Skip to main content

On Recent Developments in Linear Programming

  • Chapter
Trends in Mathematical Optimization

Abstract

Linear programming remains a central subject of Mathematical Programming although already in 1947, Dantzig developed the Simplex method for solving Linear Programming problems. That method has been implemented in quite powerful codes which are very efficient for solving real world problems. With the growing interest in the complexity of algorithms, however, it turned out that the Simplex method is exponential in the worst case (Klee-Minty [1972]). Until now the existence of pivoting rules resulting in a polynomial variant of the Simplex Method is an open question. For most known rules conterexamples to polynomiality have been constructed. The worst case approach itself is due to much criticizm. The analysis of the average case is usually much harder, but seems to explain the typically efficient performance of the Simplex method much better. For a broad discussion of average case analysis of the Simplex method we refer the reader to Borgwardt [1984].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

Introduction

  • Borgwardt, K. H.: A probabilistic analysis of the simplex method. Habilitation thesis, Kaiserslautern, October 1984.

    Google Scholar 

  • Khachian, L. G.: A polynomial algorithm in linear programming. Soviet Mathematics Doklady 20 (1979), 191–194.

    Google Scholar 

  • Khachian, L. G.: Polynomial algorithms in linear programming. USSR Computational Mathematics and Mathematical Physics 20 (1980), 53–72.

    Article  Google Scholar 

  • Klee, V. and Minty, G. J.: How good is the simplex algorithm. In: Shisha, O. (ed.): Inequalities III (1972), 159–175, Academic Press, New York.

    Google Scholar 

The Projective Approach

  • Anstreicher, K. M.: Analysis of a modified Karmarkar algorithm for linear programming. Working paper series B 84 (1985), Yale School of Organization and Management, Box 1A, New Haven, CT 06520.

    Google Scholar 

  • Benichou, M.; Gauthier, J. M.; Hentges, G. and Ribiére, G.: The efficient solution of large scale linear programming problems - some algorithmic techniques and computational results. Mathematical Programming 13 (1977), 280–322.

    Article  Google Scholar 

  • Birge, J. R. and Qi, L.: Solving stochastic linear programs via a variant od Karmarkars algorithms. Technical report 85-12, University of Michigan, College of Engineering, Ann Arbor, Michigan 48109-2117.

    Google Scholar 

  • Blair, Ch.: The iterative step in the linear programming algorithm of N. Karmarkar. Faculty Working Paper No. 1114 (1985), College of Commerce and Business Administration, University of Illinois at Urbana/Champaign.

    Google Scholar 

  • Blum, L.: Towards an asymptotic analysis of Karmarkar’s algorithm. Mills College, Oakland, CA 94613 and the Department of Mathematics, University of California, Berkeley 94720 (1985).

    Google Scholar 

  • Charnes, A.; Song, T. and Wolfe, M.: An explicit solution sequence and convergence of Karmarkar’s algorithm. Research report CCS 501 (1984), College of Business Administration 5.202, The University of Texas at Austin, Austin, Texas 78712/1177.

    Google Scholar 

  • Chiu, S. S. and Ye, Yinyu.: Recovering the shadow price in projection methods of linear programming. Engineering-Economics Systems Department, Stanford University, Stanford, California 94305 (1985).

    Google Scholar 

  • Fletcher, R. and Powell, M.J.D.: On the modification of LDLT factorizations. Math. Comp. 28 (1974), 1067–1087.

    Google Scholar 

  • Gay, D. M.: A variant of Karmarkar’s linear programming algorithms for problems in standard form. Numerical analysis manuscript 85-10 (1985), AT & T Bell Laboratories, Murray Hill, New Jersey 07974.

    Google Scholar 

  • Gay, D. M.: Electronic mail distribution of linear programming test problems. COAL Newsletter 13 (1985), 10–12.

    Google Scholar 

  • George, J. A. and Heath, M. T.: Solution of sparse linear least squares problems using Givens rotations. Linear Algebra and its applications 34 (1980), 69–83.

    Article  Google Scholar 

  • Ghellinck, G. de, and Vial, J. Ph.: A polynomial Newton method for linear programming. CORE discussion paper 8614 (1986), Université Catholique de Louvain, B-1348 Louvain-la-Neuve.

    Google Scholar 

  • Ghellinck, G. de, and Vial, J. Ph.: An extension of Karmarkar’s algorithm for solving a system of linear homogeneous equations on the simplex. Groupe de Recherches scientifiques en Gestion de l’Université Louis Pasteur de Strasbourg (1985).

