An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints
The primary goal of this paper is to provide an efficient solution algorithm based on the
augmented Lagrangian framework for optimization problems with a stochastic objective …
augmented Lagrangian framework for optimization problems with a stochastic objective …
Simulation model calibration with dynamic stratification and adaptive sampling
Calibrating simulation models that take large quantities of multi-dimensional data as input is
a hard simulation optimization problem. Existing adaptive sampling strategies offer a …
a hard simulation optimization problem. Existing adaptive sampling strategies offer a …
Robust Simulation Optimization with Stratification
Stratification has been widely used as a variance reduction technique when estimating a
simulation output, whereby the input variates are generated following a stratified sampling …
simulation output, whereby the input variates are generated following a stratified sampling …
Dynamic Stratification and Post-Stratified Adaptive Sampling for Simulation Optimization
P Jain, S Shashaani - 2023 Winter Simulation Conference …, 2023 - ieeexplore.ieee.org
Post-stratification is a variance reduction technique that groups samples in respective strata
only after collecting the samples randomly. We incorporate this technique within an adaptive …
only after collecting the samples randomly. We incorporate this technique within an adaptive …