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arXiv · 2107.01745

Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems

Ajay K. Sampathirao·Panagiotis Patrinos·Alberto Bemporad·Pantelis Sopasakis
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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.

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Ajay K. Sampathirao, Panagiotis Patrinos, Alberto Bemporad, Pantelis Sopasakis. 2021-07-04. Massively parallelizable proximal algorithms for large-scale stochastic optimal control problems. https://arxiv.org/abs/2107.01745

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