    Google Scholar 

  • Goldfarb, D. and Mehrotra, S.: A relaxed version of Karmarkar’s method. Report (re-vised March 1986 ), Department of Industrial Engineering and Operations Research (1985), Columbia University, New York, NY 10027.

    Google Scholar 

  • Goldfarb, D. and Mehrotra, S.: Relaxed variants of Karmarkar’s algorithm for linear programs with unkown optimal objective value. Report, Department of Industrial Engineering and Operations Research (1986), Columbia University, New York, NY 10027.

    Google Scholar 

  • Golub, G. H. and Van Loan, C. F.: Matrix computations. John Hopkins University Press (1983).

    Google Scholar 

  • Gonzaga, C.: A conical projection algorithm for linear programming. Memo No. UCB/ERL M85/61, Electronics Research Laboratory, College of Engineering, University of California, Berkeley, CA 94720.

    Google Scholar 

  • Grötschel, M.; Lovász, L. and Schrijver, A.: The ellipsoid method and combinatorial optimization. Springer, Berlin (1986).

    Google Scholar 

  • Haverly, C. A.: Results of a new series of case runs using the Karmarker algorithm. Haverly Systems Inc. (1985), Denville, New Jersey 07834.

    Google Scholar 

  • Haverly, C. A.: Number of simplex iterations for four model structures. Haverly Systems Inc. (1985), Denville, New Jersey 07834.

    Google Scholar 

  • Haverly, C. A.: Studies on behaviour of the Karmarkar method. Haverly Systems Inc. (1985), Denville, New Jersey 07834.

    Google Scholar 

  • Karmarkar, N.: A new polynomial-time algorithm for linear programming. Combinatorica 4 (1984), 373–395.

    Article  Google Scholar 

  • Karmarkar, N.: Some comments on the significance of the new polynomial-time algorithm for linear programming. AT & T Bell Laboratories (1984),Murray Hill, New Jersey 07974.

    Google Scholar 

  • Kozlov, A. and Black, L. W.: Berkely obtains new results with the Karmarkar algorithm. Progress report in SIAM News 3 (19) 1986, p. 3 and 20.

    Google Scholar 

  • Lustig, I. J.: A practical approach to Karmzrkar’s algorithm. Technical Report Sol 85-5 (1985), Department of Operations Research, Stanford University, Stanford, CA 94305.

    Google Scholar 

  • Mehrotra, S.: A self correcting version of Karmarkar’s algorithm. Report (1986), Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027.

    Google Scholar 

  • Murtagh, B. A. and Saunders, M. A.: MINOS-5.0 users guide. Report Sol 83-20 (1983), Department of Operations Research, Stanford University, Stanford, CA 94305.

    Google Scholar 

  • Nickels, W.; Rodder, W.; Xu, L. and Zimmermann, H.-J.: Intelligent gradient search in linear programming. European Journal of Operational Research 22 (1985), 293–303.

    Article  Google Scholar 

  • Padberg, M.: Solution of a nonlinear programming problem arising in the projective method. Preprint, New York University, NY 10003 (1985).

    Google Scholar 

  • Padberg, M.: A different convergence proof of the projective method for linear programming. Operations research letters 4 (1986), 253–257.

    Article  Google Scholar 

  • Paige, C. C. and Saunders, M. A.: LSQR: An algorithm for sparse linear equations and sparse least-squares. ACM Transactions on mathematical software 8 (1982), 43–71.

    Article  Google Scholar 

  • Pickel, P. F.: Approximate projections for the Karmarkar algorithm. Manuscript (1985), Poly technique Institute of New York, Farmingdale, NY.

    Google Scholar 

  • Roos, C.: On Karmarkar’s projective method for linear programming. Report 85-23 ( 1985 ), Department of Mathematics and Informatics, Delft University of Technology, 2600 AJ Delft.

    Google Scholar 

  • Roos, C.: A pivoting rule for the simplex method which is related to Karmarkar’s potential function. Department of Mathematics and Informatics, Delft University of Technology, P. O.Box 356, 2600 AL Delft, The Netherlands.

    Google Scholar 

  • Schreck, H.: Experiences with an implementation of Karmarkar’s LP-algorithm. Mathematisches Institut der Technischen Universität München, Arcisstrasse 21, München (1985).

    Google Scholar 

  • Schrijver, A.: The new linear programming method of Karmarkar. CWI newsletter 8 (1985), Centre for Mathematics and Computer Science, P. O.Box 4079, 1009 AB Amsterdam, The Netherlands.

