An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

Interior point methods for optimization have been around for more than 25 years now. Their
presence has shaken up the field of optimization. Interior point methods for linear and …

Interior-point methods (IPMs) are among the most efficient methods for solving linear, and
also wide classes of other convex optimization problems. Since the path-breaking work of …

This series of studies present the state‐of‐the‐art for the solution of the transient stability
constrained optimal power flow problem (TSC‐OPF). Three different classes of solution …

Recently, machine learning, particularly message-passing graph neural networks (MPNNs),
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …

In this paper we present a redesign of a linear algebra kernel of an interior point method to
avoid the explicit use of problem matrices. The only access to the original problem data …

The optimal solutions of the restricted master problems typically leads to an unstable
behavior of the standard column generation technique and, consequently, originates an …

We report a computational study of two-stage SP models on a large set of benchmark
problems and consider the following methods:(i) Solution of the deterministic equivalent …

Linear energy system models are a crucial component of energy system design and
operations, as well as energy policy consulting. If detailed enough, such models lead to …

In this paper we discuss the polynomiality of a feasible version of Mehrotra's predictor-
corrector algorithm whose variants have been widely used in several interior point method …