Embedded Files
. M. Bertero, P. Boccacci, V. Ruggiero, Inverse Imaging with Poisson Data - From cells to galaxies, IOP Publishing, Series 2053-2563, (2018), https://doi.org/10.1088/2053-2563/aae109
. N. Krklec Jerinkic, F. Porta, V. Ruggiero, I. Trombini, Variable metric proximal stochastic gradient methods with additional sampling, Computational Optimization and Applications, https://doi.org/10.1007/s10589-025-00720-w, doi: 10.1007/s10589-025-00720-w (2025)
. N. Krklec Jerinkic, V. Ruggiero, I. Trombini, Spectral Stochastic Gradient Method with Additional Sampling for Finite and Infinite Sums, Computational Optimization and Applications, 91, 717-758 (2025), https://doi.org/10.1007/s10589-025-00664-1
. G. Franchini, F. Porta, V. Ruggiero, I. Trombini, L. Zanni, A stochastic gradient method with variance control and variable learning rate for Deep Learning, Journal of Computational and Applied Mathematics, https://doi.org/10.1016/j.cam.2024.116083, Volume 451, 1 December 2024, 116083 (2024).
. A. Benfenati, A. Catozzi, V. Ruggiero, Neural blind deconvolution with Poisson data , Inverse Problems, 39, 054003 (2023),10.1088/1361-6420/acc2e0
. G. Franchini, F. Porta, V. Ruggiero, I. Trombini, L. Zanni, Learning rate selection in stochastic gradient methods based on line search strategies, Applied Mathematics in Science and Engineering , 31,1, 2164000, (2023), https://doi.org/10.1080/27690911.2022.2164000.
. G. Franchini, F. Porta, V. Ruggiero, I. Trombini, Correction to: A Line Search Based Proximal Stochastic Gradient Algorithm with Dynamical Variance Reduction, Journal of Scientific Computing, 94-1,(23), 2023. (23), 10.1007/s10915-022-02084-3)
. G. Franchini, F. Porta, V. Ruggiero, I. Trombini, A line search based proximal stochastic gradient algorithm with dynamical variance reduction, Journal of Scientific Computing, 94-1, article 23 (2023).
. S. Crisci, F. Porta, V. Ruggiero, L. Zanni, Hybrid limited memory gradient projection methods for box-constrained optimization problems, Computational Optimization and Applications (2022), DOI : 10.1007/s10589-022-00409-4
. S. Crisci, F. Porta, V. Ruggiero, L. Zanni, On the convergence properties of scaled gradient projection methods with non-monotone Armijo–like line searches, Annali dell'Università di Ferrara, 58, 521-554 (2022), http://doi.org/10.1007/s11565-022-00437-2
. G. Franchini, V. Ruggiero, F. Porta, L. Zanni, Neural Architecture Search via standard Machine Learning methods, Mathematics in Engineering, V. 5, 1 (2023) 1-21, https://doi.org/10.3934/mine.2023012
. S. Crisci, M. Piana, V. Ruggiero, S. Scussolini, A regularized affine-scaling Trust Region method for parametric imaging of dynamic PET data, SIAM Journal on Imaging Sciences, 14 (1) (2021), 418–439, https://doi.org/10.1137/20M1336370
. S. Bonettini, F. Porta, V. Ruggiero, L. Zanni, Variable metric techniques for forward-backward methods in imaging, Journal of Computational and Applied Mathematics, 385 (2021) 113192, https://doi.org/10.1016/j.cam.2020.113192
. G. Franchini, V. Ruggiero, L. Zanni, Ritz-like values in steplength selections for stochastic gradient methods, Soft Computing, Vol. 24, 23 (2020) 17573-17588, https://doi.org/10.1007/s00500-020-05219-6
. S. Crisci, F. Porta, V. Ruggiero, L. Zanni, Spectral properties of Barzilai-Borwein rules in solving singly linearly constrained problems subject to lower and upper bounds, SIAM Journal on Optimization, Vol. 30, No. 2 : pp. 1300-1326 (2020) https://doi.org/10.1137/19M1268641
. S. Crisci, V. Ruggiero, L. Zanni, Steplength selection in gradient projection methods for box-constrained quadratic programs, Applied Mathematics and Computation, 356 (2019) 312-32, https://doi.org/10.1016/j.amc.2019.03.039.
