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On generation of test problems for linear programming codes
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Source
Communications of the ACM archive
Volume 17 ,  Issue 10  (October 1974) table of contents
Pages: 583 - 586  
Year of Publication: 1974
ISSN:0001-0782
Authors
A. Charnes  Univ. of Texas, Austin
W. M. Raike  The Naval Post Graduate School, Monterey
J. D. Stutz  Univ. of Texas, Austin
A. S. Walters  Carnegie-Mellon Univ., Pittsburgh, PA
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 5,   Downloads (12 Months): 41,   Citation Count: 2
Additional Information:

abstract   cited by   index terms   collaborative colleagues  

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ABSTRACT

Users of linear programming computer codes have realized the necessity of evaluating the capacity, effectiveness, and accuracy of the solutions provided by such codes. Large scale linear programming codes at most installations are assumed to be generating correct solutions without ever having been “bench-marked” by test problems with known solutions. The reason for this failure to adequately test the codes is that rarely are there large problems with known solutions readily available. This paper presents a theoretical justification and an Illustrative implementation of a method for generating linear programming test problems with known solutions. The method permits the generation of test problems that are of arbitrary size and have a wide range of numerical characteristics.


CITED BY  2
William M. Michaels , Richard P. O'Neill, A Mathematical Program Generator MPGENR, ACM Transactions on Mathematical Software (TOMS), v.6 n.1, p.31-44, March 1980
Benjamin W. Y. Lin , Ronald L. Rardin, Development of a Parametric Generating Procedure for Integer Programming Test Problems, Journal of the ACM (JACM), v.24 n.3, p.465-472, July 1977

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Linear programming


General Terms:
Performance, Reliability


Keywords:
LP program evaluation, LP program validation, linear programming, test problem generation

Collaborative Colleagues:
A. Charnes: colleagues
W. M. Raike: colleagues
J. D. Stutz: colleagues
A. S. Walters: colleagues

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