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Analyzing Infeasible Mixed-Integer and Integer Linear Programs.

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Τίτλος: Analyzing Infeasible Mixed-Integer and Integer Linear Programs.
Συγγραφείς: Guieu, Olivier, Chinneck, John W.
Πηγή: INFORMS Journal on Computing; Winter99, Vol. 11 Issue 1, p63, 15p, 1 Diagram, 1 Graph
Θεματικοί όροι: Algorithms, Integer programming, Linear programming
Περίληψη: Algorithms and computer-based tools for analyzing infeasible linear and nonlinear programs have been developed in recent years, but few such tools exist for infeasible mixed-integer or integer linear programs. One approach that has proven especially useful for infeasible linear programs is the isolation of an Irreducible Infeasible Set of constraints (IIS), a subset of the constraints defining the overall linear program that is itself infeasible, but for which any proper subset is feasible. Isolating an IIS from the larger model speeds the diagnosis and repair of the model by focussing the analytic effort. This paper describes and tests algorithms for finding small infeasible sets in infeasible mixed-integer and integer linear programs; where possible these small sets are IISs. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Analyzing Infeasible Mixed-Integer and Integer Linear Programs.
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  Data: INFORMS Journal on Computing; Winter99, Vol. 11 Issue 1, p63, 15p, 1 Diagram, 1 Graph
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  Data: Algorithms and computer-based tools for analyzing infeasible linear and nonlinear programs have been developed in recent years, but few such tools exist for infeasible mixed-integer or integer linear programs. One approach that has proven especially useful for infeasible linear programs is the isolation of an Irreducible Infeasible Set of constraints (IIS), a subset of the constraints defining the overall linear program that is itself infeasible, but for which any proper subset is feasible. Isolating an IIS from the larger model speeds the diagnosis and repair of the model by focussing the analytic effort. This paper describes and tests algorithms for finding small infeasible sets in infeasible mixed-integer and integer linear programs; where possible these small sets are IISs. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1287/ijoc.11.1.63
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 63
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Integer programming
        Type: general
      – SubjectFull: Linear programming
        Type: general
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      – TitleFull: Analyzing Infeasible Mixed-Integer and Integer Linear Programs.
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            NameFull: Guieu, Olivier
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              Text: Winter99
              Type: published
              Y: 1999
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              Value: 11
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              Value: 1
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