Time-Varying Optimization of Networked Systems With Human Preferences
Time-Varying Optimization of Networked Systems With Human Preferences
复制标题
DOI：
10.1109/tcns.2022.3203467
复制
发表时间：
2021-03
影响因子：
4.2
通讯作者：
Ana M. Ospina;Andrea Simonetto;E. Dall’Anese
中科院分区：
文献类型：
--
作者：
Ana M. Ospina;Andrea Simonetto;E. Dall’Anese
本文考虑与系统网络相关的时变优化问题，每个系统都由多个个体共享（并影响）。目标是最小化与个人偏好相关的成本函数，这些偏好是未知的，受到捕获网络物理或操作限制的时变约束。为此，本文开发了一种具有并行学习成本函数的分布式在线优化算法。成本函数是通过利用形状约束高斯过程中的工具根据用户的反馈（以不规则的间隔提供）动态学习的。在线算法基于原对偶方法，并以闭环方式有效地起作用，其中：首先，利用用户的反馈来估计成本，其次，在算法步骤中利用网络的测量来绕过感知网络（未知）外源输入的需要。从动态网络遗憾和约束违反方面分析了算法的性能。在分布式能源实时优化的背景下给出了数值例子。
This article considers a time-varying optimization problem associated with a network of systems, with each of the systems shared by (and affecting) a number of individuals. The objective is to minimize cost functions associated with the individuals' preferences, which are unknown, subject to time-varying constraints that capture physical or operational limits of the network. To this end, this article develops a distributed online optimization algorithm with concurrent learning of the cost functions. The cost functions are learned on-the-fly based on the users' feedback (provided at irregular intervals) by leveraging tools from the shape-constrained Gaussian process. The online algorithm is based on a primal–dual method and acts effectively in a closed-loop fashion where: First, users' feedback is utilized to estimate the cost, and second, measurements from the network are utilized in the algorithmic steps to bypass the need for sensing of (unknown) exogenous inputs of the network. The performance of the algorithm is analyzed in terms of dynamic network regret and constraint violation. Numerical examples are presented in the context of real-time optimization of distributed energy resources.