• Robert J. Vanderbei
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 114)


In this chapter, we shall study an application of linear programming to an area of statistics called regression. As a specific example, we shall use size and iterationcount data collected from a standard suite of linear programming problems to derive a regression estimate of the number of iterations needed to solve problems of a given size.


Linear Programming Problem Simplex Method Facility Location Problem Exam Score Regular Employee 
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  1. Gay, D. (1985), ‘Electronic mail distribution of linear programming test problems’, Mathematical Programming Society COAL Newslettter 13, 10–12.Google Scholar
  2. Gonin, R. & Money, A. (1989), Nonlinear L p -Norm Estimation, Marcel Dekker, New York-Basel.MATHGoogle Scholar
  3. Dodge, Y., ed. (1987), Statistical Data Analysis Based on The L1-Norm and Related Methods, North-Holland, Amsterdam.Google Scholar
  4. Bloomfield, P. & Steiger,W. (1983), Least Absolute Deviations: Theory, Applications, and Algorithms, Birkhäuser, Boston.MATHGoogle Scholar
  5. Smale, S. (1983), ‘On the average number of steps of the simplex method of linear programming’, Mathematical Programming 27, 241–262.MATHCrossRefMathSciNetGoogle Scholar
  6. Borgwardt, K.-H. (1982), ‘The average number of pivot steps required by the simplexmethod is polynomial’, Zeitschrift fü Operations Research 26, 157–177.MATHCrossRefMathSciNetGoogle Scholar
  7. Borgwardt, K.-H. (1987a), Probabilistic analysis of the simplex method, in ‘Operations Research Proceedings, 16th DGOR Meeting’, pp. 564–576.Google Scholar
  8. Adler, I. & Megiddo, N. (1985), ‘A simplex algorithm whose average number of steps is bounded between two quadratic functions of the smaller dimension’, Journal of the ACM 32, 871–895.MATHCrossRefMathSciNetGoogle Scholar
  9. Todd, M. (1986), ‘Polynomial expected behavior of a pivoting algorithm for linear complementarity and linear programming’, Mathematical Programming 35, 173– 192.MATHCrossRefMathSciNetGoogle Scholar
  10. Adler, I. & Berenguer, S. (1981), Random linear programs, Technical Report 81-4, Operations Research Center Report, U.C. Berkeley.Google Scholar

Copyright information

© Robert J.Vanderbei 2008

Authors and Affiliations

  • Robert J. Vanderbei
    • 1
  1. 1.Dept. of Operations Research and Financial EngineeringPrinceton UniversityNew JerseyUSA

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