A constraint-based architecture for local search
OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Constraints
Constraint and Integer Programming in OPL
INFORMS Journal on Computing
Constraint-Based Local Search
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IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inferring variable conflicts for local search
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Generic Incremental Algorithms for Local Search
Constraints
Revisiting constraint-directed search
Information and Computation
A Hybrid LS/CP Approach to Solve the Weekly Log-Truck Scheduling Problem
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Memoisation for constraint-based local search
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Constraint-based local search for the automatic generation of architectural tests
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Kangaroo: an efficient constraint-based local search system using lazy propagation
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
On the design of the next generation access networks
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
An automaton Constraint for Local Search
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Towards solver-independent propagators
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Invariants that incrementally maintain the value of expressions under assignments to their variables are a natural abstraction to build high-level local search algorithms. But their functionalities are not sufficient to allow arbitrary expressions as constraints or objective functions as in constraint programming. Differentiable invariants bridge this expressiveness gap. A differentiable invariant maintains the value of an expression and its variable gradients, it supports differentiation to evaluate the effect of local moves. The benefits of differentiable invariants are illustrated on a number of applications which feature complex, possibly reified, expressions and whose models are essentially similar to their CP counterparts. Experimental results demonstrate their practicability.