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arXiv · 2002.05318

Online Optimization with Memory and Competitive Control

Guanya Shi·Yiheng Lin·Soon-Jo Chung·Yisong Yue·Adam Wierman
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Abstract

This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous $p$ decisions. This setting generalizes Smoothed Online Convex Optimization. The proposed approach, Optimistic Regularized Online Balanced Descent, achieves a constant, dimension-free competitive ratio. Further, we show a connection between online optimization with memory and online control with adversarial disturbances. This connection, in turn, leads to a new constant-competitive policy for a rich class of online control problems.

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Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman. 2020-02-13. Online Optimization with Memory and Competitive Control. https://arxiv.org/abs/2002.05318

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