5 citations · 11 across the 6 of their papers we have counts for
6 papers
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…
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…