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Combining interior and exterior simplex type algorithms

Published: 19 September 2013 Publication History

Abstract

Linear Programming (LP) is a significant research area in the field of operations research. The simplex algorithm is the most widely used and well-studied method for solving Linear Programming problems (LPs). Many algorithms have been proposed for the solution of LPs. The vast majority of these algorithms belong to three main categories: (i) Simplex-type or pivoting algorithms, (ii) interior-point methods (IPMs) and (iii) exterior point simplex type algorithms (EPSA). The aim of this paper is to present an implementation of a hybrid simplex algorithm that begins to solve the LP using an IPM and after a number of iterations continues with a primal-dual EPSA algorithm. This hybrid approach aims to take advantage of: (i) IPM strengths, which is the fast convergence in the first iterations, and (ii) EPSA strengths, i.e. the fast convergence when making steps in directions that are linear combinations of attractive directions. The idea of combining different types of linear programming algorithms is not new; to the best of our knowledge, this is the first time that interior point methods and exterior point algorithms are combined. The interior point that is calculated by IPM after a number of iterations can lead to such attractive directions. In order to gain an insight into the practical behavior of the proposed algorithm, we have performed some computational experiments over sparse randomly generated optimal LPs. Finally, in the computational study that we have conducted, we investigate the adequate number of iterations that IPM should run in order to decrease the CPU time and the iterations of the proposed algorithm.

References

[1]
Borgwardt, H. K. 1982. The average number of pivot steps required by the simplex method is polynomial. Zeitschrift fur Operational Research 26, 1, 157--177. DOI= http://dx.doi.org/10.1007%2FBF01917108.
[2]
Bazaraa, M. S., Jarvis, J. J., and Sherali, H. D. 2005. Linear Programming and Network Flows, 3rd ed. John Wiley & Sons. DOI= http://dx.doi.org/10.1002/9780471703778.
[3]
Bertsimas, D., and Tsitsiklis, J. N. 1997. Introduction to Linear Optimization. Athena Scientific.
[4]
Dantzig, G. B. 1963. Linear Programming and Extensions. Princeton, University Press, Princeton, NJ.
[5]
Andersen, E. D. and Ye, Y. 1996. Combining interior-point and pivoting algorithms for Linear Programming. Management Science 42, 12, 1719--1731.
[6]
Glavelis, Th. and Samaras, N. 2013. An experimental investigation of a primal-dual exterior point simplex algorithm. Optimization - A Journal of Mathematical Programming and Operations Research, 1--10. DOI= http://dx.doi.org/10.1080/02331934.2013.765422
[7]
Gondzio, J. 1996. Multiple centrality corrections in a primal-dual method for linear programming. Computational Optimization and Applications 6, 2, 137--156. DOI= http://dx.doi.org/10.1007/BF00249643
[8]
Karmarkar, N. K. 1984. A new polynomial-time algorithm for linear programming, Combinatorica, 4, 4, 373--395. DOI= http://dx.doi.org/10.1007/BF02579150
[9]
Klee, V. and Minty, G. J. 1992. How good is the simplex algorithm?. Inequalities III. New York: Academic Press, 159--175.
[10]
Paparrizos, K. 1991. An infeasible exterior point simplex algorithm for assignment problems. Mathematical Programming 51, 1-3, 45--54. DOI= http://dx.doi.org/10.1007/BF01586925
[11]
Paparrizos, K., Samaras, N., and Stephanides, G. 2003. A new efficient primal dual simplex algorithm. Computers and Operations Research 30, 9, 1383--1399. DOI= http://dx.doi.org/10.1016/S0305-0548(02)00077-1
[12]
Paparrizos, K., Samaras, N., and Stephanides, G. 2003. An efficient simplex type algorithm for sparse and dense linear programs. European Journal of Operational Research 148, 2, 323--334. DOI= http://dx.doi.org/10.1016/S0377-2217(02)00400-9
[13]
Terlaky, T. and Zhang, S. 1993. Pivot rules for linear programming -- A survey. Annals of Operations Research 46-47, 1, 203--233. DOI= http://dx.doi.org/10.1007/BF02096264
[14]
Zhang, Y. 1998. Solving Large-Scale Linear Programs by Interior-Point Methods under the MATLAB Environment. Optimization Methods and Software 10, 1, 1--31. DOI= http://dx.doi.org/10.1080/10556789808805699

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cover image ACM Other conferences
PCI '13: Proceedings of the 17th Panhellenic Conference on Informatics
September 2013
359 pages
ISBN:9781450319690
DOI:10.1145/2491845
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

  • University of Macedonia
  • Aristotle University of Thessaloniki
  • The University of Sheffield: The University of Sheffield
  • Alexander TEI of Thessaloniki

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 September 2013

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Author Tags

  1. MATLAB
  2. computational study
  3. exterior point simplex type algorithms
  4. interior point method
  5. linear programming
  6. operations research
  7. simplex algorithm

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PCI 2013
Sponsor:
  • The University of Sheffield
PCI 2013: 17th Panhellenic Conference on Informatics
September 19 - 21, 2013
Thessaloniki, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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