Black-box stochastic optimization involves sampling in both the solution and data spaces. Traditional variance reduction methods mainly designed for reducing the data sampling noise may suffer from ...

Adaptive control has been widely and successfully employed to realize complete consensus in integer-order homogeneous multi-agent systems. Nevertheless, its extension to fractional-order heterogeneous ...

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 ...

Stochastic gradient descent and Adam are optimization algorithms that update model parameters from estimated gradients, but ...

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 ...