    Google Scholar 

  • Shanno, D. F.: A reduced gradient variant of Karmarkar’s algorithm. Working paper 85-10 (1985), Graduate School of Administration, University of California, Davis, CA 95616.

    Google Scholar 

  • Shanno, D. F.: Computing Karmarkar projections quickly. Graduate School of Administration (1985), University of California, Davis, CA 95616.

    Google Scholar 

  • Shanno, D. F. and Marsten, R. E: On implementing Karmarkar’s method. Working paper 85-1 (1985), Graduate school of administration, University of California, Davis, CA 95616.

    Google Scholar 

  • Strang, G.: Karmarkar’s algorithm in a nutshell. SIAM News 18 (1985), 13.

    Google Scholar 

  • Swart, E. R.: How I implemented the Karmarkar algorithm in one evening. APL Quote Quad 15. 3 (1985).

    Google Scholar 

  • Swart, E. R.: A modified version of the Karmarkar algorithm. Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada.

    Google Scholar 

  • Todd, M. J. and Burreil, B. P.: An extension of Karmarkar’s algorithm for linear programming using dual variables. Technical report 85-648 (1985), School of Operations Research and Industrial Engineering, College of Engineering, Cornell University, Ithaca, New York 14850.

    Google Scholar 

  • Tomlin, J. A.: An experimental approach to Karmarkar’s projective method for linear programming. Ketron Inc., Mountain View, CA 94040 (1985).

    Google Scholar 

  • Ye, Y.: Barrier projection and sliding current objective method for linear programming. Presentation at 12th Mathematical Programming Symposium, Boston, 1985a, Stanford, CA 94305: Engineering Economic Systems Department, Stanford University.

    Google Scholar 

  • Ye, Y.: Cutting-objective and scaling methods - a polynomial algorithm for linear programming. Submitted to Math. Programming, August 1985b.

    Google Scholar 

The Scaling Approach

  • Aronson, J.; Barr, R.; Helgason, R.; Kennington, J.; Loh, A. and Zaki, H.: The projective transformation algorithm by Karmarkar: A computational experiment with assignment problems. Technical report 85 OR-3 (1985), Department of Operations Research, Southern Methodist University, and Department of Industrial Engineering, University of Houston, Houston.

    Google Scholar 

  • Barnes, E. R.: A variation on Karmarkar’s algorithm for solving linear programming problems. Manuscript, IBM Watson Research Center, Yorktown Heights, NY (1985).

    Google Scholar 

  • Cavalier, T. M. and Soyster, A. L.: Some computational experience and a modification of the Karmarkar Algorithm. Department of Industrial and Management Systems Engineering (1985), 207 Hammond Building, The Pennsylvania State University, University Park, PA 16802.

    Google Scholar 

  • Chandru, V. and Khochar, B. S.: Exploiting special structures using a variant of Karmarkar’s algorithm. Research memorandum 86-10 (1985), School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907.

    Google Scholar 

  • Chandru, V. and Khochar, B. S.: A class of algorithms for linear programming. Research memorandum 85-14 (Revised June 1986), School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907 (1985).

    Google Scholar 

  • Chiu, S. S. and Ye, Yinyu: Simplex method and Karmarkar’s algorithm: A Unifying Structure. Engineering-Economic Systems Department (1985), Stanford University, Stanford, California 94305.

    Google Scholar 

  • Kennington, J. L. and Helgason, R. V.: Algorithms for network programming. Wiley, New York (1980).

    Google Scholar 

  • Kortanek, K.O. and Shi, M.: Convergence results and numerical experiments on a linear programming hybrid algorithm. Department of Mathematics (1985), Carnegie- Mellon University, Pittsburgh.

    Google Scholar 

  • Megiddo, N.: A variation of Karmarkar’s algorithm. Preliminary report (1985), IBM Research Laboratory, San Jose, CA 95193.

    Google Scholar 

  • Mitra, G.; Tamiz, M.; Yadegar, J. and Darby-Dowman, K.: Experimental investigation of an interior search algorithm for linear programming. Math. Programming Symposium, Boston, 1985.

    Google Scholar 

  • Poljak, B. T.: A general method of solving extremum problems. Soviet mathematics 8 (1967), 593–597.

    Google Scholar 

  • Sherali, H. D.: Algorithmic insights and a convergence analysis for a Karmarkar-type of algorithm for linear programming problems. Department of Industrial Engi-neering and Operations Research (1985), Virginia Polytechnic Institute and State University, Blacksburg, Virginial 24061.

