Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems
Abstract
Scenario-based stochastic optimal control problems suffer from the curse of dimensionality as they can easily grow to six and seven figure sizes. First-order methods are suitable as they can deal with such large-scale problems, but may fail to achieve accurate solutions within a reasonable number of iterations. To achieve solutions of higher accuracy and high speed, in this paper we propose two proximal quasi-Newtonian limited-memory algorithms - MinFBE applied to the dual problem and the Newton-type alternating minimization algorithm (NAMA) - which can be massively parallelized on lockstep hardware such as graphics processing units (GPUs). We demonstrate the performance of these methods, in terms of convergence speed and parallelizability, on large-scale problems involving millions of variables.
Cite
@article{arxiv.2107.01745,
title = {Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems},
author = {Ajay K. Sampathirao and Panagiotis Patrinos and Alberto Bemporad and Pantelis Sopasakis},
journal= {arXiv preprint arXiv:2107.01745},
year = {2021}
}