Stochastic optimization and the simultaneous perturbation method
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Global random optimization by simultaneous perturbation stochastic approximation
Proceedings of the 33nd conference on Winter simulation
Geographical feature sensitive sensor placement
Journal of Parallel and Distributed Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Brief paper: An adaptive optimization scheme with satisfactory transient performance
Automatica (Journal of IFAC)
The mathematics of continuous-variable simulation optimization
Proceedings of the 40th Conference on Winter Simulation
Large scale nonlinear control system fine-tuning through learning
IEEE Transactions on Neural Networks
Averaging and derivative estimation within stochastic approximation algorithms
Proceedings of the Winter Simulation Conference
| Hi-index | 0.00 |
Weighted averages of Kiefer--Wolfowitz-type procedures, which are driven by larger step lengths than usual, can achieve the optimal rate of convergence. A priori knowledge of a lower bound on the smallest eigenvalue of the Hessian matrix is avoided. The asymptotic mean squared error of the weighted averaging algorithm is the same as would emerge using a Newton-type adaptive algorithm. Several different gradient estimates are considered; one of them leads to a vanishing asymptotic bias. This gradient estimate applied with the weighted averaging algorithm usually yields a better asymptotic mean squared error than applied with the standard algorithm.