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- W2584162521 abstract "We study the quadratic assignment problem, in computer vision also known as graph matching. Two leading solvers for this problem optimize the Lagrange decomposition duals with sub-gradient and dual ascent (also known as message passing) updates. We explore this direction further and propose several additional Lagrangean relaxations of the graph matching problem along with corresponding algorithms, which are all based on a common dual ascent framework. Our extensive empirical evaluation gives several theoretical insights and suggests a new state-of-the-art anytime solver for the considered problem. Our improvement over state-of-the-art is particularly visible on a new dataset with large-scale sparse problem instances containing more than 500 graph nodes each." @default.
- W2584162521 created "2017-02-10" @default.
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- W2584162521 date "2017-07-01" @default.
- W2584162521 modified "2023-09-26" @default.
- W2584162521 title "A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching" @default.
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- W2584162521 doi "https://doi.org/10.1109/cvpr.2017.747" @default.
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