论文标题
自适应综合在线优化：静态和动态环境中的预测
Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments
论文作者
论文摘要
在过去的几年中，由于其灵活的实时性质和强大的性能保证，在线凸优化（OCO）在控制文献中受到了显着关注。在本文中，我们提出了新的步进规则和OCO算法，这些规则和OCO算法同时利用梯度预测，功能预测和动态，以及与控制应用程序特别相关的功能。根据参考作用序列，梯度预测误差和函数预测误差的动态，所提出的算法具有静态和动态的遗憾界限，这是文献中已知的规律性测量值的概括。我们介绍了凸面和强劲凸成本的结果。我们在轨迹跟踪案例研究中验证了所提出的算法的性能，以及使用现实世界数据集的投资组合优化。
In the past few years, Online Convex Optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this paper, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics, features particularly pertinent to control applications. The proposed algorithms enjoy static and dynamic regret bounds in terms of the dynamics of the reference action sequence, gradient prediction error, and function prediction error, which are generalizations of known regularity measures from the literature. We present results for both convex and strongly convex costs. We validate the performance of the proposed algorithms in a trajectory tracking case study, as well as portfolio optimization using real-world datasets.
