7 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
Loss-Optimal Classification Trees: A Generalized Framework and the Logistic Case
Tommaso Aldinucci, Matteo Lapucci
The Classification Tree (CT) is one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, tha…
A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization
Enrico Civitelli, Alessio Sortino, Matteo Lapucci +2
Batch Normalization is an essential component of all state-of-the-art neural networks architectures. However, since it introduces many practical issues, much recent research has be…