Competitive Control via Online Optimization
Abstract
Online optimization is a powerful framework in machine learning that has seen numerous applications to problems in distributed systems, robotics, autonomous planning, and sustainability. In my group at Caltech, we began by applying online optimization to ‘right-size’ capacity in data centers a decade ago; and now we have used tools from online optimization to develop algorithms for demand response, energy storage management, video streaming, drone navigation, autonomous driving, and beyond. In this talk, I will highlight both the applications of online optimization and the theoretical progress that has been driven by these applications. The community has moved from designing algorithms for one-dimensional problems with restrictive assumptions on costs to general results for high-dimensional non-convex problems that highlight the role of constraints, predictions, delay, and more. In the last two years, a connection between online optimization and adversarial control has emerged, and I will highlight how advances in online optimization can lead to advances in the control of linear dynamical systems.
Here are two recent, relevant papers, though the speaker will also talk about new results beyond these:
-- https://arxiv.org/pdf/2002.05318.pdf (To appear in NeurIPS 2020)
-- https://arxiv.org/pdf/1911.03827.pdf (Appeared in Sigmetrics 2020)
