This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

The presence of uncertainty in a system description has always been a critical issue in
control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain …

In this paper, we study the link between a Chance-Constrained optimization Problem (CCP)
and its sample counterpart (SP). SP has a finite number, say N, of sampled constraints …

The scenario approach is a general methodology for data-driven optimization that has
attracted a great deal of attention in the past few years. It prescribes that one collects a …

This paper presents a survey of the most significant results on robust control theory. In
particular, we study the modeling of uncertain systems, robust stability analysis for systems …

We propose a new method for solving chance constrained optimization problems that lies
between robust optimization and scenario-based methods. Our method does not require …

We consider convex optimization problems with uncertain, probabilistically described,
constraints. In this context, scenario optimization is a well recognized methodology where a …

Random convex programs (RCPs) are convex optimization problems subject to a finite
number N of random constraints. The optimal objective value J^* of an RCP is thus a …

Random projection algorithm is of interest for constrained optimization when the constraint
set is not known in advance or the projection operation on the whole constraint set is …