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math.OC2025
On the Batch Size Selection in Stochastic Gradient Methods Using No-Replacement Sampling
Marco Boresta, Alberto De Santis, Stefano Lucidi
Recent stochastic gradient methods that have appeared in the literature base their efficiency and global convergence properties on a suitable control of the variance of the gradien…
math.OC2024
Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta +2
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classific…
math.OC2021
Mixed Finite Differences Scheme for Gradient Approximation
Marco Boresta, Tommaso Colombo, Alberto De Santis +1
In this paper we focus on the linear functionals defining an approximate version of the gradient of a function. These functionals are often used when dealing with optimization prob…