Approximated consistency for the automatic recording problem

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3709 LNCS, Issue , 2005, Pages 822-826

  Sellmann, Meinolf ^a  

^a Brown Univerity   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33646179006     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564751_72     Document Type: Conference Paper

References (9)

1. T. Fahle and M. Sellmann. Cost-Based Filtering for the Constrained Knapsack Problem. Annals of Operations Research, 115:73-93, 2002.
2. F. Focacci, A. Lodi, M. Milano. Cost-Based Domain Filtering. Principles and Practice of Constraint Programming (CP) Springer LNCS 1713:189-203, 1999.
3. E.G. Freuder. A Sufficient Condition for Backtrack-Free Search. Journal of the ACM, 29(1):24-32, 1982.
4. O.H. Ibarra and C.E. Kim. Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems. Journal of the ACM, 22(4):463-468, 1975.
5. M. Milano. Integration of Mathematical Programming and Constraint Programming for Combinatorial Optimization Problems, Tutorial at CP2000, 2000.
6. M. Sellmann. The Practice of Approximated Consistency for Knapsack Constraints. Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI), AAAI Press, pp. 179-184, 2004.
7. M. Sellmann. Approximated Consistency for Knapsack Constraints. CP, Springer LNCS 2833: 679-693, 2003.
8. M. Sellmann and T. Fahle. Constraint Programming Based Lagrangian Relaxation for the Automatic Recording Problem. Annals of Operations Research, 118:17-33, 2003.
9. M. Trick. A Dynamic Programming Approach for Consistency and Propagation for Knapsack Constraints. 3rd International Workshop on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR), pp. 113-124, 2001.