. S. Bonettini, S. Rebegoldi, V. Ruggiero, Inertial variable metric techniques for the inexact forward--backward algorithm, SIAM Journal on Scientific Computing, 40:5 (2018), A3180–A3210, https://doi.org/10.1137/17M116001X
. B. Benciolini, V. Ruggiero, A. Vitti, M. Zanetti, Roof planes detection via a second-order variational model, ISPRS Journal of Photogrammetry and Remote Sensing, 138 (2018) 101–120, https://doi.org/10.1016/j.isprsjprs.2018.01.022
. D. Di Serafino, V. Ruggiero, G. Toraldo, L. Zanni, On the steplength selection in gradient methods for unconstrained optimization, Applied Mathematics and Computation, 318 (2018), 176-195, http://dx.doi.org/10.1016/j.amc.2017.07.037
. R. Zanella, F. Porta, V. Ruggiero, M. Zanetti, Serial and parallel approaches for image segmentation by numerical minimization of a second-order functional Applied Mathematics and Computation, 318 (2018),153-175, https://doi.org/10.1016/j.amc.2017.07.021.
. F. Porta, A. Cornelio, V. Ruggiero, Runge-Kutta-like scaling techniques for first-order methods in convex optimization, Applied Numerical Mathematics, 116 (2017), 256–272, http://dx.doi.org/10.1016/j.apnum.2016.08.011
. S. Bonettini, A. Benfenati, V. Ruggiero, Scaling techniques for epsilon-subgradient methods, SIAM Journal on Optimization, 26:3 (2016), 1741-1772, DOI: 10.1137/14097642X.
. S. Bonettini, F. Porta, V. Ruggiero, A variable metric forward-backward method with extrapolation, SIAM Journal on Scientific Computing, 38, 4, (2016), A2558–A2584, DOI: 10.1137/15M1025098
· M. Zanetti, V. Ruggiero, M. Miranda, Numerical minimization of a second-order functional for image segmentation, Communications in Nonlinear Science and Numerical Simulation, 36 (2016), 528-548, doi:10.1016/j.cnsns.2015.12.018
· L. Zanni, A. Benfenati, M. Bertero, V. Ruggiero, Numerical methods for parameter estimation in Poisson data inversion, Journal of Mathematical Imaging and Vision, 52:3 (2015), 397-413,DOI: 10.1007/s10851-014-0553-9.
· A. Benfenati, V. Ruggiero, Inexact Bregman iteration for deconvolution of superimposed extended and point sources, Communications in Nonlinear Science and Numerical Simulation, 20 (2015), 882-896, DOI: 10.1016/j.cnsns.2014.06.045
· S. Bonettini, V. Ruggiero, An Alternating Extragradient Method with Non Euclidean Projections for Saddle Point Problems, Computational Optimization and Applications, 59(3) (2014), 511-540, doi: 10.1007/s10589-014-9650-3
· A. Benfenati, V. Ruggiero, Inexact Bregman iteration with an application to Poisson data reconstruction, Inverse Problems, 29,6, 06516 (2013), doi:10.1088/0266-5611/29/6/065016
· S. Bonettini, V. Ruggiero, On the convergence of primal-dual hybrid gradient algorithms for total variation image restoration, Journal of Mathematical Imaging and Vision (2012), 44(3), 236-253 (2012),doi: 10.1007/s10851-011-0324-9.
· S. Bonettini, V. Ruggiero, An alternating extragradient method for total variation based image restoration from Poisson data, Inverse Problems, 27, 095001 (2011),doi: 10.1088/0266-5611/27/9/095001.
