Stochastic Optimization Methods

Focusing on optimization under uncertainty, this book explores how to derive robust solutions that remain effective despite variations in random model parameters. It discusses the transformation of stochastic optimization problems into deterministic equivalents, utilizing probability distributions and decision theory concepts. By addressing the challenges posed by randomness, it provides a framework for computing solutions that maintain their integrity in practical applications.