中文标题#
基于控制的在线分布式优化
英文标题#
Control-Based Online Distributed Optimization
中文摘要#
在本文中，我们设计了一类新颖的在线分布式优化算法，利用控制理论技术。我们首先关注二次成本，并假设知道其变化的内部模型。在此设置下，我们将算法设计表述为一个鲁棒控制问题，表明它会产生一个完全分布式的算法。我们还提供了一个获取内部模型的分布式例程。我们证明该算法可以精确收敛到最优解的序列。我们对不同参数选择下的算法性能进行了经验评估。此外，我们还对具有不精确内部模型的二次问题和非二次问题的算法性能进行了评估，并表明在两种情况下它都优于其他算法。
英文摘要#
In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming to know an internal model of their variation. In this set-up, we formulate the algorithm design as a robust control problem, showing that it yields a fully distributed algorithm. We also provide a distributed routine to acquire the internal model. We show that the algorithm converges exactly to the sequence of optimal solutions. We empirically evaluate the performance of the algorithm for different choices of parameters. Additionally, we evaluate the performance of the algorithm for quadratic problems with inexact internal model and non-quadratic problems, and show that it outperforms alternative algorithms in both scenarios.
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