Dharmashankar Subramanian, Segev Wasserkrug, et al.
INFORMS 2021
We propose a deep neural network-based optimization for minimizing expensive black-box functions with mixed categorical-continuous inputs and linear constraints. We use a ReLU deep neural network to get a surrogate model from the historical data. To overcome the non-smoothness and bad local minimum of the training problem, a smoothed DNN optimized by a second-order optimization method is utilized. A new sample is obtained by solving a linearized version of the DNN surrogate model.
Dharmashankar Subramanian, Segev Wasserkrug, et al.
INFORMS 2021
Yi Zhou, Guanghui Lan, et al.
INFORMS 2021
Fernando Marianno, Wang Zhou, et al.
INFORMS 2021
Ruijiang Gao, Max Biggs, et al.
INFORMS 2021