The goal is to implement, analyze, and compare various stochastic control techniques—from classical methods to modern machine learning approaches—with applications to real-world problems in portfolio ...

Note that the higher the dimensionality (parameters number) of the optimization problem, the harder it is, and the slower the optimization process is. A family of problem-independent, gradient-free, ...

Abstract: The optimal conditions evaluation in complex stochastic systems modelled through Discrete Event Simulation is often extremely costly in computational terms. Especially when the number of ...

A global research team led by scientists from China’s Tianjin Renai College has developed a novel stochastic optimization technique for enhanced dispatching and operational efficiency in PV-powered ...

Abstract: Iterative learning control (ILC) applies to systems that repeat the same finite-duration task repeatedly, where each repetition is termed a trial and the finite duration is termed the trial ...