    Google Scholar 

  • Shor, N. Z.: Generalized gradient methods of nondifferentiable optimization employing space dilation operators. In: Bachem, A.; Grotschel, M.; Korte, B.:”Mathematical Programming: The state of the art” (1983), Bonn.

    Google Scholar 

  • Vanderbei, R. J.; Meketon, M. S. and Freedman, B. A.: A modification of Karmarkar’s linear programming algorithm. AT & T Bell Laboratories (1985), Holmdel, New Jersey 07733.

    Google Scholar 

The Barrier Function Approach

  • Dembo, R. S.; Eisenstat S. C. and Steinhaug, T.: Inexact Newton methods. SIAM J. on Numerical Analysis 19 (1982), 400–408.

    Article  Google Scholar 

  • Eriksson, J. R.: Algorithms for entropy and mathematical programming. Linkoping Studies in Science and Technology, Dissertations No. 63 (1981), Department of Mathematics, Linkoping University, S-581 83 Linkoping, Sweden.

    Google Scholar 

  • Eriksson, J. R.: An iterative primal-dual algorithm for linear programming. Report LiTH-MAT-R-1985-10 (1985), Institute of Technology, Linkoping University, S-581 83 Linkoping, Sweden.

    Google Scholar 

  • Fiacco, A. V. and McCormick, G. P.: Nonlinear programming: Sequential unconstrained minimization techniques. Wiley, New York (1968).

    Google Scholar 

  • Frisch, K. R.: The logarithmic potential method of convex programming. Memorandum of May 13, 1955, University Institute of Economics, Oslo (1955).

    Google Scholar 

  • George, J. A. and Heath M. T.: Solution of large sparse linear least squares problems using Givens rotations. Linear Algebra and its applications 34 (1980), 69–83.

    Article  Google Scholar 

  • Gill, Ph. E.; Murray, W.; Saunders, M. A. and Wright, M. H.: A note on nonlinear approaches to linear programming. Technical report SOL 86-7 (1986), Department of Operations Research, Stanford University, Stanford, CA 94305.

    Google Scholar 

  • Gill, Ph. E.; Murray, W.; Saunders, M. A.; Tomlin, J. A. and Wright, M. H.: On projected Newton barrier methods for linear programming and an equivalence to Karmarkar’s projective method. Technical report SOL 85-11 (1985), Department of Operations Research, Stanford University, Stanford, CA 94305.

    Google Scholar 

  • Jittorntrum, K.: Sequential algorithms in nonlinear programming. Ph. D. Thesis, Australian National University (1978).

    Google Scholar 

  • Jittorntrum, K. and Osborne, M. R.: Trajectory analysis and extrapolation in barrier function methods. Journal of Australian Mathematical Society 21 (1980), 1–18.

    Article  Google Scholar 

  • Mifflin, R.: Convergence bounds for nonlinear programming algorithms. Administrative Sciences report 57, Yale University, Connecticut (1972).

    Google Scholar 

  • Mifflin, R.: On the convergence of the logarithmic barrier function method. In: Lootsma, F.:”Numerical Methods for Nonlinear Optimization” (1972), 367–369, Academic Press, London.

    Google Scholar 

  • Osborne, M. R.: Dual barrier functions with superfast rates of convergence for the linear programming problem. Report (1986), Department of Statistics, Research School of Social Sciences, Australian National University.

    Google Scholar 

A Penalty Approach

  • Iri, M. and Imai, H.: A multiplicative penalty function method for linear programming - another”New and Fast” algorithm. Department of Mathematical Engineering and Instrumentation Physics, Faculty of Engineering, University of Tokyo, Bunkyoku, Tokyo, Japan 113 (1985).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1988 Birkhaürser Verlag Basel

About this chapter

Cite this chapter

Zimmermann, U. (1988). On Recent Developments in Linear Programming. In: Hoffmann, KH., Zowe, J., Hiriart-Urruty, JB., Lemarechal, C. (eds) Trends in Mathematical Optimization. International Series of Numerical Mathematics/Internationale Schriftenreihe zur Numerischen Mathematik/Série internationale d’Analyse numérique, vol 84. Birkhäuser Basel. https://doi.org/10.1007/978-3-0348-9297-1_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-0348-9297-1_23

  • Publisher Name: Birkhäuser Basel

  • Print ISBN: 978-3-0348-9984-0

  • Online ISBN: 978-3-0348-9297-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics