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Mixed-integer linear programming formulations and column generation algorithms for the Minimum Normalized Cuts problem on networks
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DOI：10.1016/j.ejor.2024.02.033.png)
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Abstract
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This paper deals with the k-way normalized cut problem in complex networks. It presents a methodology that uses mathematical optimization to provide mixed-integer linear programming formulations for the problem. The paper also develops a branch-and-price algorithm for the above-mentioned problem which scales better than the compact formulations. Additionally, a heuristic algorithm which is able to approximate largescale image problems in those cases where the exact methods are not applicable is presented. Extensive computational experiments assess the usefulness of these methods to solve the k-way normalized cut problem. Finally, we have applied the minimum normalized cut objective function to the segmentation of actual images, showing the applicability of the introduced methodology.
Keywords:
Complex networks detection
Community detection
Mathematical programming
Image segmentation
Normalized cuts
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