Distributed solution of stochastic optimal control problems on GPUs

Sampathirao, Ajay Kumar; Sopasakis, Pantelis; Bemporad, Alberto; Patrinos, Panagiotis

doi:10.1109/cdc.2015.7403352

Cited by 14 publications

(17 citation statements)

References 25 publications

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“…In particular, such a parallelization -assuming that full parallelization is supported by the hardware -equalizes the complexity of the scenario-based Riccati recursion to that of a deterministic one. A detailed exposition of the details of this procedure is available in [22,9].…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we present a software for the fast and efficient solution of such problems harnessing the immense computational capabilities of graphics processing units (GPU) building up on our previous work [9,21,22].…”

Section: State Of the Artmentioning

confidence: 99%

“…The scenario-based optimization problem (19) is now in the form (22) with f and g given by (24) and (28) respectively and H given by (27).…”

Section: Problem Reformulationmentioning

confidence: 99%

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Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

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We present an open-source solution for the operational control of drinking water distribution networks which accounts for the inherent uncertainty in water demand and electricity prices in the day-ahead market of a volatile deregulated economy. As increasingly more energy markets adopt this trading scheme, the operation of drinking water networks requires uncertainty-aware control approaches that mitigate the effect of volatility and result in an economic and safe operation of the network that meets the consumers' need for uninterrupted water supply. We propose the use of scenario-based stochastic model predictive control: an advanced control methodology which comes at a considerable computation cost which is overcome by harnessing the parallelization capabilities of graphics processing units (GPUs) and using a massively parallelizable algorithm based on the accelerated proximal gradient method.

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Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Uncertainty-aware demand management of water distribution networks in deregulated energy markets

Sopasakis¹,

Sampathirao²,

Bemporad³

et al. 2018

Environmental Modelling & Software

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show abstract

“…Because of the exponential growth of scenario trees with respect to time horizon and probability distribution, solving SMPC problems soon becomes very challenging in terms of computational requirements. One solution is developing algorithms amenable to parallelization (see [8] and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Stochastic gradient methods for stochastic model predictive control

Themelis

Villa

Patrinos

et al. 2016

2016 European Control Conference (ECC)

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Abstract-We introduce a new stochastic gradient algorithm, SAAGA, and investigate its employment for solving Stochastic MPC problems and multi-stage stochastic optimization programs in general. The method is particularly attractive for scenario-based formulations that involve a large number of scenarios, for which "batch" formulations may become inefficient due to high computational costs. Benefits of the method include cheap computations per iteration and fast convergence due to the sparsity of the proposed problem decomposition.

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“…Recent research focuses on very large problems, where distributed IP methods of different flavors have been developed (Blomvall and Lindberg, 2002;Pakazad et al, 2016;Hübner et al, 2016). The authors in (Sampathirao et al, 2015) implemented a dual accelerated proximal gradient method on a GPU to solve tree-structured convex problems in a massively parallelizable fashion.…”

Section: Introductionmentioning

confidence: 99%