A new approach to the minimum cut problem
Journal of the ACM (JACM)
Approximating s-t minimum cuts in Õ(n2) time
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Using random sampling to find maximum flows in uncapacitated undirected graphs
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Finding maximum flows in undirected graphs seems easier than bipartite matching
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Better random sampling algorithms for flows in undirected graphs
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Random Sampling in Cut, Flow, and Network Design Problems
Mathematics of Operations Research
Beyond the flow decomposition barrier
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Flows in undirected unit capacity networks
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Weighted random sampling with a reservoir
Information Processing Letters
Maximum Bipartite Flow in Networks with Adaptive Channel Width
ICALP '09 Proceedings of the 36th Internatilonal Collogquium on Automata, Languages and Programming: Part II
On computing minimum (s,t)-cuts in digraphs
Information Processing Letters
Weighted random sampling with a reservoir
Information Processing Letters
Maximum bipartite flow in networks with adaptive channel width
Theoretical Computer Science
A general framework for graph sparsification
Proceedings of the forty-third annual ACM symposium on Theory of computing
A new approach to computing maximum flows using electrical flows
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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Consider an n-vertex, m-edge, undirected graph with maximum flow value v. We give a new Õ(m+nv)-time maximum flow algorithm based on finding augmenting paths in random samples of the edges of residual graphs. After assigning certain special sampling probabilities to edges in Õ(m) time, our algorithm is very simple: repeatedly find an augmenting path in a random sample of edges from the residual graph.