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We introduce a new approach to computing an approximately maximum s-t flow in a capacitated, undirected graph. This flow is computed by solving a sequence of electrical flow problems. Each electrical flow is given by the solution of a system of linear equations in a Laplacian matrix, and thus may be approximately computed in nearly-linear time. Using this(More)
We present an O(m 10 7) = O(m 1.43)-time 1 algorithm for the maximum s-t flow and the minimum s-t cut problems in directed graphs with unit capacities. This is the first improvement over the sparse-graph case of the long-standing O(m min{ √ m, n 2/3 }) running time bound due to Even and Tarjan [ET75] and Karzanov [Kar73]. By well-known reductions, this also(More)
We combine the work of Garg and Konemann, and Fleischer with ideas from dynamic graph algorithms to obtain faster (1-ε)-approximation schemes for various versions of the multicommodity flow problem. In particular, if ε is moderately small and the size of every number used in the input instance is polynomially bounded, the running times of our(More)
We study theoretical runtime guarantees for a class of optimization problems that occur in a wide variety of inference problems. These problems are motivated by the LASSO framework and have applications in machine learning and computer vision. Our work shows a close connection between these problems and core questions in algorithmic graph theory. While this(More)
We study packet buffering, a basic problem occurring in network switches. We construct an optimal 16/13-competitive randomized online algorithm PB for the case of two input buffers of arbitrary sizes. Our proof is based on geometrical transformations which allow to identify the set of sequences incurring extremal competitive ratios. Later we may analyze the(More)