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Combinatorial optimization: networks and matroids
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The article was published on 2021-08-16 and is currently open access. It has received 2526 citations till now. The article focuses on the topics: Combinatorial explosion & Optimization problem.read more
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Future paths for integer programming and links to artificial intelligence
TL;DR: Four key areas of Integer programming are examined from a framework that links the perspectives of artificial intelligence and operations research, and each has characteristics that appear usefully relevant to developments on the horizon.
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The ellipsoid method and its consequences in combinatorial optimization
TL;DR: The method yields polynomial algorithms for vertex packing in perfect graphs, for the matching and matroid intersection problems, for optimum covering of directed cuts of a digraph, and for the minimum value of a submodular set function.
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A new approach to the maximum-flow problem
TL;DR: An alternative method based on the preflow concept of Karzanov, which runs as fast as any other known method on dense graphs, achieving an O(n) time bound on an n-vertex graph and faster on graphs of moderate density.
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Thirty years of graph matching in pattern recognition
TL;DR: This paper will try to characterize the role that graphs play within the Pattern Recognition field, and presents two taxonomies that include almost all the graph matching algorithms proposed from the late seventies and describes the different classes of algorithms.
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The Complexity of Markov Decision Processes
TL;DR: All three variants of the classical problem of optimal policy computation in Markov decision processes, finite horizon, infinite horizon discounted, and infinite horizon average cost are shown to be complete for P, and therefore most likely cannot be solved by highly parallel algorithms.