![]() ![]() Comput. 39(6), 2212–2231 (2010)īyrka, J., Ghodsi, M., Srinivasan, A.: LP-rounding algorithms for facility-location problems. Springer, Heidelberg (2009)īyrka, J., Aardal, K.: An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem. In: Sarbazi-Azad, H., Parhami, B., Miremadi, S.-G., Hessabi, S. Springer, Heidelberg (2003)Īsadi, M., Niknafs, A., Ghodsi, M.: An Approximation Algorithm for the k-level Uncapacitated Facility Location Problem with Penalties. In: Baeten, J.C.M., Lenstra, J.K., Parrow, J., Woeginger, G.J. Lett. 72(5-6), 161–167 (1999)Īgeev, A., Ye, Y., Zhang, J.: Improved Combinatorial Approximation Algorithms for the k-Level Facility Location Problem. This process is experimental and the keywords may be updated as the learning algorithm improves.Īardal, K., Chudak, F., Shmoys, D.: A 3-Approximation Algorithm for the k-Level Uncapacitated Facility Location Problem. These keywords were added by machine and not by the authors. Then, armed with this simple view point, we exercise the randomization on a more complicated algorithm for the k-level version of the problem with penalties in which the planner has the option to pay a penalty instead of connecting chosen clients, which results in an improved approximation algorithm. In this paper we first give a simple interpretation of this randomization process in terms of solving an auxiliary (factor revealing) LP. In particular, the currently best 1.488-approximation algorithm for the uncapacitated facility location (UFL) problem by Shi Li is presented as a result of a non-trivial randomization of a certain scaling parameter in the LP-rounding algorithm by Chudak and Shmoys combined with a primal-dual algorithm of Jain et al. The state of the art in approximation algorithms for facility location problems are complicated combinations of various techniques.
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