Some remarks on the division of cognitive labor. - Marco Viola (Roars Trans., 2015)

Proportionality-Based Gradient Methods with Applications in Contact Mechanics. - Zdenek Dostál; Gerardo Toraldo; Marco Viola; Oldrich Vlach (HPCSE, 2017)

A generalized eigenvalues classifier with embedded feature selection. - Marco Viola; Mara Sangiovanni; Gerardo Toraldo; Mario R. Guarracino (Optim. Lett., 2017)

A Two-Phase Gradient Method for Quadratic Programming Problems with a Single Linear Constraint and Bounds on the Variables. - Daniela di Serafino; Gerardo Toraldo; Marco Viola; Jesse L. Barlow (SIAM J. Optim., 2018)

A Gradient-Based Globalization Strategy for the Newton Method. - Daniela di Serafino; Gerardo Toraldo; Marco Viola (NUMTA(1), 2019)

Semi-supervised generalized eigenvalues classification. - Marco Viola; Mara Sangiovanni; Gerardo Toraldo; Mario R. Guarracino (Ann. Oper. Res., 2019)

A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers. - Valentina De Simone; Daniela di Serafino; Marco Viola (CoRR, 2019)

ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration. - Daniela di Serafino; Germana Landi; Marco Viola (Appl. Math. Comput., 2020)

LSOS: Line-search Second-Order Stochastic optimization methods. - Daniela di Serafino; Natasa Krejic; Natasa Krklec Jerinkic; Marco Viola (CoRR, 2020)

TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter. - Daniela di Serafino; Germana Landi; Marco Viola (ICCSA (Workshops), 2021)

Using gradient directions to get global convergence of Newton-type methods. - Daniela di Serafino; Gerardo Toraldo; Marco Viola (Appl. Math. Comput., 2021)

Directional TGV-Based Image Restoration under Poisson Noise. - Daniela di Serafino; Germana Landi; Marco Viola (J. Imaging, 2021)

Sparse Approximations with Interior Point Methods. - Valentina De Simone; Daniela di Serafino; Jacek Gondzio; Spyridon Pougkakiotis; Marco Viola (SIAM Rev., 2022)

LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums. - Daniela di Serafino; Natasa Krejic; Natasa Krklec Jerinkic; Marco Viola (Math. Comput., 2022)

Combined First- and Second-Order Directions for Deep Neural Networks Training. - Ángeles Martínez 001; Marco Viola; Mahsa Yousefi (NUMTA (1), 2023)

Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization. - Laura Antonelli; Valentina De Simone; Marco Viola (SSVM, 2023)

Cartoon-texture evolution for two-region image segmentation. - Laura Antonelli; Valentina De Simone; Marco Viola (Comput. Optim. Appl., 2023)

On the Adaptive Penalty Parameter Selection in ADMM. - Serena Crisci; Valentina De Simone; Marco Viola (Algorithms, 2023)

Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks. - Gabriele Sanguin; Arjun Pakrashi; Marco Viola; Francesco Rinaldi (AICS, 2024)

Barzilai-Borwein-like rules in proximal gradient schemes for ℓ 1 -regularized problems. - Serena Crisci; Simone Rebegoldi; Gerardo Toraldo; Marco Viola (Optim. Methods Softw., 2024)

On the stationarity for nonlinear optimization problems with polyhedral constraints. - Daniela di Serafino; William W. Hager; Gerardo Toraldo; Marco Viola (Math. Program., 2024)

An algorithm for a constrained P-spline. - Rosanna Campagna; Serena Crisci; Gabriele Santin; Gerardo Toraldo; Marco Viola (CoRR, 2025)

A Scaled Gradient Projection method for the realization of the balancing principle in TGV-based image restoration. - Germana Landi; Marco Viola; Fabiana Zama (Comput. Optim. Appl., 2025)