Miguel Anjos

Professor and Chair of Operational Research



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Miguel Anjos

Professor and Chair of Operational Research




Miguel Anjos

Professor and Chair of Operational Research



Solving k -way graph partitioning problems to optimality: The impact of semidefinite relaxations and the bundle method


Chapter


M.F. Anjos, B. Ghaddar, L. Hupp, F. Liers, A. Wiegele
Facets of Combinatorial Optimization, Springer, 2013


Semantic Scholar DOI
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Cite

APA   Click to copy
Anjos, M. F., Ghaddar, B., Hupp, L., Liers, F., & Wiegele, A. (2013). Solving k -way graph partitioning problems to optimality: The impact of semidefinite relaxations and the bundle method. In Facets of Combinatorial Optimization. Springer. https://doi.org/10.1007/978-3-642-38189-8_15


Chicago/Turabian   Click to copy
Anjos, M.F., B. Ghaddar, L. Hupp, F. Liers, and A. Wiegele. “Solving k -Way Graph Partitioning Problems to Optimality: The Impact of Semidefinite Relaxations and the Bundle Method.” In Facets of Combinatorial Optimization. Springer, 2013.


MLA   Click to copy
Anjos, M. F., et al. “Solving k -Way Graph Partitioning Problems to Optimality: The Impact of Semidefinite Relaxations and the Bundle Method.” Facets of Combinatorial Optimization, Springer, 2013, doi:10.1007/978-3-642-38189-8_15.


BibTeX   Click to copy

@inbook{m2013a,
  title = {Solving k -way graph partitioning problems to optimality: The impact of semidefinite relaxations and the bundle method},
  year = {2013},
  publisher = {Springer},
  doi = {10.1007/978-3-642-38189-8_15},
  author = {Anjos, M.F. and Ghaddar, B. and Hupp, L. and Liers, F. and Wiegele, A.},
  booktitle = {Facets of Combinatorial Optimization}
}

Abstract

This paper is concerned with computing global optimal solutions for maximum k-cut problems. We improve on the SBC algorithm of Ghaddar, Anjos and Liers in order to compute such solutions in less time. We extend the design principles of the successful BiqMac solver for maximum 2-cut to the general maximum k-cut problem. As part of this extension, we investigate different ways of choosing variables for branching. We also study the impact of the separation of clique inequalities within this new framework and observe that it frequently reduces the number of subproblems considerably. Our computational results suggest that the proposed approach achieves a drastic speedup in comparison to SBC, especially when k=3. We also made a comparison with the orbitopal fixing approach of Kaibel, Peinhardt and Pfetsch. The results suggest that, while their performance is better for sparse instances and larger values of k, our proposed approach is superior for smaller k and for dense instances of medium size. Furthermore, we used CPLEX for solving the ILP formulation underlying the orbitopal fixing algorithm and conclude that especially on dense instances the new algorithm outperforms CPLEX by far.




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