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 ...

Stochastic dominance provides a rigorous method to compare uncertain prospects without imposing restrictive assumptions on investor risk preferences, thus offering an alternative to traditional ...

Professor Ruszczynski’s interests are in the theory, numerical methods and applications of stochastic optimization. He is author of "Nonlinear Optimization", "Lectures on Stochastic programming", and ...