Sparsification—a technique for speeding up dynamic graph algorithms
Journal of the ACM (JACM)
Randomized fully dynamic graph algorithms with polylogarithmic time per operation
Journal of the ACM (JACM)
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Introduction to Algorithms
Fully Dynamic Maintenance of Vertex Cover
WG '93 Proceedings of the 19th International Workshop on Graph-Theoretic Concepts in Computer Science
Worst-case update times for fully-dynamic all-pairs shortest paths
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
On graph problems in a semi-streaming model
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Approximating the minimum vertex cover in sublinear time and a connection to distributed algorithms
Theoretical Computer Science
Distributed approximate matching
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
Faster dynamic matchings and vertex connectivity
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
An O(v|v| c |E|) algoithm for finding maximum matching in general graphs
SFCS '80 Proceedings of the 21st Annual Symposium on Foundations of Computer Science
Finding graph matchings in data streams
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Algorithms for the transportation problem in geometric settings
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Linear programming in the semi-streaming model with application to the maximum matching problem
Information and Computation
Simple deterministic algorithms for fully dynamic maximal matching
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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We consider the problem of maintaining a large matching and a small vertex cover in a dynamically changing graph. Each update to the graph is either an edge deletion or an edge insertion. We give the first randomized data structure that simultaneously achieves a constant approximation factor and handles a sequence of K updates in K*polylog(n) time, where n is the number of vertices in the graph. Previous data structures require a polynomial amount of computation per update.