0000-0003-4335-7248
mit.licenseOPEN_ACCESS_POLICYen_US


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