Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Radio Link Frequency Assignment
Constraints
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Arc consistency for soft constraints
Artificial Intelligence
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Memory intensive branch-and-bound search for graphical models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Exploiting tree decomposition and soft local consistency in weighted CSP
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Dynamic management of heuristics for solving structured CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Soft arc consistency revisited
Artificial Intelligence
Towards parallel non serial dynamic programming for solving hard weighted CSP
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Dynamic virtual arc consistency
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and tree-decomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.