Online Saddle Point Tracking with Decision-Dependent Data

Killian Wood; Emiliano Dall'Anese

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arXiv · 2212.02693

Online Saddle Point Tracking with Decision-Dependent Data

Killian Wood·Emiliano Dall'Anese

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Abstract

In this work, we consider a time-varying stochastic saddle point problem in which the objective is revealed sequentially, and the data distribution depends on the decision variables. Problems of this type express the distributional dependence via a distributional map, and are known to have two distinct types of solutions--saddle points and equilibrium points. We demonstrate that, under suitable conditions, online primal-dual type algorithms are capable of tracking equilibrium points. In contrast, since computing closed-form gradient of the objective requires knowledge of the distributional map, we offer an online stochastic primal-dual algorithm for tracking equilibrium trajectories. We provide bounds in expectation and in high probability, with the latter leveraging a sub-Weibull model for the gradient error. We illustrate our results on an electric vehicle charging problem where responsiveness to prices follows a location-scale family based distributional map.

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BibTeX RIS

Killian Wood, Emiliano Dall'Anese. 2022-12-06. Online Saddle Point Tracking with Decision-Dependent Data. https://arxiv.org/abs/2212.02693

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