Integrated Value Function Global Optimization Approaches for Two-Stage Stochastic Programs

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dc.contributor.advisorSchaefer, Andrew J.
dc.contributor.committeeMemberHicks, Illya V.
dc.contributor.committeeMemberHuchette, Joey
dc.contributor.committeeMemberOzaltın, Osman Y.
dc.creatorAntley, Eric M
dc.date.accessioned2021-06-04T14:46:45Z
dc.date.available2022-05-01T05:01:14Z
dc.date.created2021-05
dc.date.issued2021-05-27
dc.date.submittedMay 2021
dc.date.updated2021-06-04T14:46:46Z
dc.description.abstractWe consider a class of nonnegative two-stage stochastic integer programs with discretely distributed right-hand sides. We develop a global optimization algorithm that exploits the structure of the superadditive dual for mixed-integer programs to search for the optimal solution. We demonstrate that our algorithm is capable of solving instances with large scenario counts to optimality.
dc.embargo.terms2022-05-01
dc.format.mimetypeapplication/pdf
dc.identifier.citationAntley, Eric M. "Integrated Value Function Global Optimization Approaches for Two-Stage Stochastic Programs." (2021) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/110654">https://hdl.handle.net/1911/110654</a>.
dc.identifier.urihttps://hdl.handle.net/1911/110654
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectStochastic Programming
dc.subjectMixed-Intger Programming
dc.subjectSuper-additive
dc.subjectDuality·Non-convex Optimization
dc.titleIntegrated Value Function Global Optimization Approaches for Two-Stage Stochastic Programs
dc.typeThesis
dc.type.materialText
thesis.degree.departmentComputational and Applied Mathematics
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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