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SIAM Journal on Computing
Geometric matching under noise: combinatorial bounds and algorithms
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SIAM Journal on Computing
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The discrepancy method: randomness and complexity
The discrepancy method: randomness and complexity
A formation behavior for large-scale micro-robot force deployment
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Balls and bins models with feedback
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
On Balls and Bins with Deletions
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
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RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
Approximated two choices in randomized load balancing
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
Grid emulation for managing random sensor networks
Ad Hoc Networks
Minimum Delay Data Gathering in Radio Networks
ADHOC-NOW '09 Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks
Managing random sensor networks by means of grid emulation
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
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Given a graph G=(V,E) with |V|=n, we consider the following problem. Place n points on the vertices of G independently and uniformly at random. Once the points are placed, relocate them using a bijection from the points to the vertices that minimizes the maximum distance between the random place of the points and their target vertices. We look for an upper bound on this maximum relocation distance that holds with high probability (over the initial placements of the points). For general graphs, we prove the #P-hardness of the problem and that the maximum relocation distance is $O(\sqrt{n})$ with high probability. We also present a Fully Polynomial Randomized Approximation Scheme when the input graph admits a polynomial-size family of witness cuts while for trees we provide a 2-approximation algorithm.