Güzin Bayraksan

Identifying Effective Scenarios in Distributionally Robust Stochastic Programs with Total Variation Distance

Categories Stochastic Programming

Traditional stochastic programs assume that the probability distribution of uncertainty is known. However, in practice, the probability distribution oftentimes is not known or cannot be accurately approximated. One way to address such distributional ambiguity is to work with distributionally robust convex stochastic programs (DRSPs), which minimize the worst-case expected cost with respect to a set … Read more

Monte Carlo Sampling-Based Methods for Stochastic Optimization

Categories Optimization of Simulated Systems, Stochastic Programming

This paper surveys the use of Monte Carlo sampling-based methods for stochastic optimization problems. Such methods are required when—as it often happens in practice—the model involves quantities such as expectations and probabilities that cannot be evaluated exactly. While estimation procedures via sampling are well studied in statistics, the use of such methods in an optimization … Read more