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• W2962981076 abstract "In this work we study convex relaxations of quadratic optimisation problems over permutation matrices. While existing semidefinite programming approaches can achieve remarkably tight relaxations, they have the strong disadvantage that they lift the original n×n-dimensional variable to an n2×n2-dimensional variable, which limits their practical applicability. In contrast, here we present a lifting-free convex relaxation that is provably at least as tight as existing (lifting-free) convex relaxations. We demonstrate experimentally that our approach is superior to existing convex and non-convex methods for various problems, including image arrangement and multi-graph matching." @default.
• W2962981076 created "2019-07-30" @default.
• W2962981076 creator A5016208719 @default.
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• W2962981076 date "2018-06-01" @default.
• W2962981076 modified "2023-10-01" @default.
• W2962981076 title "DS: Tighter Lifting-Free Convex Relaxations for Quadratic Matching Problems" @default.
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• W2962981076 doi "https://doi.org/10.1109/cvpr.2018.00453" @default.
• W2962981076 hasPublicationYear "2018" @default.
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