Branch and peg algorithms for the simple plant location problem
Computers and Operations Research
Branch and peg algorithms for the simple plant location problem
Computers and Operations Research
Journal of Global Optimization
Equivalent instances of the simple plant location problem
Computers & Mathematics with Applications
Non-monotone submodular maximization under matroid and knapsack constraints
Proceedings of the forty-first annual ACM symposium on Theory of computing
A utility-theoretic approach to privacy and personalization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
SFO: A Toolbox for Submodular Function Optimization
The Journal of Machine Learning Research
Reliable Facility Location Design Under the Risk of Disruptions
Operations Research
A utility-theoretic approach to privacy in online services
Journal of Artificial Intelligence Research
Maximizing Nonmonotone Submodular Functions under Matroid or Knapsack Constraints
SIAM Journal on Discrete Mathematics
Branch and bound strategies for non-maximal suppression in object detection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Maximizing Non-monotone Submodular Functions
SIAM Journal on Computing
Submodular maximization by simulated annealing
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
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The Data-Correcting (DC) Algorithm is a recursive branch-and-bound type algorithm, in which the data of a given problem instance are "heuristically corrected" at each branching in such a way that the new instance will be as close as possible to polynomially solvable and the result satisfies a prescribed accuracy (the difference between optimal and current solution). In this paper the DC algorithm is applied to determining exact or approximate global minima of supermodular functions. The working of the algorithm is illustrated by an instance of the Simple Plant Location (SPL) Problem. Computational results, obtained for the Quadratic Cost Partition Problem (QCP), show that the DC algorithm outperforms a branch-and-cut algorithm, not only for sparse graphs but also for nonsparse graphs (with density more than 40%), often with speeds 100 times faster.