Reversible DAC and other improvements for solving Max-CSP
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
An Original Constraint Based Approach for Solving over Constrained Problems
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
New Lower Bounds of Constraint Violations for Over-Constrained Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Lower Bounds for Non-binary Constraint Optimization Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A Hybrid Framework for Over-Constrained Generalized
Artificial Intelligence Review
A hybrid framework for over-constrained generalized resource-constrained project scheduling problems
Artificial Intelligence Review
A new local consistency for weighted CSP dedicated to long domains
Proceedings of the 2006 ACM symposium on Applied computing
A New Scheme for Mobility, Sensing, and Security Management in Wireless Ad Hoc Sensor Networks
ANSS '06 Proceedings of the 39th annual Symposium on Simulation
A Branch and Bound Algorithm for Numerical MAX-CSP
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Bounds arc consistency for weighted CSPs
Journal of Artificial Intelligence Research
Mobility, sensing, and security management in wireless ad hoc sensor systems
Computers and Electrical Engineering
Soft constraints of difference and equality
Journal of Artificial Intelligence Research
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A Max-CSP consists of searching for a solution which minimizes the number of violated constraints. The best existing solving algorithm is PFC-MRDAC. It is based on the computation of a lower bound of the number of violations. To compute this lower bound it is required to evaluate the violations with respect to each value of each domain. Unfortunately, some applications imply thousands of variables with huge domains. In scheduling, it arises that numerous activities have to be scheduled over several months with a unit of time of a few minutes. In this situation using PFC-MRDAC requires a large amount of memory which can prevent from using it. In this paper, we propose an algorithm called the Range-based Max-CSP Algorithm (RMA), based on the exploitation of bound-based filtering algorithms of constraints. This technique does not require to study each value of each domain: its complexity depends only on the number of variables and the number of constraints. No assumption is made on the constraints except that their filtering algorithms are related to the bounds of the involved variables, the general case for scheduling constraints. Then, when the only information available for a variable x w.r.t. a constraint C are the new bounds of D(x) obtained by applying the filtering algorithm of C, the lower bounds of violations provided by PFC-MRDAC and RMA are identical.