论文标题
集中和分布式的在线学习，以稀疏时变优化
Centralized and distributed online learning for sparse time-varying optimization
论文作者
论文摘要
在过去几年中，在线算法的开发引起了很多关注，尤其是在在线凸优化的框架中。同时，稀疏的时变优化已成为处理广泛应用程序的强大工具，从动态压缩感应到简约的系统识别范围。在大多数有关稀疏时变问题的文献中，假定有关系统演化的一些先前信息可用。相比之下，在本文中，我们提出了一种在线学习方法，该方法不采用给定模型，适合对抗框架。具体来说，我们开发了集中和分布的算法，并从在线学习的角度以动态遗憾来分析它们。此外，我们提出的数值实验说明了它们的实际有效性。
The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious system identification. In most of the literature on sparse time-varying problems, some prior information on the system's evolution is assumed to be available. In contrast, in this paper, we propose an online learning approach, which does not employ a given model and is suitable for adversarial frameworks. Specifically, we develop centralized and distributed algorithms, and we theoretically analyze them in terms of dynamic regret, in an online learning perspective. Further, we propose numerical experiments that illustrate their practical effectiveness.
