Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Nonserial Dynamic Programming
Improving connectionist energy minimization
Journal of Artificial Intelligence Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
On the feasibility of distributed constraint satisfaction
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
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GSAT is a randomized greedy local repair procedure that was introduced for solving propositional satisfiability and constraint satisfaction problems. We present an improvement to GSAT that is sensitive to the problem's structure. When the problem has a tree structure the algorithm is guaranteed to find a solution in linear time. For non-tree networks, the algorithm designates a subset of nodes, called cutset, and executes a regular GSAT algorithm on this set of variables. On all the rest of the variables it executes a specialized local search algorithm for trees. This algorithm finds an assignment that, like GSAT, locally minimizes the sum of unsatisfied constraints and also globally minimizes the number of conflicts in every tree-like subnetwork. We will present results of experiments showing that this new algorithm outperforms regular GSAT on sparse networks whose cycle-cutset size is bounded by 30% of the nodes.