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Balanced metric labeling
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Source Annual ACM Symposium on Theory of Computing archive
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
Authors
Joseph (Seffi) Naor  Technion, Haifa, Israel
Roy Schwartz  Technion, Haifa, Israel
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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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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N. Alon and J. Spencer. The probabilistic method. Wiley, New York, 2000.
 
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Collaborative Colleagues:
Joseph (Seffi) Naor: colleagues
Roy Schwartz: colleagues