A fast and simple randomized parallel algorithm for the maximal independent set problem
Journal of Algorithms
A simple parallel algorithm for the maximal independent set problem
STOC '85 Proceedings of the seventeenth annual ACM symposium on Theory of computing
Greedy strikes back: improved facility location algorithms
Journal of Algorithms
Improved approximation algorithms for a capacitated facility location problem
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Distributed computing: a locality-sensitive approach
Distributed computing: a locality-sensitive approach
Improved Approximation Algorithms for Metric Facility Location Problems
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Group Strategyproof Mechanisms via Primal-Dual Algorithms
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP
Journal of the ACM (JACM)
Facility location: distributed approximation
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
A distributed O(1)-approximation algorithm for the uniform facility location problem
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem
APPROX '07/RANDOM '07 Proceedings of the 10th International Workshop on Approximation and the 11th International Workshop on Randomization, and Combinatorial Optimization. Algorithms and Techniques
ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
Return of the primal-dual: distributed metric facilitylocation
Proceedings of the 28th ACM symposium on Principles of distributed computing
Algorithmica - Special Issue: Scandinavian Workshop on Algorithm Theory; Guest Editor: Joachim Gudmundsson
Parallel approximation algorithms for facility-location problems
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Linear-work greedy parallel approximate set cover and variants
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Facility location in sublinear time
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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We present two distributed, constant factor approximation algorithms for the metric facility location problem. Both algorithms have been designed with a strong emphasis on applicability in the area of wireless sensor networks: in order to execute them, each sensor node only requires limited local knowledge and simple computations. Also, the algorithms can cope with measurement errors and take into account that communication costs between sensor nodes do not necessarily increase linearly with the distance, but can be represented by a polynomial. Since it cannot always be expected that sensor nodes execute algorithms in a synchronized way, our algorithms are executed in an asynchronous model (but they are still able to break symmetry that might occur when two neighboring nodes act at exactly the same time). Furthermore, they can deal with dynamic scenarios: if a node moves, the solution is updated and the update affects only nodes in the local neighborhood. Finally, the algorithms are robust in the sense that incorrect behavior of some nodes during some round will, in the end, still result in a good approximation. The first algorithm runs in expected $\mathcal{O}(\log_{1+\epsilon} n)$ communication rounds and yields a $#956;4(1+4$#956;2(1+ε)1/p)p approximation, while the second has a running time of expected $\mathcal{O}(\log_{1+\epsilon}^2 n)$ communication rounds and an approximation factor of $#956;4(1+2(1+ε)1/p)p. Here, ε0 is an arbitrarily small constant, p the exponent of the polynomial representing the communication costs, and $#956; the relative measurement error.