具有最优性保证的 A 优化实验设计一阶贪婪算法

Christian Aarset
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引用次数: 0

摘要

优化实验设计(OED)关注的是确定理想的数据收集方法,例如通过传感器的放置。emph{greedyalgorithm},即以迭代优化的方式一次放置一个传感器,是一种极其稳健且易于执行的算法。然而,这种算法是否会导致次优状态,目前尚不清楚。利用作者最近在 OED 非光滑凸优化标准方面的研究成果,我们在此提出一个框架,用于验证贪婪算法的全局最优性,并采用基于梯度的加速方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A first-order greedy algorithm for A-optimal experimental design with optimality guarantee
Optimal experimental design (OED) concerns itself with identifying ideal methods of data collection, e.g.~via sensor placement. The \emph{greedy algorithm}, that is, placing one sensor at a time, in an iteratively optimal manner, stands as an extremely robust and easily executed algorithm for this purpose. However, it is a priori unclear whether this algorithm leads to sub-optimal regimes. Taking advantage of the author's recent work on non-smooth convex optimality criteria for OED, we here present a framework for verifying global optimality for the greedy algorithm, as well as employing gradient-based speed-ups.
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