14 citations · 20 across the 11 of their papers we have counts for
11 papers
Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing
Francesca Lanzillotta, Chiara Albisani, Davide Pucci +3
In many learning tasks, certain requirements on the processing of individual data samples should arguably be formalized as strict constraints in the underlying optimization problem…
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,…
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…
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…
Optimization-Driven Design of Monolithic Soft-Rigid Grippers
Pierluigi Mansueto, Mihai Dragusanu, Anjum Saeed +3
Sim-to-real transfer remains a significant challenge in soft robotics due to the unpredictability introduced by common manufacturing processes such as 3D printing and molding. Thes…
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…
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…
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…
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…
Cardinality-Constrained Multi-Objective Optimization: Novel Optimality Conditions and Algorithms
Matteo Lapucci, Pierluigi Mansueto
In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous neces…
Improved Front Steepest Descent for Multi-objective Optimization
Matteo Lapucci, Pierluigi Mansueto
In this paper, we deal with the Front Steepest Descent algorithm for multi-objective optimization. We point out that the algorithm from the literature is often incapable, by design…