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Generalized Column Generation for Linear Programming

Column generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows tha... Full description

Journal Title: Management Science 2002, Vol.48 (3), p.444-452
Main Author: Ogtildeuz, Osman
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Linthicum: INFORMS
ID: ISSN: 0025-1909
Link: http://econpapers.repec.org/article/inmormnsc/v_3a48_3ay_3a2002_3ai_3a3_3ap_3a444-452.htm
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recordid: cdi_repec_primary_inmormnsc_v_3a48_3ay_3a2002_3ai_3a3_3ap_3a444_452_htm
title: Generalized Column Generation for Linear Programming
format: Article
creator:
  • Ogtildeuz, Osman
subjects:
  • Algorithms
  • Coefficients
  • Column generation
  • Experimentation
  • Integer programming
  • Linear programming
  • Management
  • Management science
  • Mathematical models
  • Mathematical vectors
  • Methods
  • Objective functions
  • Optimization
  • Simplex algorithm
  • Simplex method
  • Small scale optimization
  • Studies
  • Variable coefficients
  • Variables
  • Zero vectors
ispartof: Management Science, 2002, Vol.48 (3), p.444-452
description: Column generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows that, even if the number of variables is low enough for explicit inclusion in the model with the available technology, it may still be more efficient to resort to column generation for some class of problems.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0025-1909
fulltext: fulltext
issn:
  • 0025-1909
  • 1526-5501
url: Link


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descriptionColumn generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows that, even if the number of variables is low enough for explicit inclusion in the model with the available technology, it may still be more efficient to resort to column generation for some class of problems.
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subjectAlgorithms ; Coefficients ; Column generation ; Experimentation ; Integer programming ; Linear programming ; Management ; Management science ; Mathematical models ; Mathematical vectors ; Methods ; Objective functions ; Optimization ; Simplex algorithm ; Simplex method ; Small scale optimization ; Studies ; Variable coefficients ; Variables ; Zero vectors
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abstractColumn generation is a well-known and widely practiced technique for solving linear programs with too many variables or constraints to include in the initial formulation explicitly. Instead, the required column information is generated at each iteration of the simplex algorithm. This paper shows that, even if the number of variables is low enough for explicit inclusion in the model with the available technology, it may still be more efficient to resort to column generation for some class of problems.
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