6 papers · 1 filter
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems
Matteo Lapucci, Davide Pucci
In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest li…
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…
Effective Front-Descent Algorithms with Convergence Guarantees
Matteo Lapucci, Pierluigi Mansueto, Davide Pucci
In this manuscript, we address continuous unconstrained multi-objective optimization problems and we discuss descent type methods for the reconstruction of the Pareto set. Specific…
On the Computation of the Efficient Frontier in Advanced Sparse Portfolio Optimization
Arturo Annunziata, Matteo Lapucci, Pieluigi Mansueto +1
In this work, we deal with the problem of computing a comprehensive front of efficient solutions in multi-objective portfolio optimization problems in presence of sparsity constrai…
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models
Matteo Lapucci, Davide Pucci
In this paper, we deal with algorithms to solve the finite-sum problems related to fitting over-parametrized models, that typically satisfy the interpolation condition. In particul…