Publications

Preprints

• S. Bellavia, D. Palitta, M. Porcelli, V. Simoncini, Regularized methods via cubic model subspace minimization for nonconvex optimization, pp. 1-28, 2023 (arXiv:2306.14290)

• L. Cocchi, F. Marini, M. Porcelli, E. Riccietti, Black-box optimization for the design of a jet plate for impingement cooling, pp. 1-26, 2024.

Journal articles

1. M. Macconi, B. Morini, M. Porcelli, Trust-region quadratic methods for nonlinear systems of mixed equalities and inequalities, Applied Numerical Mathematics, 59:5 (2009), pp. 859-876.

2. M. Macconi, B. Morini, M. Porcelli, A Gauss-Newton method for solving bound-constrained underdetermined nonlinear systems, Optimization Methods and Software, 24:2 (2009), pp. 219-235.

3. B. Morini, M. Porcelli, R.H. Chan, A Reduced Newton method for constrained linear least-squares problems, Journal of Computational and Applied Mathematics, 233:9 (2010), pp. 2200-2212.

4. M. Porcelli,  Metodi trust-region a convergenza quadratica per problemi ai minimi quadrati non lineari con vincoli semplici e problemi di ammissibilità non lineari, La Matematica a nella Società e nella Cultura, Rivista dell'Unione Matematica Italiana, Serie I, Vol IV, a Aprile 2011, pp. 67-70.

5. B. Morini, M. Porcelli, TRESNEI, a Matlab trust-region solver for systems of nonlinear equalities and inequalities, Computational Optimization and Applications, 51:1 (2012), pp. 27-49.

6. 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. 1-22.

7. M. Porcelli, On the convergence of an inexact Gauss-Newton trust-region method for nonlinear least-squares problems with simple bounds, Optimization Letters, 7:3 (2013), pp. 447-465.

8. 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. 321-340.

9. S. Bellavia, M. Porcelli, Preconditioning issues in the numerical solution of nonlinear equations and nonlinear least-squares , Pesquisa Operacional, 34:3 (2014), pp. 421-445.

10. M. Porcelli, F. Rinaldi, A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for l1-regularized least-squares, Computational Optimization and Applications, 59:3 (2014), pp. 565-589.

11. 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 NOSA-ITACA , CALCOLO, 52:2 (2015), pp. 167­-186.

12. M. Porcelli, V. Simoncini, M. Tani, Preconditioning of active-set Newton methods for PDE-Constrained optimal control problems, SIAM Journal on Scientific Computing, 37:5 (2015), pp. S472-S502.

13. 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. 380-390.

14. M. Porcelli, Ph.L. Toint, BFO, a trainable derivative-free Brute Force Optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete variables, ACM Transactions on Mathematical Software, 44:1 (2017), Article 6.

15. M. Porcelli, V. Simoncini, M. Stoll, Preconditioning PDE-constrained optimization with L^1-sparsity and control constraints, Computers and Mathematics with Applications, 74:5 (2017), pp. 1059-1075.

16. B. Iannazzo, M. Porcelli, The Riemannian Barzilai-Borwein method with nonmonotone line-search and the matrix geometric mean computation, IMA Journal of Numerical Analysis, 38:1 (2018), pp. 495-517.

17. B. Morini, M. Porcelli, Ph.L. Toint, Approximate norm descent methods for constrained nonlinear systems, Mathematics of Computation, 87 (2018), pp. 1327-1351.

18. L. Marini, B. Morini, M. Porcelli, Quasi-Newton methods for constrained nonlinear systems: complexity analysis and application, Computational Optimization and Applications, 72:1 (2018), pp. 147-170.

19. S. Bellavia, J. Gondzio, M. Porcelli, An inexact dual logarithmic barrier method for solving sparse semidefinite programs, Mathematical Programming Ser. A, 178:1-2 (2019), pp. 109-143.

20. M. Porcelli, Ph.L. Toint, A note on using performance and data profiles for training algorithms, ACM Transactions on Mathematical Software, 45:2 (2019), Article 20.

21. J.W. Pearson, M. Porcelli, M. Stoll, Interior Point Methods and Preconditioning for PDE-Constrained Optimization Problems Involving Sparsity Terms , Numerical Linear Algebra with Applications, 27:2 (2020): e2276.

22. M. Girardi, C. Padovani, D. Pellegrini, M. Porcelli, L. Robol, Finite element model updating for structural applications, Journal of Computational and Applied Mathematics, 370 (2020), 112675.

