Robust stochastic optimisation methods seek decision rules that perform reliably under both inherent randomness and ambiguity in probability models. Combining classical stochastic programming—where ...

Course in stochastic optimization with an emphasis on formulating, solving, and approximating optimization models under uncertainty. Topics include: Models and applications: extensions of the linear ...

Your institution does not have access to this book on JSTOR. Try searching on JSTOR for other items related to this book. Gradient-Based Methods for Deterministic Continuous Optimization Chapter One ...