TY - JOUR
T1 - A Distributed Optimal Control Problem with Averaged Stochastic Gradient Descent
AU - Sun , Qi
AU - Du , Qiang
JO - Communications in Computational Physics
VL - 3
SP - 753
EP - 774
PY - 2020
DA - 2020/02
SN - 27
DO - http://doi.org/10.4208/cicp.OA-2018-0295
UR - https://global-sci.org/intro/article_detail/cicp/13928.html
KW - PDE-constrained elliptic control, high-dimensional random inputs, Monte Carlo finite element, stochastic gradient descent.
AB - <p style="text-align: justify;">In this work, we study a distributed optimal control problem, in which the governing system is given by second-order elliptic equations with log-normal coefficients. To lessen the curse of dimensionality that originates from the representation of stochastic coefficients, the Monte Carlo finite element method is adopted for numerical discretization where a large number of sampled constraints are involved. For the solution of such a large-scale optimization problem, stochastic gradient descent method is widely used but has slow convergence asymptotically due to its inherent variance. To remedy this problem, we adopt an averaged stochastic gradient descent method which performs stably even with the use of relatively large step sizes and small batch sizes. Numerical experiments are carried out to validate our theoretical findings.</p>