Skip to main content
Springer Nature Link
Log in

Impact-Based Search Strategies for Constraint Programming

  • Conference paper
Principles and Practice of Constraint Programming – CP 2004 (CP 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3258))

Abstract

A key feature of constraint programming is the ability to design specific search strategies to solve problems. On the contrary, integer programming solvers have used efficient general-purpose strategies since their earliest implementations. We present a new general purpose search strategy for constraint programming inspired from integer programming techniques and based on the concept of the impact of a variable. The impact measures the importance of a variable for the reduction of the search space. Impacts are learned from the observation of domain reduction during search and we show how restarting search can dramatically improve performance. Using impacts for solving multiknapsack, magic square, and Latin square completion problems shows that this new criteria for choosing variables and values can outperform classical general-purpose strategies.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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.

Similar content being viewed by others

References

  1. ILOG CPLEX 9.0. User Manual. ILOG, S.A., Gentilly, France (September 2003)

    Google Scholar 

  2. Benichou, M., Gauthier, J.M., Girodet, P., Hentges, G., Ribiere, G., Vincent, O.: Experiments in mixed-integer linear programming. Mathematical Programming (1), 76–94 (1971)

    Google Scholar 

  3. Bessiere, C., Chmeiss, A., Sais, L.: Neighborhood-based variable ordering heuristics for the constraint satisfaction problem. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 565–569. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  4. Bessière, C., Régin, J.-C.: MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems. In: Freuder, E.C. (ed.) CP 1996. LNCS, vol. 1118, pp. 61–75. Springer, Heidelberg (1996)

    Google Scholar 

  5. Bixby, R.E., Cook, W., Cox, A., Lee, E.K.: Parallel mixed integer programming. Technical Report Research Monograph CRPC-TR95554, Center for Research on Parallel Computation (1995)

    Google Scholar 

  6. Brélaz, D.: New methods to color the vertices of a graph. Communication of the ACM (22), 251–256 (1979)

    Google Scholar 

  7. Gauthier, J.-M., Ribiere, G.: Experiments in mixed-integer linear programming using pseudo-costs. Mathematical Programming (12), 26–47 (1977)

    Google Scholar 

  8. Gomes, C.: Complete randomized backtrack search (survey). In: Milano, M. (ed.) Constraint and Integer Programming: Toward a Unified Methodology, pp. 233–283. Kluwer, Dordrecht (2003)

    Google Scholar 

  9. Gomes, C., Regin, J.-C.: Modelling alldi. matrix models in constraint programming. In: Optimization days, Montreal, Canada (2003)

    Google Scholar 

  10. Gomes, C., Shmoys, D.: Completing quasigroups or latin squares: A structured graph coloring problem. In: Proceedings of the Computational Symposium on Graph Coloring and Extensions (2002)

    Google Scholar 

  11. Haralick, R., Elliot, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence (14), 263–313 (1980)

    Google Scholar 

  12. Linderoth, J., Savelsberg, M.: A computational study of search strategies for mixed integer programming. INFORMS Journal on Computing 11(2), 173–187 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  13. McGregor, J.J.: Relational consistency algorithms and their application in finding subgraph and graph isomorphisms. Information Science 19, 229–250 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  14. Moskewicz, M.W., Madigan, C.F., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Design Automation Conference (2001)

    Google Scholar 

  15. Smith, B.: The Brelaz heuristic and optimal static ordering. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 405–418. Springer, Heidelberg (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ILOG, Les Taissounieres, 1681, route des Dolines, 06560, Sophia Antipolis, France

    Philippe Refalo

Authors
  1. Philippe Refalo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Clayton School of IT, Monash University, Australia

    Mark Wallace

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Refalo, P. (2004). Impact-Based Search Strategies for Constraint Programming. In: Wallace, M. (eds) Principles and Practice of Constraint Programming – CP 2004. CP 2004. Lecture Notes in Computer Science, vol 3258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30201-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30201-8_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23241-4

  • Online ISBN: 978-3-540-30201-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics