Explaining Flow-Based Propagation
Abstract
Lazy clause generation is a powerful approach to reducing search in constraint programming. For use in a lazy clause generation solver, global constraints must be extended to explain themselves. In this paper we present two new generic flow-based propagators (for hard and soft flow-based constraints) with several novel features, and most importantly, the addition of explanation capability. We discuss how explanations change the tradeoffs for propagation compared with the previous generic flow-based propagator, and show that the generic propagators can efficiently replace specialized versions, in particular for gcc and sequence constraints. Using real-world scheduling and rostering problems as examples, we compare against a number of standard Constraint Programming implementations of these contraints (and in the case of soft constraints, Mixed-Integer Programming models) to show that the new global propagators are extremely beneficial on these benchmarks.
Keywords
Constraint Program Soft Constraint Global Constraint Nurse Rostering Residual GraphPreview
Unable to display preview. Download preview PDF.
References
- 1.Achterberg, T.: Conflict analysis in mixed integer programming. Discrete Optimization 4(1), 4–20 (2007)MathSciNetMATHCrossRefGoogle Scholar
- 2.Bockmayr, A., Pisaruk, N., Aggoun, A.: Network Flow Problems in Constraint Programming. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 196–210. Springer, Heidelberg (2001)CrossRefGoogle Scholar
- 3.Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting Systematic Search by Weighting Constraints. In: Proc. ECAI 2004, pp. 146–150 (2004)Google Scholar
- 4.Brand, S., Narodytska, N., Quimper, C.-G., Stuckey, P.J., Walsh, T.: Encodings of the Sequence Constraint. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 210–224. Springer, Heidelberg (2007)CrossRefGoogle Scholar
- 5.Davey, B., Boland, N., Stuckey, P.: Efficient Intelligent Backtracking Using Linear Programming. IJOC 14(4), 373–386 (2002)MathSciNetCrossRefGoogle Scholar
- 6.Ford, L., Fulkerson, D.: Maximal flow through a network. Canad. J. Math. 8, 399–404 (1956)MathSciNetMATHCrossRefGoogle Scholar
- 7.Gauthier, J.M., Ribière, G.: Experiments in mixed-integer linear programming using pseudo-costs. Mathematical Programming 12, 26–47 (1977)MathSciNetMATHCrossRefGoogle Scholar
- 8.Gent, I.P.: Two Results on Car-sequencing Problems. Technical report APES-02-1998, Dept. of CS, University of Strathclyde, UK (1998)Google Scholar
- 9.Gent, I.P., Walsh, T.: CSPlib: A Benchmark Library for Constraints. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 480–481. Springer, Heidelberg (1999)Google Scholar
- 10.Giunchiglia, E., Maratea, M., Tacchella, A.: (In)Effectiveness of Look-Ahead Techniques in a Modern SAT Solver. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 842–846. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 11.Gravel, M., Gagné, C., Price, W.L.: Review and Comparison of Three Methods for the Solution of the Car Sequencing Problem. J.O.R. Soc. 56(11), 1287–1295 (2005)MATHCrossRefGoogle Scholar
- 12.Katsirelos, G.: Nogood processing in CSPs. Ph.D. thesis, University of Toronto, Canada (2008)Google Scholar
- 13.Löbel, A.: MCF 1.3 - A network simplex implementation (2004), available free of charge for academic use, http://www.zib.de/loebel
- 14.Maher, M., Narodytska, N., Quimper, C.-G., Walsh, T.: Flow-Based Propagators for the SEQUENCE and Related Global Constraints. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 159–174. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 15.Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., Malik, S.: Chaff: engineering an efficient SAT solver. In: Proc. DAC 2001, pp. 530–535 (2001)Google Scholar
- 16.Ohrimenko, O., Stuckey, P., Codish, M.: Propagation via lazy clause generation. Constraints 14, 357–391 (2009)MathSciNetMATHCrossRefGoogle Scholar
- 17.Refalo, P.: Impact-Based Search Strategies for Constraint Programming. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 557–571. Springer, Heidelberg (2004)CrossRefGoogle Scholar
- 18.Régin, J.C.: Generalized arc consistency for global cardinality constraint. In: Proc. AAA 1996, pp. 209–215 (1996)Google Scholar
- 19.Régin, J.C., Puget, J.F.: A Filtering Algorithm for Global Sequencing Constraints. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 32–46. Springer, Heidelberg (1997)CrossRefGoogle Scholar
- 20.Rochart, G.: Explications et programmation par contraintes avancée (in French). Ph.D. thesis, Université de Nantes, France (2005)Google Scholar
- 21.Smith, B.: Succeed-first or Fail-first: A Case Study in Variable and Value Ordering. In: Malyshkin, V.E. (ed.) PaCT 1997. LNCS, vol. 1277, pp. 321–330. Springer, Heidelberg (1997)Google Scholar
- 22.Steiger, R., van Hoeve, W.J., Szymanek, R.: An efficient generic network flow constraint. In: Proc. SAC 2011, pp. 893–900 (2011)Google Scholar
- 23.Tarjan, R.E.: Depth-First Search and Linear Graph Algorithms. SIAM J. Computing 1(2), 146–160 (1972)MathSciNetMATHCrossRefGoogle Scholar
- 24.Vanhoucke, M., Maenhout, B.: NSPLib – A Nurse Scheduling Problem Library: A tool to evaluate (meta-)heuristic procedures. In: Proc. ORAHS 2005 (2005)Google Scholar