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Combining Interior-Point and Pivoting Algorithms for Linear Programming

Author

Listed:
  • Erling D. Andersen

    (Department of Management, Odense University, DK-5230 Odense M, Denmark)

  • Yinyu Ye

    (Department of Management Sciences, The University of Iowa, Iowa City, Iowa 52242)

Abstract

We propose a new approach to combine linear programming (LP) interior-point and simplex pivoting algorithms. In any iteration of an interior-point algorithm we construct a related LP problem, which approximates the original problem, with a known (strictly) complementary primal-dual solution pair. Thus, we can apply Megiddo's (Megiddo, N. 1991. On finding primal- and dual-optimal bases. ORSA J. Comput. 3(1) 63--65.) pivoting procedure to compute an optimal basis for the approximate problem in strongly polynomial time. We show that, if the approximate problem is constructed from an interior-point iterate sufficiently close to the optimal face, then any optimal basis of the approximate problem is an optimal basis for the original problem. If the LP data are rational, the total number of interior-point iterations to create such a sufficient approximate problem is bounded by a polynomial in the data size. We develop a modification of Megiddo's procedure and discuss several implementation issues in solving the approximate problem. We also report encouraging computational results for this combined approach.

Suggested Citation

  • Erling D. Andersen & Yinyu Ye, 1996. "Combining Interior-Point and Pivoting Algorithms for Linear Programming," Management Science, INFORMS, vol. 42(12), pages 1719-1731, December.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:12:p:1719-1731
    DOI: 10.1287/mnsc.42.12.1719
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    Citations

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    Cited by:

    1. B. Curtis Eaves & Arthur F. Veinott, 2014. "Maximum-Stopping-Value Policies in Finite Markov Population Decision Chains," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 597-606, August.
    2. Maria Gonzalez-Lima & Hua Wei & Henry Wolkowicz, 2009. "A stable primal–dual approach for linear programming under nondegeneracy assumptions," Computational Optimization and Applications, Springer, vol. 44(2), pages 213-247, November.
    3. Erling D. Andersen, 1999. "On Exploiting Problem Structure in a Basis Identification Procedure for Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 11(1), pages 95-103, February.
    4. Liu, Yanwu & Tu, Yan & Zhang, Zhongzhen, 2021. "The row pivoting method for linear programming," Omega, Elsevier, vol. 100(C).
    5. Michael O’Sullivan & Arthur F. Veinott, Jr., 2017. "Polynomial-Time Computation of Strong and n -Present-Value Optimal Policies in Markov Decision Chains," Mathematics of Operations Research, INFORMS, vol. 42(3), pages 577-598, August.

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