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for nonlinear systems of mixed equalities and inequalities,
Applied Numerical Mathematics, 59:5 (2009), pp. 859876.
 M. Macconi, B. Morini, M. Porcelli, A GaussNewton method for
solving boundconstrained underdetermined nonlinear systems,
Optimization Methods and Software,
24:2 (2009), pp. 219235.
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Reduced Newton method for constrained linear leastsquares problems,
Journal of Computational and Applied
Mathematics, 233:9 (2010), pp. 22002212.
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Metodi trustregion a convergenza quadratica per problemi ai minimi quadrati
non lineari con vincoli semplici e problemi di ammissibilità non lineari, La Matematica
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a Aprile 2011, pp. 6770.
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solver for systems of nonlinear equalities and inequalities,
Computational Optimization and Applications,
51:1 (2012), pp. 2749.
 N.I.M. Gould, M. Porcelli, Ph.L. Toint, Updating the regularization parameter in the
adaptive cubic regularization algorithm, Computational Optimization and Applications,
53:1 (2012), pp. 122.
 M. Porcelli, On the convergence of an inexact GaussNewton trustregion method
for nonlinear leastsquares problems with simple bounds,
Optimization Letters, 7:3 (2013), pp. 447–465.
 S. Bellavia, B. Morini, M. Porcelli, New updates of incomplete LU factorizations and applications to large nonlinear systems,
Optimization Methods and Software,
29:2 (2014), pp. 321340.
 S. Bellavia, M. Porcelli, Preconditioning issues in the numerical solution of nonlinear
equations and nonlinear leastsquares , Pesquisa Operacional,
34:3 (2014), pp. 421445.
 M. Porcelli, F. Rinaldi, A variable fixing version of the twoblock nonlinear constrained
GaussSeidel algorithm for l1regularized leastsquares , Computational Optimization and Applications, 59:3 (2014), pp. 565589.
 M. Porcelli, V. Binante, M. Girardi, C. Padovani, G. Pasquinelli,
A solution procedure for constrained eigenvalue
problems and its application within the structural finite element code NOSAITACA , CALCOLO, 52:2 (2015), pp. 167186.
 M. Porcelli, V. Simoncini, M. Tani,
Preconditioning of activeset Newton methods for PDEConstrained optimal control problems, SIAM Journal on Scientific Computing, 37:5 (2015), pp. S472S502.
 C. Carcasci, L. Marini, B. Morini, M. Porcelli,
A new modular procedure for industrial plant simulations and its reliable implementation,
Energy, 94:1 (2016), pp. 380390.
 B. Iannazzo, M. Porcelli,
The Riemannian BarzilaiBorwein method
with nonmonotone linesearch and the matrix geometric mean computation, IMA Journal of Numerical Analysis, (2017) drx015. doi: 10.1093/imanum/drx015.
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Approximate norm descent methods
for constrained nonlinear systems, Mathematics of Computation, (2017) https://doi.org/10.1090/mcom/3251.
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BFO, a trainable derivativefree Brute Force Optimizer for nonlinear boundconstrained optimization and equilibrium
computations with continuous and discrete variables, ACM Transactions on Mathematical Software, 44:1 (2017), Article 6, 25 pages.
 M. Porcelli, V. Simoncini, M. Stoll,
Preconditioning PDEconstrained optimization with L^1sparsity and control constraints,
Computers and Mathematics with Applications, 74:5 (2017), pp. 10591075.
