Skip to main content

Approximation with a Fixed Number of Solutions of Some Biobjective Maximization Problems

  • Conference paper
Book cover Approximation and Online Algorithms (WAOA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7164))

Included in the following conference series:

Abstract

We investigate the problem of approximating the Pareto set of biobjective optimization problems with a given number of solutions. This task is relevant for two reasons: (i) Pareto sets are often computationally hard so approximation is a necessary tradeoff to allow polynomial time algorithms; (ii) limiting explicitly the size of the approximation allows the decision maker to control the expected accuracy of approximation and prevents him to be overwhelmed with too many alternatives. Our purpose is to exploit general properties that many well studied problems satisfy. We derive existence and constructive approximation results for the biobjective versions of Max Bisection, Max Partition, Max Set Splitting and Max Matching.

This research has been supported by the project ANR-09-BLAN-0361 GUaranteed Efficiency for PAReto optimal solutions Determination (GUEPARD).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alimonti, P.: Non-Oblivious Local Search for Graph and Hypergraph Coloring Problems. In: Nagl, M. (ed.) WG 1995. LNCS, vol. 1017, pp. 167–180. Springer, Heidelberg (1995)

    Chapter  Google Scholar 

  2. Angel, E., Bampis, E., Gourvès, L.: Approximation algorithms for the bi-criteria weighted max-cut problem. Discrete Applied Mathematics 154(12), 1685–1692 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  3. Angel, E., Bampis, E., Gourvès, L., Monnot, J.: (Non)-Approximability for the Multi-criteria TSP(1,2). In: Liśkiewicz, M., Reischuk, R. (eds.) FCT 2005. LNCS, vol. 3623, pp. 329–340. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  4. Angel, E., Bampis, E., Kononov, A.: On the approximate tradeoff for bicriteria batching and parallel machine scheduling problems. Theoretical Computer Science 306(1-3), 319–338 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bazgan, C., Hugot, H., Vanderpooten, D.: Implementing an efficient fptas for the 0-1 multi-objective knapsack problem. European Journal of Operational Research 198(1), 47–56 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press (2009)

    Google Scholar 

  7. Edmonds, J.: Paths, trees, and flowers. Canadian Journal of Mathematics 17, 449–467 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ehrgott, M.: Multicriteria optimization. LNEMS. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  9. Erlebach, T., Kellerer, H., Pferschy, U.: Approximating multiobjective knapsack problems. Management Science 48(12), 1603–1612 (2002)

    Article  MATH  Google Scholar 

  10. Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of ACM 42(6), 1115–1145 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  11. Halperin, E., Zwick, U.: A unified framework for obtaining improved approximation algorithms for maximum graph bisection problems. Random Structure Algorithms 20(3), 382–402 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hansen, P.: Bicriteria path problems. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making: Theory and Applications, pp. 109–127 (1980)

    Google Scholar 

  13. Hastad, J.: Some optimal inapproximability results. Journal of ACM 48(4), 798–859 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  14. Kann, V., Lagergren, J., Panconesi, A.: Approximability of maximum splitting of k-sets and some other apx-complete problems. Information Processing Letters 58(3), 105–110 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  15. Karp, R.M.: Reducibility among combinatorial problems. In: Miller, R., Thatcher, J. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)

    Google Scholar 

  16. Manthey, B.: On Approximating Multi-Criteria TSP. In: Albers, S., Marion, J.-Y. (eds.) Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS 2009). LIPIcs, pp. 637–648 (2009)

    Google Scholar 

  17. Paluch, K., Mucha, M., Mądry, A.: A 7/9 - Approximation Algorithm for the Maximum Traveling Salesman Problem. In: Dinur, I., Jansen, K., Naor, J., Rolim, J. (eds.) APPROX 2009. LNCS, vol. 5687, pp. 298–311. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  18. Papadimitriou, C.H., Yannakakis, M.: On the approximability of trade-offs and optimal access of web sources. In: Proceedings of the 41st Annual Symposium on Foundations of Computer Science (FOCS 2000), pp. 86–92 (2000)

    Google Scholar 

  19. Serafini, P.: Some considerations about computational complexity for multi objective combinatorial problems. In: Jahn, J., Krabs, W. (eds.) Recent Advances and Historical Development of Vector Optimization. Lecture Notes in Economics and Mathematical Systems, vol. 294, pp. 222–232 (1986)

    Google Scholar 

  20. Stein, C., Wein, J.: On the existence of schedules that are near-optimal for both makespan and total weighted completion time. Operational Research Letters 21(3), 115–122 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  21. Tsaggouris, G., Zaroliagis, C.: Multiobjective Optimization: Improved FPTAS for Shortest Paths and Non-linear Objectives with Applications. In: Asano, T. (ed.) ISAAC 2006. LNCS, vol. 4288, pp. 389–398. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Warburton, A.: Approximation of pareto-optima in multiple-objective shortest path problems. Operations Research 35(1), 70–79 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  23. Woeginger, G.: A polynomial time approximation scheme for maximizing the minimum machine completion time. Operations Research Letters 20(4), 149–154 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  24. Zhang, J., Yea, Y., Han, Q.: Improved approximations for max set splitting and max NAE SAT. Discrete Applied Mathematics 142(1-3), 133–149 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  25. Zwick, U.: Approximation algorithms for constraint satisfaction problems involving at most three variables per constraint. In: Proceedings of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 1998), pp. 201–210 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bazgan, C., Gourvès, L., Monnot, J. (2012). Approximation with a Fixed Number of Solutions of Some Biobjective Maximization Problems. In: Solis-Oba, R., Persiano, G. (eds) Approximation and Online Algorithms. WAOA 2011. Lecture Notes in Computer Science, vol 7164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29116-6_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29116-6_20

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29115-9

  • Online ISBN: 978-3-642-29116-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics