On finding lowest common ancestors: simplification and parallelization
SIAM Journal on Computing
Efficient parallel evaluation of straight-line code and arithmetic circuits
SIAM Journal on Computing
Optimal and sublogarithmic time randomized parallel sorting algorithms
SIAM Journal on Computing
An introduction to parallel algorithms
An introduction to parallel algorithms
Recursive star-tree parallel data structure
SIAM Journal on Computing
A Graph-Theoretic Game and its Application to the $k$-Server Problem
SIAM Journal on Computing
On approximating arbitrary metrices by tree metrics
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Probabilistic approximation of metric spaces and its algorithmic applications
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Local Search Heuristics for k-Median and Facility Location Problems
SIAM Journal on Computing
Optimal Time Bounds for Approximate Clustering
Machine Learning
A tight bound on approximating arbitrary metrics by tree metrics
Journal of Computer and System Sciences - Special issue: STOC 2003
Parallel approximation algorithms for facility-location problems
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
An O(pn2) algorithm for the p -median and related problems on tree graphs
Operations Research Letters
Parallel graph decompositions using random shifts
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
| Hi-index | 0.00 |
This paper presents parallel algorithms for embedding an arbitrary n-point metric space into a distribution of dominating trees with O(log n) expected stretch. Such embedding has proved useful in the design of many approximation algorithms in the sequential setting. We give a parallel algorithm that runs in O(n2 log n) work and O(log2 n) depth---these bounds are independent of Δ = (maxx,y d(x,y))/(minx≠ y d(x,y)), the ratio of the largest to smallest distance. Moreover, when Δ is exponentially bounded (Δ ≤ 2O(n)), our algorithm can be improved to O(n2) work and O(log2 n) depth. Using these results, we give an RNC O(log k)-approximation algorithm for k-median and an RNC O(log n)-approximation for buy-at-bulk network design. The k-median algorithm is the first RNC algorithm with non-trivial guarantees for arbitrary values of k, and the buy-at-bulk result is the first parallel algorithm for the problem.