TY  - RPRT
VL  - arXiv:1604.01074
EP  - 11
ID  - eprints3544
N2  - Despite the proven advantages of scenario-based stochastic model predictive control for the operational control of water networks, its applicability is limited by its considerable computational footprint. In this paper we fully exploit the structure of these problems and solve them using a proximal gradient algorithm parallelizing the involved operations. The proposed methodology is applied and validated on a case study: the water network of the city of Barcelona.
M1  - working_paper
PB  - arXiv
A1  - Sampathirao, Ajay Kumar
A1  - Sopasakis, Pantelis
A1  - Bemporad, Alberto
A1  - Patrinos, Panagiotis
UR  - https://arxiv.org/abs/1604.01074
Y1  - 2016///
TI  - GPU-accelerated stochastic predictive control of drinking water networks
AV  - public
ER  -