174 citations · 193 across the 11 of their papers we have counts for
12 papers
Scalarization via utility functions in multi-objective optimization
Lorenzo Lampariello, Simone Sagratella, Valerio Giuseppe Sasso +1
We study a general scalarization approach via utility functions in multi-objective optimization. It consists of maximizing utility which is obtained from the objectives' bargaining…
Best-response algorithms for a class of monotone Nash equilibrium problems with mixed-integer variables
Filippo Fabiani, Simone Sagratella
We characterize the convergence properties of traditional best-response (BR) algorithms in computing solutions to mixed-integer Nash equilibrium problems (MI-NEPs) that turn into a…
Proximal-like algorithms for equilibrium seeking in mixed-integer Nash equilibrium problems
Filippo Fabiani, Barbara Franci, Simone Sagratella +2
We consider potential games with mixed-integer variables, for which we propose two distributed, proximal-like equilibrium seeking algorithms. Specifically, we focus on two scenario…
On the solution of monotone nested variational inequalities
Lorenzo Lampariello, Gianluca Priori, Simone Sagratella
We study nested variational inequalities, which are variational inequalities whose feasible set is the solution set of another variational inequality. We present a projected averag…
A gray-box approach for curriculum learning
Francesco Foglino, Matteo Leonetti, Simone Sagratella +1
Curriculum learning is often employed in deep reinforcement learning to let the agent progress more quickly towards better behaviors. Numerical methods for curriculum learning in t…
A dominance maximization approach to portfolio selection
Francesco Cesarone, Lorenzo Lampariello, Simone Sagratella
In the portfolio multiobjective optimization framework, we propose to compare and choose, among all feasible asset portfolios of a given market, the one that maximizes the product…
Computing all solutions of Nash equilibrium problems with discrete strategy sets
Simone Sagratella
The Nash equilibrium problem is a widely used tool to model non-cooperative games. Many solution methods have been proposed in the literature to compute solutions of Nash equilibri…
A Bridge between Bilevel Programs and Nash Games
Lorenzo Lampariello, Simone Sagratella
We study connections between optimistic bilevel programming problems and Generalized Nash Equilibrium Problems (GNEP)s. We remark that, when addressing bilevel problems, we conside…
A Class of Parallel Decomposition Algorithms for SVMs Training
Andrea Manno, Laura Palagi, Simone Sagratella
The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms g…
Canonical Dual Approach for Contact Mechanics Problems with Friction
Vittorio Latorre, Simone Sagratella, David Yang Gao
This paper presents an application of Canonical duality theory to the solution of contact problems with Coulomb friction. The contact problem is formulated as a quasi-variational i…
Parallel Selective Algorithms for Big Data Optimization
Francisco Facchinei, Gesualdo Scutari, Simone Sagratella
We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The l…
Flexible Parallel Algorithms for Big Data Optimization
Francisco Facchinei, Simone Sagratella, Gesualdo Scutari
We propose a decomposition framework for the parallel optimization of the sum of a differentiable function and a (block) separable nonsmooth, convex one. The latter term is typical…