· S. Bonettini, V. Ruggiero, Analysis of Interior Point methods for edge-preserving removal of Poisson noise, “Recent advances in nonlinear optimization and equilibrium problems: a tribute to Marco D'Apuzzo”, (V. De Simone, D. di Serafino and G. Toraldo eds.), Quaderni di Matematica, vol. 27, Dipartimento di Matematica, Seconda Università degli Studi di Napoli, Aracne, ISBN 978-88-548-5687-5, 67-91, (2012).
· V. Ruggiero, T. Serafini, R. Zanella, L. Zanni, Iterative regularization algorithms for constrained image deblurring on graphics processors, Journal of Global Optimization, 48, 145-157 (2010), doi: 10.1007/s10898-009-9516-x.
· S. Bonettini, V. Ruggiero, F. Tinti, On the solution of indefinite systems arising in nonlinear programming problems, Numerical Linear Algebra with Applications, 14, 10, 807-831 (2007).
· S. Bonettini, E. Galligani, V. Ruggiero, Inner solvers for interior point methods for large scale nonlinear programming, Computational Optimization and Applications, 37, 1-34 (2007), doi:10.1007/s10589-007-9012-5
· S. Bonettini, V. Ruggiero, Some iterative methods for the solution of a symmetric indefinite KKT system, Computational Optimization and Applications, 38, 3-25 (2007).
· V. Ruggiero, F. Tinti, A preconditioner for solving large-scale variational inequality problems by a semismooth inexact approach, International Journal of Computer Mathematics, Vol. 83, N. 10, 723-739 (2006).
· S. Bonettini, E. Galligani, V. Ruggiero, An Inexact Newton Method Combined with Hestenes Multipliers' Scheme for the solution of the Karush-Kuhn-Tucker systems, Applied Mathematics and Computation, 168, 651-676 (2005).
· S. Bonettini, E. Galligani, V. Ruggiero, Hestenes Method for Symmetric Indefinite Systems in Interior-Point Method, Rendiconti di Matematica, Serie VII, Vol. 24, 185-199, Roma (2004).
· C. Durazzi, V. Ruggiero, Global Convergence of the Newton Interior-Point Method for Nonlinear Programming, Journal of Optimization Theory and Applications, 120, 1, 199-208 (2004).
· C. Durazzi, V. Ruggiero, A Newton Inexact Interior-Point Method for Large Scale Nonlinear Optimization Problems, Annali dell'Università di Ferrara, Sezione VII Scienze Matematiche, Vol. IL, 333-357 (2003).
· C. Durazzi, V. Ruggiero, Indefinitely Preconditioned Conjugate Gradient Method for Large Sparse Equality and Inequality Constrained Quadratic Problems, Numerical Linear Algebra with Applications, 10, 673-688 (2003).
· C. Durazzi, V. Ruggiero, Numerical Solution of Special Linear and Quadratic Programs via a Parallel Interior-Point Method, Parallel Computing, 29/4, 485-503 (2003).
· C. Durazzi, V. Ruggiero, G. Zanghirati, A Parallel Interior-Point Method for Linear and Quadratic Programs with Special Structure, Journal of Optimization Theory and Applications, 110, 2, 289-313 (2001).
· V. Ruggiero, L. Zanni, Splitting and Projection-type Methods for Large Convex Quadratic Programs, Annali dell'Università di Ferrara, Suppl. n. 46, Ferrara, 521-540 (2000).
· V. Ruggiero, L. Zanni, A modified projection algorithm for large strictly convex quadratic programs , Journal of Optimization Theory and Applications, 104, 2, 255-279 (2000).
· V. Ruggiero, L. Zanni, On a Class of Iterative Methods for Large-Scale Convex Quadratic Program, in Numerical Methods in Optimization, (A. Maugeri, E. Galligani eds.), Rendiconti del Circolo Matematico di Palermo, Serie II, Supplemento, n. 58, Palermo, 231-228 (1999).
· E. Galligani, V. Ruggiero, L. Zanni, A Minimization Method for the Solution of Large Symmetric Eigenproblems , International Journal of Computer Mathematics, Vol. 70, Exeter 99-115 (1998).
