A. Benfenati, F. Porta, T. A. Bubba, M. Viola (Editors), Advanced Techniques in Optimization for Machine Learning and Imaging, Springer INdAM Series, Volume 61
L. Antonelli, V. De Simone, M. Viola: A multiplicative components framework for joint correction and segmentation of magnetic resonance images, to appear on Applied Mathematics and Computation, 2025
R. Campagna, S. Crisci, G. Santin, G. Toraldo, M. Viola: An algorithm for a constrained P-spline, BIT. Numerical mathematics, Volume 65, article number 29, 2025 (arXiv)
G. Landi, M. Viola, F. Zama: A Scaled Gradient Projection method for the realization of the Balancing Principle in TGV-based image restoration, Computational Optimization and Applications, 91, 759–785, 2025
S. Crisci, S. Rebegoldi, G. Toraldo, M. Viola: Barzilai-Borwein-like rules in proximal gradient schemes for ℓ1−regularized problems, Optimization Methods and Software, 39(3), pp. 601–633, 2024 (Optimization Online)
D. di Serafino, W.W. Hager, G. Toraldo, M. Viola: On the stationarity for nonlinear optimization problems with polyhedral constraints, Mathematical Programming, 205, pp. 107–134, 2024 (Open access)
S. Crisci, V. De Simone, M. Viola: On the Adaptive Penalty Parameter Selection in ADMM, Algorithms, 16(6), p. 264, 2023 (Open access)
D. di Serafino, N. Krejic, N. Krklec Jerinkic, M. Viola: LSOS: Line-search Second-Order Stochastic optimization methods for nonconvex finite sums, Mathematics of Computation, 92, pp. 1273-1299, 2023 (Optimization Online, arXiv)
L. Antonelli, V. De Simone, M. Viola: Cartoon-texture evolution for two-region image segmentation, Computational Optimization and Applications, 84(1), pp. 5–26, 2023 (Open access)
V. De Simone, D. di Serafino, J. Gondzio, S. Pougkakiotis, M. Viola: Sparse Approximations with Interior Point Methods, SIAM Review, 64(4), pp. 954-988, 2022 (Optimization Online, arXiv)
D. di Serafino, G. Landi, M. Viola: Directional TGV-based image restoration under Poisson noise, Journal of Imaging, 7 (6), p. 99, 2021 (Open access, preprint available on arXiv)
D. di Serafino, G. Toraldo, M. Viola: Using gradient directions to get global convergence of Newton-type methods, Applied Mathematics and Computation, article 125612, 2020 (Optimization Online, arXiv)
V. De Simone, D. di Serafino, M. Viola: A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers, Electronic Transactions on Numerical Analysis, 53, pp.406-425, 2020 (Optimization Online, arXiv)
D. di Serafino, G. Landi, M. Viola: ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration, Applied Mathematics and Computation, 364, article 124678, 2020 (arXiv)
M. Viola, M. Sangiovanni, G. Toraldo, M.R. Guarracino: Semi-supervised Generalized Eigenvalues Classification, Annals of Operations Research, 276, pp. 249-266, 2019
D. di Serafino, G. Toraldo, M. Viola, J. Barlow: A two-phase gradient method for Quadratic Programming problems with a single linear constraint and bounds on the variables, SIAM Journal on Optimization, 28(4), pp. 2809—2838, 2018 (arXiv)
M. Viola, M. Sangiovanni, G. Toraldo, M.R. Guarracino: A Generalized Eigenvalues Classifier with Embedded Feature Selection, Optimization Letters, 11(2), pp. 299-311, 2017
E. Murphy, M. Viola, V. Krylov: A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments, Irish Machine Vision & Image Processing Conference 2025 -- Winner of the Jon Campbell Best Paper Award
G. Sanguin, A. Pakrashi, M. Viola, F. Rinaldi: Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks, Proceedings of The 32nd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2024), CEUR Workshop Proceedings, 2025
Á. Martínez Calomardo, M. Viola, M. Yousefi: Combined First- and Second-order directions for Deep Neural Networks Training, In: Sergeyev, Y.D., Kvasov, D.E., Astorino, A. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2023, Lecture Notes in Computer Science, vol 14476, 2025
F. Porta, S. Villa, M. Viola, M. Zach: On the inexact proximal Gauss-Newton methods for regularized nonlinear least squares problems, In: Benfenati, A., Porta, F., Bubba, T.A., Viola, M. (eds): Advanced Techniques in Optimization for Machine Learning and Imaging. ATOMI 2022, Springer INdAM Series, vol 61, pp 151–165, 2024
L. Antonelli, V. De Simone, M. Viola: Segmenting MR images through texture extraction and multiplicative components optimization, In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (Eds.): Scale Space and Variational Methods in Computer Vision. SSVM 2023, Lecture Notes in Computer Science, vol 14009, pp. 511-521, 2023
D. di Serafino, G. Landi, M. Viola: TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter, 21st International Conference on Computational Science and Its Applications (ICCSA), 2021, pp. 139-145 (available on arXiv)
D. di Serafino, G. Toraldo, M. Viola: A gradient-based globalization strategy for the Newton method, In: Y. D. Sergeyev and D. E. Kvasov (Eds.): Numerical Computations: Theory and Algorithms. NUMTA 2019, Lecture Notes in Computer Science, vol 11973, pp. 177–185, 2020.
Z. Dostál, G. Toraldo, M. Viola, O. Vlach: Proportionality-based gradient methods with applications in contact mechanics, In: Kozubek T. et al. (Eds.): High Performance Computing in Science and Engineering. HPCSE 2017. Lecture Notes in Computer Science, vol 11087, 2018
M.R. Guarracino, M. Sangiovanni, G. Severino, G. Toraldo, M. Viola: On the regularization of generalized eigenvalues classifiers, Proceedings of the 2nd International Conference "Numerical Computations: Theory and Algorithms", AIP Conference Proceedings, 2016
M. Liberini, S. Esposito, K. Reshad, B. Previtali, M. Viola, A. Squillace: Development of an innovative method to predict and to characterize the performances of Ti-6Al-4V LBW joints, Proceedings of the 19th International ESAFORM Conference on Material Forming, AIP Conference Proceedings, 2016