collaborators

9 papers

math.OC2026

Projected Gradient Methods with Momentum

Matteo Lapucci, Giampaolo Liuzzi, Stefano Lucidi +2

We focus on the optimization problem with smooth, possibly nonconvex objectives and a convex constraint set for which the Euclidean projection operation is practically available. F…

math.OC2026

Derivative-Free Bilevel Optimization with Inexact Lower-Level Solutions

Edoardo Cesaroni, Giampaolo Liuzzi, Stefano Lucidi

In this work, we propose derivative-free framework for bilevel optimization. We consider both the upper and lower-level problems with bound constraints on the variables, as well as…

math.OC2026

Nonlinear Derivative-free Constrained Optimization with a Penalty-Interior Point Method and Direct Search

Andrea Brilli, Ana L. Custódio, Giampaolo Liuzzi +1

In this work, the joint use of a mixed penalty-interior point method and direct search is proposed, to address {nonlinear} constrained derivative-free optimization problems. A meri…

math.OC2025

A Globally Convergent Gradient Method with Momentum

Matteo Lapucci, Giampaolo Liuzzi, Stefano Lucidi +2

In this work, we consider smooth unconstrained optimization problems and we deal with the class of gradient methods with momentum, i.e., descent algorithms where the search directi…

math.OC2025

Nonlinear Derivative-free Constrained Optimization with a Penalty-Interior Point Method and Direct Search

Andrea Brilli, Ana L. Custódio, Giampaolo Liuzzi +1

In this work, we propose the joint use of a mixed penalty-interior point method and direct search, for addressing nonlinearly constrained derivative-free optimization problems. A m…

math.OC2025

A linesearch-based derivative-free method for noisy black-box problems

Alberto De Santis, Giampaolo Liuzzi, Stefano Lucidi

In this work we consider unconstrained optimization problems. The objective function is known through a zeroth order stochastic oracle that gives an estimate of the true objective…