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A Lagrangian search method for the P-median problem

Author

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  • Joshua Q. Hale

    (Georgia Institute of Technology)

  • Enlu Zhou

    (Georgia Institute of Technology)

  • Jiming Peng

    (University of Houston)

Abstract

In this paper, we propose a novel algorithm for solving the classical P-median problem. The essential aim is to identify the optimal extended Lagrangian multipliers corresponding to the optimal solution of the underlying problem. For this, we first explore the structure of the data matrix in P-median problem to recast it as another equivalent global optimization problem over the space of the extended Lagrangian multipliers. Then we present a stochastic search algorithm to find the extended Lagrangian multipliers corresponding to the optimal solution of the original P-median problem. Numerical experiments illustrate that the proposed algorithm can effectively find a global optimal or very good suboptimal solution to the underlying P-median problem, especially for the computationally challenging subclass of P-median problems with a large gap between the optimal solution of the original problem and that of its Lagrangian relaxation.

Suggested Citation

  • Joshua Q. Hale & Enlu Zhou & Jiming Peng, 2017. "A Lagrangian search method for the P-median problem," Journal of Global Optimization, Springer, vol. 69(1), pages 137-156, September.
    • Handle: RePEc:spr:jglopt:v:69:y:2017:i:1:d:10.1007_s10898-016-0481-x
    DOI: 10.1007/s10898-016-0481-x
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    References listed on IDEAS

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