TY  - CONF
ID  - eprints3645
T2  - 2016 European Control Conference (ECC)
EP  - 159
M2  - Aalborg, Denmark
A1  - Themelis, A.
A1  - Villa, S.
A1  - Patrinos, Panagiotis
A1  - Bemporad, Alberto
SN  - 978-1-5090-2591-6
PB  - IEEE
N2  - 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.
SP  - 154
AV  - public
TI  - Stochastic gradient methods for stochastic model predictive control
Y1  - 2016/06//
KW  - Context;Convergence;Gradient methods;Indexes;Measurement;Predictive control;Stochastic processes
UR  - https://doi.org/10.1109/ECC.2016.7810279
ER  -