Arc and path consistence revisited
Artificial Intelligence
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Arc-consistency and arc-consistency again
Artificial Intelligence
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
Artificial Intelligence
Mixed logical-linear programming
Discrete Applied Mathematics - Special issue on the satisfiability problem and Boolean functions
New bundle methods for solving Lagrangian relaxation dual problems
Journal of Optimization Theory and Applications
Optimization-Oriented Global Constraints
Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Constraint Programming Contribution to Benders Decomposition: A Case Study
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A scheme for unifying optimization and constraint satisfaction methods
The Knowledge Engineering Review
The Linear Programming Polytope of Binary Constraint Problems with Bounded Tree-Width
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Cost propagation: numerical propagation for optimization problems
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
A totally unimodular description of the consistent value polytope for binary constraint programming
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Hi-index | 0.00 |
This paper presents a CSPs filtering method combining arc-consistency and dual Lagrangean relaxation techniques. First, we model the constraint satisfaction problem as a 0/1 linear integer program (IP); then, the consistency of a value is defined as an optimization problem on which a dual Lagrangean relaxation is defined. While solving the dual Lagrangean relaxation, values inconsistencies may be detected (dual Lagrangean inconsistent values); the constraint propagation of this inconsistency can be performed by arc-consistency. After having made the CSP arc-consistent, the process iteratively selects values of variables which may be dual Lagrangean inconsistent. Computational experiments performed over randomly generated problems show the advantages of the hybrid filtering technique combining arc-consistency and dual Lagrangean relaxation.