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698
Fibonacci Heaps and Their Uses in Improved Network optimization algorithms
, 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized tim ..."
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Cited by 739 (18 self)
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in the problem graph: ( 1) O(n log n + m) for the singlesource shortest path problem with nonnegative edge lengths, improved from O(m logfmh+2)n); (2) O(n*log n + nm) for the allpairs shortest path problem, improved from O(nm lo&,,,+2,n); (3) O(n*logn + nm) for the assignment problem (weighted bipartite
A new approach to the maximum flow problem
 JOURNAL OF THE ACM
, 1988
"... All previously known efficient maximumflow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortestlength augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the pre ..."
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Cited by 672 (33 self)
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of the algorithm running in O(nm log(n²/m)) time on an nvertex, medge graph. This is as fast as any known method for any graph density and faster on graphs of moderate density. The algorithm also admits efticient distributed and parallel implementations. A parallel implementation running in O(n²log n) time using
Implementation of O(nm log n) Weighted Matchings in General Graphs  The Power of Data Structures
 IN WORKSHOP ON ALGORITHM ENGINEERING (WAE), LECTURE NOTES IN COMPUTER SCIENCE
, 2000
"... We describe the implementation of an O(nm log n) algorithm for weighted matchings in general graphs. The algorithm is a variant of the algorithm of Galil, Micali, and Gabow [GMG86] and requires the use of concatenable priority queues. No previous implementation had a worst{case guarantee of O(nm ..."
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Cited by 7 (1 self)
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We describe the implementation of an O(nm log n) algorithm for weighted matchings in general graphs. The algorithm is a variant of the algorithm of Galil, Micali, and Gabow [GMG86] and requires the use of concatenable priority queues. No previous implementation had a worst{case guarantee of O(nm
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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to the bound of O(nm²) derived in Part III of this series and that the bipartiteness requirement can be omitted. The result which deserves attention is the complexity bound for the cardinality matching problem which was shown for the bipartite case by Feder and Motwani. This bound was previously claimed
I nm u no log ia,
"... Effect of monoclonal antibodies directed against Candida albicans cell wall antigens on the adhesion of the fungus to polystyrene Rosario San Millan, ' Pilar A. Ezkurra, ' Guillermo Quindbs,' ..."
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Effect of monoclonal antibodies directed against Candida albicans cell wall antigens on the adhesion of the fungus to polystyrene Rosario San Millan, ' Pilar A. Ezkurra, ' Guillermo Quindbs,'
A fast bitvector algorithm for approximate string matching based on dynamic programming
 J. ACM
, 1999
"... Abstract. The approximate string matching problem is to find all locations at which a query of length m matches a substring of a text of length n with korfewer differences. Simple and practical bitvector algorithms have been designed for this problem, most notably the one used in agrep. These alg ..."
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Cited by 185 (1 self)
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. These algorithms compute a bit representation of the current stateset of the kdifference automaton for the query, and asymptotically run in either O(nmk/w) orO(nm log �/w) time where w is the word size of the machine (e.g., 32 or 64 in practice), and � is the size of the pattern alphabet. Here we present
The Sample Complexity of Pattern Classification With Neural Networks: The Size of the Weights is More Important Than the Size of the Network
, 1997
"... Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the ne ..."
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Cited by 213 (15 self)
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estimate (that is related to squared error on the training set) plus A³ p (log n)=m (ignori...
Max flows in O(nm) time, or better
, 2012
"... In this paper, we present improved polynomial time algorithms for the max flow problem defined on a network with n nodes and m arcs. We show how to solve the max flow problem in O(nm) time, improving upon the best previous algorithm due to King, Rao, and Tarjan, who solved the max flow problem in O( ..."
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Cited by 7 (0 self)
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(n 1/3 log −3 n), we show how to solve the max flow problem in O(nm / log n) steps. In the case that log(U ∗ ) = O(log k n) for some fixed positive integer k, we show how to solve the max flow problem in Õ(n8/3) time. This latter algorithm relies on a subroutine for fast matrix multiplication. 1
A FASTER STRONGLY POLYNOMIAL MINIMUM COST FLOW ALGORITHM
, 1991
"... In this paper, we present a new strongly polynomial time algorithm for the minimum cost flow problem, based on a refinement of the EdmondsKarp scaling technique. Our algorithm solves the uncapacitated minimum cost flow problem as a sequence of O(n log n) shortest path problems on networks with n no ..."
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Cited by 160 (11 self)
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. Tardos, by a factor of n 2 /m. Our algorithm for the capacitated minimum cost flow problem is even more efficient if the number of arcs with finite upper bounds, say n', is much less than m. In this case, the running time of the algorithm is O((m ' + n)log n(m + n log n)).
Strictly Nonblocking fCast Log d (N;m; p) Networks
"... Abstract—Necessary and sufficient conditions for Log ..."
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