7 citations · 10 across the 6 of their papers we have counts for
4 papers · 1 filter
Fully Convergent Projection-based Methods with Momentum under Nonconvex Geometric Constraints
Matteo Lapucci, Diego Scuppa
Nonlinear optimization problems with complicated, nonconvex, yet geometrically structured constraints can be tackled by projected-gradient methods: under weak regularity assumption…
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…
Penalty decomposition derivative free method for the minimization of partially separable functions over a convex feasible set
Francesco Cecere, Matteo Lapucci, Davide Pucci +1
In this paper, we consider the problem of minimizing a smooth function, given as finite sum of black-box functions, over a convex set. In order to advantageously exploit the struct…
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…