Modern heuristic techniques for combinatorial problems
Using global constraints for local search
DIMACS workshop on on Constraint programming and large scale discrete optimization
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A constraint-based architecture for local search
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Constraint-Based Local Search
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
Generating propagators for finite set constraints
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Inferring variable conflicts for local search
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Set variables and local search
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Revisiting constraint-directed search
Information and Computation
Memoisation for constraint-based local search
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
An automaton Constraint for Local Search
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
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When a new (global) constraint is introduced in local search, measures for the penalty and variable conflicts of that constraint must be defined, and incremental algorithms for maintaining these measures must be implemented. These are complicated and time-consuming tasks, which clearly reduces the productivity of the local-search practitioner. We introduce a generic scheme that, from a description of a constraint in monadic existential second-order logic extended with counting, automatically gives penalty and variable-conflict measures for such a constraint, as well as incremental algorithms for maintaining these measures. We prove that our variable-conflict measure for a variable x is lower-bounded by the maximum penalty decrease that may be achieved by only changing the value of x, as well as upper bounded by the penalty measure. Without these properties, the local search performance may degrade. We also demonstrate the usefulness of the approach by replacing a built-in global constraint by a modelled version, while still obtaining competitive results in terms of runtime and robustness. This is especially attractive when a particular (global) constraint is not built in.