Proximal limited-memory quasi-newton methods for scenario-based stochastic optimal control

@inproceedings{Sampathirao2017ProximalLQ,
  title={Proximal limited-memory quasi-newton methods for scenario-based stochastic optimal control},
  author={Ajay K. Sampathirao and Pantelis Sopasakis and Alberto Bemporad and Panagiotis Patrinos},
  year={2017}
}
Stochastic optimal control problems are typically of rather large scale involving millions of decision variables, but possess a certain structure which can be exploited by first-order methods such as forward-backward splitting and the alternating direction method of multipliers (ADMM). In this paper, we use the forward-backward envelope, a real-valued continuously differentiable penalty function, to recast the dual of the original nonsmooth problem as an unconstrained problem which we solve via… CONTINUE READING
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