6 papers · 1 filter
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…
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…
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…
On the Batch Size Selection in Stochastic Gradient Methods Using No-Replacement Sampling
Marco Boresta, Alberto De Santis, Stefano Lucidi
Recent stochastic gradient methods that have appeared in the literature base their efficiency and global convergence properties on a suitable control of the variance of the gradien…
Worst-case complexity analysis of derivative-free methods for multi-objective optimization
Giampaolo Liuzzi, Stefano Lucidi
In this work, we are concerned with the worst case complexity analysis of "a posteriori" methods for unconstrained multi-objective optimization problems where objective function va…
Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization
Matteo Lapucci, Giampaolo Liuzzi, Stefano Lucidi +1
In this paper we consider bound-constrained mixed-integer optimization problems where the objective function is differentiable w.r.t.\ the continuous variables for every configurat…