论文标题

正交组的亚组的同步问题的统一方法

A Unified Approach to Synchronization Problems over Subgroups of the Orthogonal Group

论文作者

Liu, Huikang, Yue, Man-Chung, So, Anthony Man-Cho

论文摘要

$ \ Mathcal {G} $对同步的问题旨在估算组元素的集合$ g^*_ 1,\ dots,g^*_ n \ in \ Mathcal {g} $,基于嘈杂的观察到所有成对的子集的噪声观察,$ g^*_ i {g^*_ i {g^^*_} $ g^*_ i {这种问题最近引起了很多关注,并在广泛的科学和工程领域发现了许多应用。在本文中,我们考虑了该组是正交组的封闭子组的一类同步问题。该课程涵盖了实践中出现的许多小组同步问题。我们的贡献是五倍。首先,我们提出了一种统一的方法来解决这类组同步问题,该方法包括基于广义能力方法的合适初始化步骤和迭代的完善步骤,并表明它在对组,测量图,噪声和初始化的某些假设下对估计误差具有强大的理论保证。其次,我们制定了两个几何条件,这些几何条件是我们的方法所要求的,并表明它们适用于正交组的各种实际相关亚组。条件与亚组的错误结合几何形状密切相关 - 优化的重要概念。第三,我们验证标准随机图和随机矩阵模型的测量图和噪声上的假设。第四,根据度量熵的经典概念,我们开发和分析了一种新型的光谱型估计量。最后,我们通过广泛的数值实验表明,我们所提出的非凸方法在计算速度,可扩展性和/或估计误差方面的表现优于现有方法。

The problem of synchronization over a group $\mathcal{G}$ aims to estimate a collection of group elements $G^*_1, \dots, G^*_n \in \mathcal{G}$ based on noisy observations of a subset of all pairwise ratios of the form $G^*_i {G^*_j}^{-1}$. Such a problem has gained much attention recently and finds many applications across a wide range of scientific and engineering areas. In this paper, we consider the class of synchronization problems in which the group is a closed subgroup of the orthogonal group. This class covers many group synchronization problems that arise in practice. Our contribution is fivefold. First, we propose a unified approach for solving this class of group synchronization problems, which consists of a suitable initialization step and an iterative refinement step based on the generalized power method, and show that it enjoys a strong theoretical guarantee on the estimation error under certain assumptions on the group, measurement graph, noise, and initialization. Second, we formulate two geometric conditions that are required by our approach and show that they hold for various practically relevant subgroups of the orthogonal group. The conditions are closely related to the error-bound geometry of the subgroup -- an important notion in optimization. Third, we verify the assumptions on the measurement graph and noise for standard random graph and random matrix models. Fourth, based on the classic notion of metric entropy, we develop and analyze a novel spectral-type estimator. Finally, we show via extensive numerical experiments that our proposed non-convex approach outperforms existing approaches in terms of computational speed, scalability, and/or estimation error.

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