This study concerns a generic model-free stochastic optimization problem requiring the minimization of a risk function defined on a given bounded domain in a Euclidean space. Smoothness assumptions ...

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 ...

In recent years, the paradigm of cloud computing has emerged as an architecture for computing that makes use of distributed (networked) computing resources. In this paper, we consider a distributed ...

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 ...

Researchers examined problems related to the timing and scheduling of surgeries and patients' stays in recovery units. In ...

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