Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Solving Hard Satisfiability Problems: A Unified Algorithm Based on Discrete Lagrange Multipliers
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Performance test of local search algorithms using new types of random CNF formulas
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Towards an understanding of hill-climbing procedures for SAT
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Adding new clauses for faster local search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Over the years, a steadily improving series of local search solvers for propositional satisfiability (SAT) have been constructed. However, these solvers are often fragile, in that they have apparently minor details in their implementation that dramatically affect performance and confound understanding. In order to understand and predict the success of differing strategies, various local search metrics have been proposed. Many of these metrics summarize properties of the boolean assignments examined during the search. This has two consequences: first, they only capture one side of satisfiability, failing to characterize the behaviour with respect to constraints. Secondly, the boolean requirement limits the applicability of these metrics to more general constraint satisfaction problems (CSPs), which can have non-boolean domains. In response, we present dual metrics, derived from existing primal (boolean assignment) metrics, that are based on the states of constraints during the search. Experimental results show a strong relationship between the primal and dual versions of these metrics on a variety of random and structured problems. This dual perspective can be easily applied to both SAT and general CSPs, allowing for new insights into the workings of a broad class of local search methods.