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

We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...

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 ...

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 ...

A novel parallel decomposition algorithm is developed for large, multistage stochastic optimization problems. The method decomposes the problem into subproblems that correspond to scenarios. The ...

Scientists have developed a new optimization approach that combines both day-ahead optimization and real-time optimization to improve operations of PV-driven EV charging stations. The framework is ...

The stochastic indicator is similar to the parabolic SAR in that it's hard to calculate but easy to interpret. The theory behind the stochastic oscillator, a well-known momentum indicator is that ...

Learn how to implement SGD with momentum from scratch in Python—boost your optimization skills for deep learning.