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      On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers

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      Journal of Scientific Computing
      Springer Nature

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          Monotone Operators and the Proximal Point Algorithm

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            On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators

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              An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision.

              After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
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                Author and article information

                Journal
                Journal of Scientific Computing
                J Sci Comput
                Springer Nature
                0885-7474
                1573-7691
                March 2016
                May 2015
                : 66
                : 3
                : 889-916
                Article
                10.1007/s10915-015-0048-x
                eea33ae5-78df-4914-8883-5533acdf1aed
                © 2016
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