7 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…
Projection-based curve pattern search for black-box optimization over smooth convex sets
Xiaoxi Jia, Matteo Lapucci, Pierluigi Mansueto
In this paper, we deal with the problem of optimizing a black-box smooth function over a full-dimensional smooth convex set. We study sets of feasible curves that allow to properly…
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…
Efficient globalization of heavy-ball type methods for unconstrained optimization based on curve searches
Federica Donnini, Matteo Lapucci, Pierluigi Mansueto
In this work, we deal with unconstrained nonlinear optimization problems. Specifically, we are interested in methods carrying out updates possibly along directions not of descent,…