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A NEW KARZANOVTYPE o(n3) MAXFLOW ALGORITHM
, 1991
"... AbstractA new algorithm is presented for finding maximal and maximum value flows in directed single commodity networks. The algorithm gradually converts a combination of blocking preflows and backflows to a maximal flow in the network. Unlike other maximal flow algorithms, the algorithm treats the ..."
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the network more symmetrically by attempting to increase flow on both the ForwardStep and the BackwardStep. The algorithm belongs to the so called phase algorithms, and is applied to Dinictype layered networks. With an effort of at most 0(n3) for maximum value flow, the algorithm ties with the fastest
Applying parallel computation algorithms in the design of serial algorithms
 J. ACM
, 1983
"... Abstract. The goal of this paper is to point out that analyses of parallelism in computational problems have practical implications even when multiprocessor machines are not available. This is true because, in many cases, a good parallel algorithm for one problem may turn out to be useful for design ..."
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Cited by 248 (7 self)
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for designing an efficient serial algorithm for another problem. A d ~ eframework d for cases like this is presented. Particular cases, which are discussed in this paper, provide motivation for examining parallelism in sorting, selection, minimumspanningtree, shortest route, maxflow, and matrix
Reducing Directed Max Flow to Undirected Max Flow
"... Abstract. In this paper, we show that the directed maximum flow problem can be reduced to the undirected maximum flow problem. Our result yields a new algorithm for finding maximum flows in directed graphs, by reducing any directed maximum flow instance into an undirected maximum flow instance and r ..."
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Cited by 1 (0 self)
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Abstract. In this paper, we show that the directed maximum flow problem can be reduced to the undirected maximum flow problem. Our result yields a new algorithm for finding maximum flows in directed graphs, by reducing any directed maximum flow instance into an undirected maximum flow instance
Brief announcement: better speedups for parallel maxflow
 In Proceedings of the 23rd ACM symposium on Parallelism in algorithms and architectures
, 2011
"... We present a parallel solution to the MaximumFlow (MaxFlow) problem, suitable for a modern manycore architecture. We show that by starting from a PRAM algorithm, following an established “programmer’s workflow ” and targeting XMT, a PRAMinspired manycore architecture, we achieve significantly ..."
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Cited by 3 (1 self)
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We present a parallel solution to the MaximumFlow (MaxFlow) problem, suitable for a modern manycore architecture. We show that by starting from a PRAM algorithm, following an established “programmer’s workflow ” and targeting XMT, a PRAMinspired manycore architecture, we achieve
Balanced Network Flows. VIII. A Revised Theory of PhaseOrdered Algorithms and the O(√nm log(n²/m)/log n) Bound for the Nonbipartite Cardinality Matching Problem
 NETWORKS
, 2003
"... This paper closes some gaps in the discussion of nonweighted balanced network flow problems. These gaps all concern the phaseordered augmentation algorithm, which can be viewed as the matching counterpart of the Dinic maxflow algorithm. We show that this algorithm runs in O(n²m) time compared to t ..."
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Cited by 1 (0 self)
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This paper closes some gaps in the discussion of nonweighted balanced network flow problems. These gaps all concern the phaseordered augmentation algorithm, which can be viewed as the matching counterpart of the Dinic maxflow algorithm. We show that this algorithm runs in O(n²m) time compared
Improved Algorithms For Bipartite Network Flow
, 1994
"... In this paper, we study network flow algorithms for bipartite networks. A network G = (V; E) is called bipartite if its vertex set V can be partitioned into two subsets V 1 and V 2 such that all edges have one endpoint in V 1 and the other in V 2 . Let n = jV j, n 1 = jV 1 j, n 2 = jV 2 j, m = jE ..."
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Cited by 45 (4 self)
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In this paper, we study network flow algorithms for bipartite networks. A network G = (V; E) is called bipartite if its vertex set V can be partitioned into two subsets V 1 and V 2 such that all edges have one endpoint in V 1 and the other in V 2 . Let n = jV j, n 1 = jV 1 j, n 2 = jV 2 j, m = j
A Fast and Simple Algorithm for the Maximum Flow Problem
 OPERATIONS RESEARCH
, 1989
"... We present a simple sequential algorithm for the maximum flow problem on a network with n nodes, m arcs, and integer arc capacities bounded by U. Under the practical assumption that U is polynomially bounded in n, our algorithm runs in time O(nm + n 2 log n). This result improves the previous best b ..."
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Cited by 42 (8 self)
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We present a simple sequential algorithm for the maximum flow problem on a network with n nodes, m arcs, and integer arc capacities bounded by U. Under the practical assumption that U is polynomially bounded in n, our algorithm runs in time O(nm + n 2 log n). This result improves the previous best
Scaling Algorithms for Network Problems
, 1985
"... This paper gives algorithms for network problems that work by scaling the numeric parameters. Assume all parameters are integers. Let n, m, and N denote the number of vertices, number of edges, and largest parameter of the network, respectively. A scaling algorithm for maximum weight matching on a b ..."
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Cited by 76 (2 self)
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: singlesource shortest paths for arbitrary edge lengths (Bellman’s algorithm); maximum weight degreeconstrained subgraph; minimum cost flow on a cl network. Scaling gives a simple maximum value flow algorithm that matches the best known bound (Sleator and Tarjan’s algorithm) when log N = O(log n
The Complexity Of The Maximum Network Flow Problem
, 1980
"... This thesis deals with the computational complexity of the maximum network flow problem. We first introduce the basic concepts and fundamental theorems upon which the study of "maxflow" has been built. We then trace the development of maxflow algorithms from the original "labeling a ..."
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This thesis deals with the computational complexity of the maximum network flow problem. We first introduce the basic concepts and fundamental theorems upon which the study of "maxflow" has been built. We then trace the development of maxflow algorithms from the original "
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