5 citations · 11 across the 12 of their papers we have counts for
13 papers
A Spatial Benders Algorithm for Delay Constrained Routing
Antonio Frangioni, Laura Galli, Luca Mencarelli +1
We study the Delay Constrained Routing (DCR) problem arising in IP computer networks to support high bandwidth applications. The goal is to route IP packets, from source to destina…
Empirical Comparison of Unified Benders Cuts for Multi-Commodity Fixed-Charge Network Design
Eric Larsen, Jean-François Cordeau, Antonio Frangioni +1
Among the many types of acceleration techniques designed to improve the performance of Benders decomposition, unified cut generation schemes have recently attracted a keen interest…
Bundle Network: a Machine Learning-Based Bundle Method
Francesca Demelas, Joseph Le Roux, Antonio Frangioni +3
This paper presents Bundle Network, a learning-based algorithm inspired by the Bundle Method for convex non-smooth minimization problems. Unlike classical approaches that rely on h…
The Ellipsoidal Separation Machine
Antonio Frangioni, Enrico Gorgone, Benedetto Manca
We propose the -- to the best of our knowledge -- first fully functional implementation of the ``Separation by a Convex Body'' (SCB) approach first outlined in Grzybowski et al. [1…
The Differentiable Feasibility Pump
Matteo Cacciola, Alexandre Forel, Antonio Frangioni +1
Although nearly 20 years have passed since its conception, the feasibility pump algorithm remains a widely used heuristic to find feasible primal solutions to mixed-integer linear…
The Algorithm Configuration Problem
Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni +1
The field of algorithmic optimization has significantly advanced with the development of methods for the automatic configuration of algorithmic parameters. This article delves into…
Learning to Configure Mathematical Programming Solvers by Mathematical Programming
Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni +1
We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it.…
A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
Gabriele Iommazzo, Claudia D'Ambrosio, Antonio Frangioni +1
We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and conf…
Structured Pruning of Neural Networks for Constraints Learning
Matteo Cacciola, Antonio Frangioni, Andrea Lodi
In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, a…
On the Convergence of Stochastic Gradient Descent in Low-precision Number Formats
Matteo Cacciola, Antonio Frangioni, Masoud Asgharian +2
Deep learning models are dominating almost all artificial intelligence tasks such as vision, text, and speech processing. Stochastic Gradient Descent (SGD) is the main tool for tra…
Comparing perspective reformulations for piecewise-convex optimization
Renan Spencer Trindade, Claudia D'Ambrosio, Antonio Frangioni +1
Our study is motivated by the solution of Mixed-Integer Non-Linear Programming (MINLP) problems with separable non-convex functions via the Sequential Convex MINLP technique, an it…
Deep Neural Networks pruning via the Structured Perspective Regularization
Matteo Cacciola, Antonio Frangioni, Xinlin Li +1
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, an…
Bicriteria data compression
Andrea Farruggia, Paolo Ferragina, Antonio Frangioni +1
The advent of massive datasets (and the consequent design of high-performing distributed storage systems) have reignited the interest of the scientific and engineering community to…