Abstract
Steiner tree problem is a typical NP-hard problems in combinatorial optimization, which has comprehensive application background and is a hot topic in recent years. In this paper, we study the stochastic prize-collecting Steiner tree problem. Before the actual requirements materialize, we can choose (purchase) some edges in the first stage. When actual requirements are revealed, drawn from a prespecified probability distribution, then there are more edges may be chosen (purchased) for the actual requirements. The goal is to minimize the sum of the first stage cost, the expected second stage cost and the expected penalty cost. We propose a primal-dual 3-approximation algorithm for the stochastic prize-collecting Steiner tree problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bern, M., Plassmann, P.: The Steiner problem with edge lengths 1 and 2. Inf. Process. Lett. 32, 171–176 (1989)
Byrka, J., Grandoni, F., Rothvoß, T., Sanità, L.: Steiner tree approximation via iterative randomized rounding. J. ACM 60(1), 1–33 (2013)
Chopra, S., Rao, M.R.: The Steiner tree problem I: formulations, compositions and extension of facets. Math. Program. 64, 20–229 (1994)
Chopra, S., Rao, M.R.: Properties and classes of facets: the Steiner tree problem II. Math. Program. 64, 231–246 (1994)
Chopra, S., Tsai, C.Y.: Polyhedral approaches for the Steiner tree problem on graphs. In: Steiner Trees in Industry, pp. 175–202. Kluwer Academic Publishers (2001)
Cole, R., Hariharan, R., Lewenstein, M., Porat, E.: A faster implementation of the Goemans-Williamson clustering algorithm. In: Proceedings of the 12th annual ACM-SIAM Symposium on Discrete Algorithms, pp. 17–25 (2001)
Feofiloff, P., Fernandes, C.G., Ferreira, C.E., Pina, J.C.D.: Primal-dual approximation algorithms for the prize-collecting Steiner tree problem. Inf. Process. Lett. 103(5), 195–202 (2007)
Goemans, M.X., Williamson, D.P.: A general approximation technique for constrained forest problems. SIAM J. Comput. 24(2), 296–317 (1995)
Gupta, A., Pál, M., Ravi, R., Sinha A.: Boosted sampling: approximation algorithms for stochastic optimization problems. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing, pp. 417–426 (2004)
Heitsch, H., Römisch, W.: Scenario reduction algorithm in stochastic programming. Comput. Optim. Appl. 24, 187–206 (2003)
Hochbaum, D.S.: Approximation Algorithms for NP-hard Problems. PWS Publishing Company, Boston (1997)
Johnson, D.S., Minkoff, M., Phillips, S.: The prize collecting Steiner tree problem: theory and practice. In: Proceedings of the 11th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 760–769 (2000)
Karp, R.M.: Reducibility among Combinatorial Problems. In: Miller, R.E., Thatcher, J.W., Bohlinger, J.D. (eds.) Complexity of Computer Computations, pp. 85–103. Springer, Boston (1972). https://doi.org/10.1007/978-1-4684-2001-2_9
Kurz, D., Mutzel, P., Zey, B.: Parameterized algorithms for stochastic Steiner tree problems. In: Kučera, A., Henzinger, T.A., Nešetřil, J., Vojnar, T., Antoš, D. (eds.) MEMICS 2012. LNCS, vol. 7721, pp. 143–154. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36046-6_14
Ravi, R., Sinha, A.: Hedging uncertainty: approximation algorithms for stochastic optimization problems. 108(1), 97–114 (2006)
Schultz, R., Stougie, L., van der Vlerk, M.H.: Two stage stochastic integer programming: a survey. Statistica Neerlandica 50(3), 404–416 (1996)
Acknowledgements
The authors are supported by Natural Science Foundation of China (Nos.11871280, 11471003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, J., Sheng, H., Sun, Y., Zhang, X. (2019). Approximation Algorithm for Stochastic Prize-Collecting Steiner Tree Problem. In: Du, DZ., Li, L., Sun, X., Zhang, J. (eds) Algorithmic Aspects in Information and Management. AAIM 2019. Lecture Notes in Computer Science(), vol 11640. Springer, Cham. https://doi.org/10.1007/978-3-030-27195-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-27195-4_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27194-7
Online ISBN: 978-3-030-27195-4
eBook Packages: Computer ScienceComputer Science (R0)