This dissertation investigates a number of models and algorithms in the context of optimization under uncertainty. The focus is on risk-averse optimization, where risk aversion is modeled via stochastic dominance, utility theory, and distributionally robust optimization. We first investigate an investment model with fixed contribution rates, optimize asset-allocation decisions in...
In this thesis we discuss the issue of solving stochastic optimization problems using sampling methods. Numerical results have shown that using variance reduction techniques from statistics can result in significant improvements over Monte Carlo sampling in terms of the number of samples needed for convergence of the optimal objective value...