· E. Galligani, V. Ruggiero, The two-stage arithmetic mean method, Applied Mathematics and Computation, Vol. 85, New York, 245-264 (1997).
· E. Galligani, V. Ruggiero, L. Zanni, Splitting Methods and Parallel Solution of Constrained Quadratic Programs, Rendiconti del Circolo Matematico di Palermo, Serie II, Supplemento, n. 48, Palermo, 121-136 (1997).
· E. Galligani, V. Ruggiero, L. Zanni, On Splitting Methods for Constrained Quadratic Programs in Data Analysis, Computers & Mathematics with Applications, Vol. 32, N. 5, Exeter, 1-9 (1996).
· E. Galligani, V. Ruggiero, L. Zanni Splitting Methods for Quadratic Optimization in Data Analysis, International Journal of Computer Mathematics, Vol. 63, Yverdon, 289--307 (1997).
· E. Galligani, V. Ruggiero, A Polynomial Preconditioner for Block Tridiagonal Matrices, Parallel Algorithms and Applications, Vol. 3, Yverdon, 227-237 (1994).
· V. Ruggiero, Polynomial Preconditioning on Vector Computers, Applied Mathematics and Computation, Vol. 59, New York, 131-150 (1993).
· I. Galligani, V. Ruggiero, Numerical Solution of Equality-Constrained Quadratic Programming Problems on Vector-Parallel Computers, Optimization Methods and Software, Vol. 2, Yverdon, 233-247 (1993).
· E. Galligani, V. Ruggiero, A Parallel Algorithm for Solving Block Tridiagonal Linear Systems, Computers & Mathematics with Applications, Vol. 24, N. 4, Exeter, 15-21 (1992).
· E. Galligani, V. Ruggiero, The Arithmetic Mean Preconditioner for Multivector Computers, International Journal of Computer Mathematics, Vol. 44, N. 1-4, Yverdon, 207-244 (1992); also in Preconditioned IterativeMethods, (D. J. Evans ed.), Topics in Computer Mathematics, Vol. 4, Gordon & Breach Science Publishers, Yverdon, 273-288 (1994).
· I. Galligani, V. Ruggiero, F. Zama, A Polynomial Preconditioner for The Conjugate Gradient Method on Vector Computers, Atti dell'Accademia delle Scienze dell'Istituto di Bologna, Memorie, Serie V, N. 4, Bologna, 63-77 (1990).
· E. Galligani, V. Ruggiero, An Iterative Method for Large Sparse Linear Systems on A Vector Computer, Computers & Mathematics with Applications, Vol. 20, 1, Exeter, 25-28 (1990).
· I. Galligani, V. Ruggiero, The Arithmetic Mean Method for Solving Essentially Positive Systems on A Vector Computer, International Journal of Computer Mathematics, Vol. 32, Yverdon, 113--121 (1990).
· I. Galligani, V. Ruggiero, Solving Large Systems of Linear Ordinary Differential Equations on A Vector Computer, Parallel Computing, 9, Amsterdam, 359-365 (1988/89).
· F. Durì, V, Ruggiero, Analisi di algoritmi per la risoluzione di sistemi tridiagonali su un calcolatore vettoriale, Atti dell'Accademia delle Scienze dell'Istituto di Bologna, Rendiconti, Serie XIV, Tomo V, Bologna, 1-46 (1987/88).
· V. Ruggiero, An Analysis of The Memory Interference in The Cray X-MP/12, Atti dell'Accademia delle Scienze dell'Istituto di Bologna, Rendiconti, Serie XIV, Tomo IV, Bologna, 1-20 (1987).
· V. Ruggiero, Un particolare metodo per la determinazione di autovalori di matrici tridiagonali simmetriche, Calcolo, Vol. XXI, fasc. III, Pisa, 213-227 (1984).
· M. Golinelli, V. Ruggiero, Generalizzazione dell'algoritmo di Grau per la valutazione numerica dei moduli degli zeri di un polinomio a coefficienti reali, Annali dell'Università di Ferrara, Sez. VII, Sc. Mat., Vol. XXVI, Ferrara, 203-212 (1980).
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