Stochastic Optimization Methods

Focusing on optimization under uncertainty, this book explores how to compute robust optimal solutions that remain stable despite variations in random model parameters. It emphasizes the need for deterministic substitute problems to address these stochastic issues effectively. Utilizing probability distributions and decision theory concepts, the text guides readers through transforming complex optimization problems influenced by randomness into manageable deterministic forms.