Time-Varying Optimization of Networked Systems with Human Preferences

TL;DR

This paper introduces a distributed online optimization algorithm for networked systems that learns individual preferences in real-time using user feedback, effectively managing time-varying constraints and costs.

Contribution

It develops a novel primal-dual online optimization method that incorporates shape-constrained Gaussian Process learning of preferences in a distributed setting.

Findings

Algorithm achieves low dynamic regret and constraint violation.

Effective in real-time distributed energy resource management.

Learns preferences accurately from irregular user feedback.

Abstract

This paper 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, the paper 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 shape-constrained Gaussian Process. The online algorithm is based on a primal-dual method, and acts effectively in a closed-loop fashion where: i) users' feedback is utilized to estimate the cost, and ii) 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.