Distributed Stochastic Nested Optimization via Cubic Regularization

TL;DR

This paper introduces a novel distributed stochastic nested optimization method using cubic regularization, with theoretical guarantees and practical demonstrations in electric vehicle data-driven multi-agent scenarios.

Contribution

It develops a local stopping criterion for the inner problem and proposes the DiSCRN algorithm with proven effectiveness for the outer problem.

Findings

The DiSCRN method converges faster than standard gradient and Newton methods.

The approach demonstrates improved stability in highly nonconvex scenarios.

The method successfully extends to EV charging with resistive losses and time-of-use pricing.

Abstract

This paper considers a nested stochastic distributed optimization problem. In it, approximate solutions to realizations of the inner-problem are leveraged to obtain a Distributed Stochastic Cubic Regularized Newton (DiSCRN) update to the decision variable of the outer problem. We provide an example involving electric vehicle users with various preferences which demonstrates that this model is appropriate and sufficiently complex for a variety of data-driven multi-agent settings, in contrast to non-nested models. The main two contributions of the paper are: (i) development of local stopping criterion for solving the inner optimization problem which guarantees sufficient accuracy for the outer-problem update, and (ii) development of the novel DiSCRN algorithm for solving the outer-problem and a theoretical justification of its efficacy. Simulations demonstrate that this approach is more stable and converges faster than standard gradient and Newton outer-problem updates in a highly nonconvex scenario, and we also demonstrate that the method extends to an EV charging scenario in which resistive battery losses and a time-of-use pricing model are considered over a time horizon.