Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

Distributed energy resources (DERs)-which can include solar photovoltaic (PV), fuel cells,
microturbines, gensets, distributed energy storage (eg, batteries and ice storage), and new …

This article investigates the problem of regulating, at every time, a linear dynamical system
to the solution trajectory of a time-varying constrained convex optimization problem. The …

Autonomous optimization refers to the design of feedback controllers that steer a physical
system to a steady state that solves a predefined, possibly constrained, optimization …

There is a growing cross-disciplinary effort in the broad domain of optimization and learning
with streams of data, applied to settings where traditional batch optimization techniques …

In this study, a framework for processing gradient algorithms is proposed in accordance with
nabla fractional-order system theory. Unlike most of the literature, the gradient algorithm is …

In laser powder bed fusion (LPBF), part quality and process conditions are highly dependent
on the interlayer temperature (ILT) of the exposure surface of printed parts. State-of-the-art …

This paper studies regularity properties of optimization-based controllers, which are
obtained by solving optimization problems where the parameter is the system state and the …