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

Self-triggered control, a well-documented technique for reducing the communication
overhead while ensuring desired system performance, is gaining increasing popularity …

This letter investigates the problem of tracking solutions of stochastic optimization problems
with time-varying costs that depend on random variables with decision-dependent …

Feedback optimization is a control paradigm that enables physical systems to autonomously
reach efficient operating points. Its central idea is to interconnect optimization iterations in …

This paper focuses on stochastic saddle point problems with decision-dependent
distributions. These are problems whose objective is the expected value of a stochastic …

We propose an algorithm based on online convex optimization for controlling discrete-time
linear dynamical systems. The algorithm is data-driven, ie, does not require a model of the …

Feedback optimization enables autonomous optimality seeking of a dynamical system
through its closed-loop interconnection with iterative optimization algorithms. Among various …

In this work, we propose a control scheme for linear systems subject to pointwise in time
state and input constraints that aims to minimize time-varying and a priori unknown cost …

This paper proposes a data-driven framework to solve time-varying optimization problems
associated with unknown linear dynamical systems. Making online control decisions to …

The exact pole placement problem concerns computing a static feedback law for a linear
dynamical system that will assign its poles at a set of pre-specified locations. This is a classic …