An Exponential Time 2-Approximation Algorithm for Bandwidth

  • Martin Fürer
  • Serge Gaspers
  • Shiva Prasad Kasiviswanathan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5917)

Abstract

The bandwidth of a graph G on n vertices is the minimum b such that the vertices of G can be labeled from 1 to n such that the labels of every pair of adjacent vertices differ by at most b.

In this paper, we present a 2-approximation algorithm for the Bandwidth problem that takes worst-case \(\mathcal{O}(1.9797^n)\) \(= \mathcal{O}(3^{0.6217 n})\) time and uses polynomial space. This improves both the previous best 2- and 3-approximation algorithms of Cygan et al. which have an \(\mathcal{O}^*(3^n)\) and \(\mathcal{O}^*(2^n)\) worst-case time bounds, respectively. Our algorithm is based on constructing bucket decompositions of the input graph. A bucket decomposition partitions the vertex set of a graph into ordered sets (called buckets) of (almost) equal sizes such that all edges are either incident on vertices in the same bucket or on vertices in two consecutive buckets. The idea is to find the smallest bucket size for which there exists a bucket decomposition. The algorithm uses a simple divide-and-conquer strategy along with dynamic programming to achieve this improved time bound.

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References

  1. 1.
    Amini, O., Fomin, F.V., Saurabh, S.: Counting Subgraphs via Homomorphisms. In: Proceedings of ICALP 2009, pp. 71–82 (2009)Google Scholar
  2. 2.
    Blum, A., Konjevod, G., Ravi, R., Vempala, S.: Semi-Definite Relaxations for Minimum Bandwidth and other Vertex-Ordering problems. Theor. Comput. Sci. 235(1), 25–42 (2000)CrossRefMATHMathSciNetGoogle Scholar
  3. 3.
    Bodlaender, H.L., Fellows, M.R., Hallett, M.T.: Beyond NP-completeness for Problems of Bounded Width: Hardness for the W-hierarchy. In: Proceedings of STOC 1994, pp. 449–458 (1994)Google Scholar
  4. 4.
    Chen, J., Huang, X., Kanj, I.A., Xia, G.: Linear FPT Reductions and Computational Lower Bounds. In: Proceedings of STOC 2004, pp. 212–221 (2004)Google Scholar
  5. 5.
    Cygan, M., Kowalik, L., Pilipczuk, M., Wykurz, M.: Exponential-time Approximation of Hard Problems, Technical Report abs/0810.4934, arXiv, CoRR (2008)Google Scholar
  6. 6.
    Cygan, M., Pilipczuk, M.: Faster exact bandwidth. In: Broersma, H., Erlebach, T., Friedetzky, T., Paulusma, D. (eds.) WG 2008. LNCS, vol. 5344, pp. 101–109. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Cygan, M., Pilipczuk, M.: Even Faster Exact Bandwidth, Technical Report abs/0902.1661, arXiv, CoRR (2009)Google Scholar
  8. 8.
    Cygan, M., Pilipczuk, M.: Exact and approximate Bandwidth. In: Proceedings of ICALP 2009, pp. 304–315 (2009)Google Scholar
  9. 9.
    Dunagan, J., Vempala, S.S.: On euclidean embeddings and bandwidth minimization. In: Goemans, M.X., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds.) RANDOM 2001 and APPROX 2001. LNCS, vol. 2129, pp. 229–240. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Feige, U.: Approximating the Bandwidth via Volume Respecting Embeddings. J. Comput. Syst. Sci. 60(3), 510–539 (2000)CrossRefMATHMathSciNetGoogle Scholar
  11. 11.
    Feige, U.: Coping with the NP-Hardness of the Graph Bandwidth Problem. In: Halldórsson, M.M. (ed.) SWAT 2000. LNCS, vol. 1851, pp. 10–19. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Feige, U., Talwar, K.: Approximating the bandwidth of caterpillars. In: Chekuri, C., Jansen, K., Rolim, J.D.P., Trevisan, L. (eds.) APPROX 2005 and RANDOM 2005. LNCS, vol. 3624, pp. 62–73. Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Fürer, M., Gaspers, S., Kasiviswanathan, S.P.: An Exponential Time 2-Approximation Algorithm for Bandwidth, Technical Report abs/0906.1953, arXiv, CoRR (2009)Google Scholar
  14. 14.
    Garey, M.R., Graham, R.L., Johnson, D.S., Knuth, D.E.: Complexity Results for Bandwidth Minimization. SIAM J. Appl. Math. 34(3), 477–495 (1978)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Impagliazzo, R., Paturi, R.: On the Complexity of k-SAT. J. Comput. Syst. Sci. 62(2), 367–375 (2001)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Lee, J.R.: Volume Distortion for subsets of Euclidean Spaces. Discrete Comput. Geom. 41(4), 590–615 (2009)CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Monien, B.: The Bandwidth Minimization Problem for Caterpillars with Hair Length 3 is NP-complete. SIAM J. Alg. Disc. Meth. 7(4), 505–512 (1986)CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    Monien, B., Sudborough, I.H.: Bandwidth Problems in Graphs. In: Proceedings of Allerton Conference on Communication, Control, and Computing 1980, pp. 650–659 (1980)Google Scholar
  19. 19.
    Papadimitriou, C.: The NP-completeness of the Bandwidth Minimization Problem. Computing 16, 263–270 (1976)CrossRefMATHMathSciNetGoogle Scholar
  20. 20.
    Saxe, J.: Dynamic Programming Algorithms for Recognizing Small-bandwidth Graphs in Polynomial Time. SIAM J. Alg. Disc. Meth. 1, 363–369 (1980)CrossRefMATHMathSciNetGoogle Scholar
  21. 21.
    Unger, W.: The Complexity of the Approximation of the Bandwidth Problem. In: Proceedings of FOCS 1998, pp. 82–91 (1998)Google Scholar
  22. 22.
    Vassilevska, V., Williams, R., Woo, S.L.M.: Confronting Hardness using a Hybrid Approach. In: Proceedings of SODA 2006, pp. 1–10 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martin Fürer
    • 1
  • Serge Gaspers
    • 2
  • Shiva Prasad Kasiviswanathan
    • 3
  1. 1.Computer Science and EngineeringPennsylvania State University 
  2. 2.CMMUniversidad de Chile 
  3. 3.Los Alamos National Laboratory 

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