| Balanced metric labeling |
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Annual ACM Symposium on Theory of Computing
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Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
table of contents
Baltimore, MD, USA
SESSION: Session 11B
table of contents
Pages: 582 - 591
Year of Publication: 2005
ISBN:1-58113-960-8
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Downloads (6 Weeks): 8, Downloads (12 Months): 54, Citation Count: 0
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ABSTRACT
We define the balanced metric labeling problem, a generalization of the metric labeling problem, in which each label has a capacity, i.e., at most l vertices can be assigned to it. The balanced metric labeling problem is a generalization of fundamental problems in the area of approximation algorithms, e.g., arrangements and balanced partitions of graphs. It is also motivated by resource limitations in certain practical scenarios. We focus on the case where the given metric is uniform and note that this case alone encompasses various well-known graph partitioning problems. We present the first (pseudo) approximation algorithm for this problem, achieving for any ε, 0 < ε < 1, an approximation factor of O((ln n)/ε), while assigning at most min {O(ln k)/1 - ε, l + 1| ( 1 + ε) l vertices to each label (k is the number of labels). Our approximation algorithm is based on a novel randomized rounding of a linear programming formulation that combines an embedding of the graph in a simplex together with spreading metrics and additional constraints that strengthen the formulation. Our randomized rounding technique uses both a randomized metric decomposition technique and a randomized label assignment technique. At the heart of our approach is the fact that only limited dependency is created between the labels assigned to different vertices, allowing us to bound the expected cost of the solution and the number of vertices assigned to each label, simultaneously. We note that the number of vertices assigned to each label is bounded via a new inequality of Janson[15] for tail bounds of (partly) dependent random variables.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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Sanjeev Arora , Satish Rao , Umesh Vazirani, Expander flows, geometric embeddings and graph partitioning, Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, p.222-231, June 13-16, 2004, Chicago, IL, USA
[doi> 10.1145/1007352.1007355]
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Chandra Chekuri , Sanjeev Khanna , Joseph Naor , Leonid Zosin, Approximation algorithms for the metric labeling problem via a new linear programming formulation, Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms, p.109-118, January 07-09, 2001, Washington, D.C., United States
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Gruia Calinescu , Howard Karloff , Yuval Rabani, Approximation algorithms for the 0-extension problem, Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms, p.8-16, January 07-09, 2001, Washington, D.C., United States
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