Brief Announcement: Characterizing Demand Graphs for (Fixed-Parameter) Shallow-Light Steiner Network

Authors Amy Babay, Michael Dinitz, Zeyu Zhang



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Author Details

Amy Babay
  • Johns Hopkins University, Baltimore, MD, USA
Michael Dinitz
  • Johns Hopkins University, Baltimore, MD, USA
Zeyu Zhang
  • Johns Hopkins University, Baltimore, MD, USA

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Amy Babay, Michael Dinitz, and Zeyu Zhang. Brief Announcement: Characterizing Demand Graphs for (Fixed-Parameter) Shallow-Light Steiner Network. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 104:1-104:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.ICALP.2018.104

Abstract

We consider the Shallow-Light Steiner Network problem from a fixed-parameter perspective. Given a graph G, a distance bound L, and p pairs of vertices {(s_i,t_i)}_{i in [p]}, the objective is to find a minimum-cost subgraph G' such that s_i and t_i have distance at most L in G' (for every i in [p]). Our main result is on the fixed-parameter tractability of this problem for parameter p. We exactly characterize the demand structures that make the problem "easy", and give FPT algorithms for those cases. In all other cases, we show that the problem is W[1]-hard. We also extend our results to handle general edge lengths and costs, precisely characterizing which demands allow for good FPT approximation algorithms and which demands remain W[1]-hard even to approximate.

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • Shallow-Light
  • Steiner Network
  • Fixed-Parameter Tractability

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References

  1. Amy Babay, Emily Wagner, Michael Dinitz, and Yair Amir. Timely, reliable, and cost-effective internet transport service using dissemination graphs. In 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017, pages 1-12, 2017. Google Scholar
  2. Parinya Chalermsook, Marek Cygan, Guy Kortsarz, Bundit Laekhanukit, Pasin Manurangsi, Danupon Nanongkai, and Luca Trevisan. From gap-eth to fpt-inapproximability: Clique, dominating set, and more. In Foundations of Computer Science (FOCS), 2017 IEEE 58th Annual Symposium on, pages 743-754. IEEE, 2017. Google Scholar
  3. Rajesh Chitnis, Andreas Emil Feldmann, and Pasin Manurangsi. Parameterized approximation algorithms for directed steiner network problems. arXiv preprint arXiv:1707.06499, 2017. Google Scholar
  4. Andreas Emil Feldmann and Dániel Marx. The complexity landscape of fixed-parameter directed steiner network problems. In 43rd International Colloquium on Automata, Languages, and Programming, ICALP 2016, volume 55 of LIPIcs, pages 27:1-27:14. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2016. Google Scholar
  5. Longkun Guo, Kewen Liao, and Hong Shen. On the shallow-light steiner tree problem. In Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2014 15th International Conference on, pages 56-60. IEEE, 2014. Google Scholar
  6. Mohammad Taghi Hajiaghayi, Guy Kortsarz, and Mohammad R Salavatipour. Approximating buy-at-bulk and shallow-light k-steiner trees. Algorithmica, 53(1):89-103, 2009. Google Scholar
  7. Refael Hassin. Approximation schemes for the restricted shortest path problem. Mathematics of Operations research, 17(1):36-42, 1992. Google Scholar
  8. Guy Kortsarz and David Peleg. Approximating shallow-light trees. Technical report, Association for Computing Machinery, New York, NY (United States), 1997. Google Scholar
  9. Joseph Naor and Baruch Schieber. Improved approximations for shallow-light spanning trees. In Foundations of Computer Science, 1997. Proceedings., 38th Annual Symposium on, pages 536-541. IEEE, 1997. Google Scholar
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