The Parameterized Complexity of k-Flip Local Search for SAT and MAX SAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Local search: is brute-force avoidable?
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Enumerating all solutions of a boolean CSP by non-decreasing weight
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Constraint satisfaction parameterized by solution size
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
Local search: Is brute-force avoidable?
Journal of Computer and System Sciences
The parameterized complexity of k-flip local search for SAT and MAX SAT
Discrete Optimization
Stable assignment with couples: Parameterized complexity and local search
Discrete Optimization
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We study the complexity of local search for the Boolean constraint satisfaction problem (CSP), in the following form: given a CSP instance, that is, a collection of constraints, and a solution to it, the question is whether there is a better (lighter, i.e., having strictly less Hamming weight) solution within a given distance from the initial solution. We classify the complexity, both classical and parameterized, of such problems by a Schaefer-style dichotomy result, that is, with a restricted set of allowed types of constraints. Our results show that there is a considerable amount of such problems that are NP-hard, but fixed-parameter tractable when parameterized by the distance.