7 papers
Asymptotic Analysis of Empirical Dynamic Programming in Infinite-Horizon Stochastic Optimal Control
Xin Chen, Elif Sena Isik, Johannes Milz
We derive statistical limit theorems for sample-based approximations of infinite-horizon discounted stochastic optimal control problems in discrete time. Our first result is a func…
Statistical Inference for Scenario-Based Dynamic Optimization under Uncertainty
Aurya Javeed, Johannes Milz
Motivated by batch and semi-batch process operation, we study finite-horizon open-loop dynamic optimization problems with uncertain parameters. A common computational approach repl…
Stochastic Optimal Control with Side Information and Bayesian Learning
Johannes Milz, Alexander Shapiro, Enlu Zhou
We study infinite-horizon stochastic optimal control problems with observable side information: a Markov chain that modulates an unknown context-conditional randomness distribution…
Nonparametric Robust Comparison of Solutions under Input Uncertainty
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song
We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIO…
Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
Johannes Milz, Alexander Shapiro
We establish central limit theorems for the Sample Average Approximation (SAA) method in discrete-time, finite-horizon stochastic optimal control. Our analysis is based on an abstr…
Randomized quasi-Monte Carlo methods for risk-averse stochastic optimization
Olena Melnikov, Johannes Milz
We establish epigraphical and uniform laws of large numbers for sample-based approximations of law invariant risk functionals. These sample-based approximation schemes include Mont…