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ReSync: Riemannian Subgradient-based Robust Rotation Synchronization

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arxiv 2305.15136 v2 pith:OSI6N6GQ submitted 2023-05-24 math.OC cs.LG

classification math.OCcs.LG
keywords resyncrotationsground-truthrotationformulationguaranteeslocalproperty
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This work presents ReSync, a Riemannian subgradient-based algorithm for solving the robust rotation synchronization problem, which arises in various engineering applications. ReSync solves a least-unsquared minimization formulation over the rotation group, which is nonsmooth and nonconvex, and aims at recovering the underlying rotations directly. We provide strong theoretical guarantees for ReSync under the random corruption setting. Specifically, we first show that the initialization procedure of ReSync yields a proper initial point that lies in a local region around the ground-truth rotations. We next establish the weak sharpness property of the aforementioned formulation and then utilize this property to derive the local linear convergence of ReSync to the ground-truth rotations. By combining these guarantees, we conclude that ReSync converges linearly to the ground-truth rotations under appropriate conditions. Experiment results demonstrate the effectiveness of ReSync.

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  1. Orientation Determination of Cryo-EM Images Using Block Stochastic Riemannian Subgradient Methods

    math.OC 2024-11 conditional novelty 5.0 of 10

    A block stochastic Riemannian subgradient algorithm solves the least unsquared deviation orientation-determination problem in cryo-EM with speedups of 5 to 30x and an O(epsilon^-4) convergence guarantee.

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