9 papers
Nonconvex optimization methods for ground states in disordered continuous-spin models
Ramgopal Agrawal, Lorenzo Ciarpaglini, Pierluigi Mansueto +4
This work explores the global optimization problem of finding lowest-energy configurations in disordered continuous-spin models from statistical physics, with a particular focus on…
A heavy-ball type curve search method for smooth convexly constrained optimization
Federica Donnini, Pierluigi Mansueto
This paper addresses smooth convexly constrained optimization problems where the Euclidean projection onto the feasible set is computationally tractable. Although momentum techniqu…
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…
A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
Pierluigi Mansueto
We introduce a nonmonotone extension of the Front Descent framework for multiobjective optimization. The method uses novel nonmonotone line searches that allow temporary increases…
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…
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,…