A Chebyshev technique for the solution of optimal control problems with nonlinear programming methods. - Francesco Mezzadri; Emanuele Galligani (Math. Comput. Simul., 2016)

On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems. - Francesco Mezzadri; Emanuele Galligani (Algorithms, 2017)

A nonlinearity lagging method for non-steady diffusion equations with nonlinear convection terms. - Francesco Mezzadri; Emanuele Galligani (Adv. Comput. Math., 2019)

Splitting Methods for a Class of Horizontal Linear Complementarity Problems. - Francesco Mezzadri; Emanuele Galligani (J. Optim. Theory Appl., 2019)

On the solution of general absolute value equations. - Francesco Mezzadri (Appl. Math. Lett., 2020)

The Loss Surfaces of Neural Networks with General Activation Functions. - Nicholas P. Baskerville; Jonathan P. Keating; Francesco Mezzadri; Joseph Najnudel (CoRR, 2020)

A second-order measure of boundary oscillations for overhang control in topology optimization. - Francesco Mezzadri; Xiaoping Qian (J. Comput. Phys., 2020)

On the convergence of modulus-based matrix splitting methods for horizontal linear complementarity problems in hydrodynamic lubrication. - Francesco Mezzadri; Emanuele Galligani (Math. Comput. Simul., 2020)

Modulus-based matrix splitting methods for horizontal linear complementarity problems. - Francesco Mezzadri; Emanuele Galligani (Numer. Algorithms, 2020)

Modulus-based synchronous multisplitting methods for solving horizontal linear complementarity problems on parallel computers. - Francesco Mezzadri (Numer. Linear Algebra Appl., 2020)

A spin-glass model for the loss surfaces of generative adversarial networks. - Nicholas P. Baskerville; Jonathan P. Keating; Francesco Mezzadri; Joseph Najnudel (CoRR, 2021)

Modulus-based matrix splitting methods for a class of horizontal nonlinear complementarity problems. - Francesco Mezzadri; Emanuele Galligani (Numer. Algorithms, 2021)

A modulus-based nonsmooth Newton's method for solving horizontal linear complementarity problems. - Francesco Mezzadri; Emanuele Galligani (Optim. Lett., 2021)

Universal characteristics of deep neural network loss surfaces from random matrix theory. - Nicholas P. Baskerville; Jonathan P. Keating; Francesco Mezzadri; Joseph Najnudel; Diego Granziol (CoRR, 2022)

Projected Splitting Methods for Vertical Linear Complementarity Problems. - Francesco Mezzadri; Emanuele Galligani (J. Optim. Theory Appl., 2022)

A modulus-based formulation for the vertical linear complementarity problem. - Francesco Mezzadri (Numer. Algorithms, 2022)

A Framework for Physics-Informed Deep Learning Over Freeform Domains. - Francesco Mezzadri; Joshua Gasick; Xiaoping Qian (Comput. Aided Des., 2023)

Easy-to-use formulations based on the homogenization theory for vascular stent design and mechanical characterization. - Dario Carbonaro; Nicola Ferro 002; Francesco Mezzadri; Diego Gallo; Alberto L. Audenino; Simona Perotto; Umberto Morbiducci; Claudio Chiastra (Comput. Methods Programs Biomed., 2024)

A modulus-based framework for weighted horizontal linear complementarity problems. - Francesco Mezzadri (Appl. Math. Comput., 2025)

Modulus-based matrix splitting algorithms for generalized complex-valued horizontal linear complementarity problems. - Francesco Mezzadri; Emanuele Galligani (J. Comput. Appl. Math., 2025)