Randomized rounding: a technique for provably good algorithms and algorithmic proofs
Combinatorica - Theory of Computing
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
.879-approximation algorithms for MAX CUT and MAX 2SAT
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Approximation of Constraint Satisfaction via Local Search (Extended Abstract)
WADS '95 Proceedings of the 4th International Workshop on Algorithms and Data Structures
Optimization-based Heuristics for Maximal Constraint Satisfaction
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Heuristic Methods for Over-Constrained Constraint Satisfaction Problems
Over-Constrained Systems
Randomized approximations of the constraint satisfaction problem
Nordic Journal of Computing
Derandomizing semidefinite programming based approximation algorithms
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
A Semidefinite Programming Approach to Side Chain Positioning with New Rounding Strategies
INFORMS Journal on Computing
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We consider the Weighted Constraint Satisfaction Problem which is an important problem in Artificial Intelligence. Given a set of variables, their domains and a set of constraints between variables, our goal is to obtain an assignment of the variables to domain values such that the weighted sum of satisfied constraints is maximized. In this paper, we present a new approach based on randomized rounding of semidefinite programming relaxation. Besides having provable worst-case bounds for domain sizes 2 and 3, our algorithm is simple and efficient in practice, and produces better solutions than some other polynomial-time algorithms such as greedy and randomized local search.