Skip to main content

Submodular Function Maximization on the Bounded Integer Lattice

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9499))

Abstract

We consider the problem of maximizing a submodular function on the bounded integer lattice. As a direct generalization of submodular set functions, \(f: \{0, \ldots , C\}^n \rightarrow \mathbb {R}_+\) is submodular, if \(f(x) + f(y) \ge f(x \wedge y) + f(x \vee y)\) for all \(x,y \in \{0, \ldots , C\}^n\) where \(\wedge \) and \(\vee \) denote element-wise minimum and maximum. The objective is finding a vector x maximizing f(x). In this paper, we present a deterministic \(\frac{1}{3}\)-approximation using a framework inspired by [2]. We also provide an example that shows the analysis is tight and yields additional insight into the possibilities of modifying the algorithm. Moreover, we examine some structural differences to maximization of submodular set functions which make our problem harder to solve.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Bolandnazar, M., Huh, W., McCormick, S., Murota, K.: A note on “order-based cost optimization in assemble-to-order systems”. University of Tokyo (February, Techical report (2015)

    Google Scholar 

  2. Buchbinder, N., Feldmann, M., Naor, J., Schwartz, R.: A tight linear (1/2)-approximation for unconstrained submodular maximization. In: FOCS pp. 649–658 (2012)

    Google Scholar 

  3. Buchbinder, N., Feldman, M.: Deterministic algorithms for submodular maximization problems. CoRR (2015). abs/1505.02695

  4. Calinescu, G., Chekuri, C., Pál, M., Vondrák, J.: Maximizing a submodular set function subject to a matroid constraint (extended abstract). In: Fischetti, M., Williamson, D.P. (eds.) IPCO 2007. LNCS, vol. 4513, pp. 182–196. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Feige, M.: Vondrák: Maximizing Non-Monotone Submodular Functions. SIAM Journal on Computing 40(4), 1133–1153 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  6. Feldman, M., Naor, J.S., Schwartz, R.: Nonmonotone submodular maximization via a structural continuous greedy algorithm. In: Aceto, L., Henzinger, M., Sgall, J. (eds.) ICALP 2011, Part I. LNCS, vol. 6755, pp. 342–353. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Fujishige, S.: Submodular Functions and Optimization. Annals of Discrete Mathematics, vol. 58, 2nd edn, pp. 305–308. Elsevier, New York (2005)

    MATH  Google Scholar 

  8. Goldengorin, B., Tijssen, G., Tso, M.: The maximization of submodular functions: old and new proofs for the correctness of the dichotomy algorithm. Technical report 99A17, University of Groningenm Research Institute SOM (1999)

    Google Scholar 

  9. Golovin, D., Krause, A.: Submodular function maximization. In: Bordeaux, L., Hamadi, Y., Kohli, P. (eds.) Tractability: Practical Approaches to Hard Problems. Cambridge University Press, Cambridge (2014)

    Google Scholar 

  10. Grabisch, M., Xie, L.: The restricted core of games on distributive lattices: how to share benefits in a hierarchy. Math. Methods Oper. Res. 73, 189–208 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  11. Grötschel, M., Lovász, L., Schrijver, A.: Geometric Algorithms and Combinatorial Optimization. Springer, Berlin (1988)

    Book  MATH  Google Scholar 

  12. Inaba, K., Kakimura, N., K., K., Soma, T.: Optimal budget allocation: Theoretical guarantee and efficient algorithm. In: Proceedings of the 31st International Conference on Machine Learning, pp. 351–359 (2014). JMLR.org

  13. Murota, K.: Submodular function minimization and maximization in discrete convex analysis. RIMS Kokyuroku Bessatsu B23, 193–211 (2010)

    MATH  MathSciNet  Google Scholar 

  14. Oveis Gharan, S., Vondrák, J.: Submodular maximization by simulated annealing. In: SODA, pp. 1098–1117 (2011)

    Google Scholar 

  15. Schulz, A., Uhan, N.: Approximating the least core value and least core of cooperative games with supermodular costs. Discrete Optim. 10(2), 163–180 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  16. Soma, T., Yoshida, Y.: Maximizing submodular functions with the diminishing return property over the integer lattice. CoRR 2015. abs/1503.01218

Download references

Acknowledgement

We thank S.Thomas McCormick and Kazuo Murota for fruitful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Corinna Gottschalk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Gottschalk, C., Peis, B. (2015). Submodular Function Maximization on the Bounded Integer Lattice. In: Sanità, L., Skutella, M. (eds) Approximation and Online Algorithms. WAOA 2015. Lecture Notes in Computer Science(), vol 9499. Springer, Cham. https://doi.org/10.1007/978-3-319-28684-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28684-6_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28683-9

  • Online ISBN: 978-3-319-28684-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics