中文标题#
基於控制的在線分佈式優化
英文标题#
Control-Based Online Distributed Optimization
中文摘要#
在本文中，我們設計了一類新穎的在線分佈式優化算法，利用控制理論技術。我們首先關注二次成本，並假設知道其變化的內部模型。在此設置下，我們將算法設計表述為一個魯棒控制問題，表明它會產生一個完全分佈式的算法。我們還提供了一個獲取內部模型的分佈式例程。我們證明該算法可以精確收斂到最優解的序列。我們對不同參數選擇下的算法性能進行了經驗評估。此外，我們還對具有不精確內部模型的二次問題和非二次問題的算法性能進行了評估，並表明在兩種情況下它都優於其他算法。
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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