非光滑优化方法在鲁棒估计中的分析
Analysis of A Nonsmooth Optimization Approach to Robust Estimation
AI总结：
本文研究了在存在间歇性和大误差的测量数据下，通过非光滑凸优化方法实现线性映射识别的问题，证明在适当条件下可从大量粗误差数据中恢复真实参数向量。
摘要：
本文考虑了从受间歇性和任意大误差影响的测量中识别线性映射的问题，该问题在许多估计相关应用中具有根本性，如故障检测、损失性网络中的状态估计、混合系统辨识、鲁棒估计等。由于其内在的组合特性，该问题具有挑战性。因此，获得有效解需要既可合理求解又在意义上能返回真实参数向量的松弛方法。本文讨论了一种非光滑凸优化方法，并提供了其行为的新分析。特别是，在适当的数据条件下，证明可以从受大量（甚至无限量）粗误差污染的数据中恢复精确估计。
英文摘要：
In this paper, we consider the problem of identifying a linear map from measurements which are subject to intermittent and arbitarily large errors. This is a fundamental problem in many estimation-related applications such as fault detection, state estimation in lossy networks, hybrid system identification, robust estimation, etc. The problem is hard because it exhibits some intrinsic combinatorial features. Therefore, obtaining an effective solution necessitates relaxations that are both solvable at a reasonable cost and effective in the sense that they can return the true parameter vector. The current paper discusses a nonsmooth convex optimization approach and provides a new analysis of its behavior. In particular, it is shown that under appropriate conditions on the data, an exact estimate can be recovered from data corrupted by a large (even infinite) number of gross errors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