23. A. Papini, M. Porcelli, C. Sgattoni, On the global convergence of a new spectral residual algorithm for nonlinear systems of equations, Bollettino dell'Unione Matematica Italiana, 14 (2021), pp. 367–378.

24. S. Bellavia, J. Gondzio, M. Porcelli, A relaxed interior point method for low-rank semidefinite programming problems with applications to matrix completion, Journal of Scientific Computing, 89 (2021), Article 46.

25. E. Meli, B. Morini, M. Porcelli, C. Sgattoni, Solving nonlinear systems of equations via spectral residual methods: stepsize selection and applications,  Journal of Scientific Computing, 90 (2022), Article 30.

26. D. Garmatter, M. Porcelli, F. Rinaldi, M. Stoll, Improved Penalty Algorithm for Mixed Integer PDE Constrained Optimization Problems, Computers and Mathematics with Applications,  116 (2022), pp . 2-14.

27. M. Porcelli, Ph.L. Toint, Exploiting problem structure in derivative free optimization, ACM Transactions on Mathematical Software, 48:1 (2022), Article 6. 

28. C. Padovani, M. Porcelli, A semidefinite programming approach for the projection onto the cone of negative semidefinite symmetric tensors with applications to solid mechanics, CALCOLO, 59:33 (2022), pp. 1-31 (pdf).

29. D. Garmatter, M. Porcelli, F. Rinaldi, M. Stoll, An Improved Penalty Algorithm using Model Order Reduction for MIPDECO problems with partial observations, Computational Optimization and Applications, 84 (2023), pp. 191-223.

30. M. Porcelli, V. Simoncini, Numerical solution of a class of quasi-linear matrix equations, Linear Algebra and Its Applications, 664 (2023), pp. 349-368.

31. Z. Shi, M. Meacci, M. Porcelli, E. Meli, A. Rindi, A degraded adhesion model for creep force calculation based on the CONTACT algorithm, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, doi: 10.1177/09544097231162228 (2023). 

Conference proceedings

1. R.H. Chan, B. Morini, M. Porcelli, Affine scaling methods for image deblurring problems, American Institute of Physics Conference Proceedings 1281, 2 (2010), pp. 1043-1046.

2. C. Carcasci, L. Marini, B. Morini, M. Porcelli, M. Micio, P.L. Di Pillo, Modular tool for the simulation of compressor trains for oil and gas applications, Energy Procedia, 82 (2015), pp. 546-553.

3. V. Binante, M. Girardi, M. Lucchesi, C. Padovani, D. Pellegrini, M. Porcelli, "Safety assessment of masonry constructions via numerical tools: the NOSA-ITACA code", In: 18th ICOMOS General Assembly and Symposium: "Heritage and Landscape as Human Values" (Florence, Italy, 9-14 November 2014)

4. M. Girardi, C. Padovani, D. Pellegrini, M. Porcelli, L. Robol, "FEA for masonry structures and vibration-based model updating using NOSA-ITACA", In: Proceedings of the International Masonry Society Conferences Volume 0, Issue 222279, 2018, Pages 723-735, 10th International Masonry Conference, IMC 2018; (Milan, Italy; 9 - 11 July 2018).

5. G. Seraghiti, A. Awari, A. Vandaele, M. Porcelli, N. Gillis, Accelerated Algorithms for Nonlinear Matrix Decomposition with the ReLU function", MLSP 2023, 17-20 September, 2023, Rome. [arXiv] [code]

6. L. Cavalli, D. Brandoni, M. Porcelli, E. Pascolo, Dictionary Learning for data compression within a Digital Twin Framework, CEUR-WP, 29-30 May 2024, Naples.  [code]

Book chapter

1. M. Girardi, C. Padovani, D. Pellegrini, M. Porcelli, L. Robol, Numerical modelling of historical masonry structures with the fnite element code NOSA-ITACA, In: Bretti, G., Cavaterra, C., Solci, M., Spagnuolo, M. (eds) Mathematical Modeling in Cultural Heritage. MACH 2021. Springer INdAM Series, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-99-3679-3_9

Other publications

• B. Colson, M. Porcelli, Ph.L. Toint, Aircraft fuselage sizing with multilevel optimization, 2013-TR-019, ISTI-CNR and naXys-07-2013, Dept of Mathematics, UNamur, Namur (B).

• V. Binante, M. Girardi, C. Padovani, G. Pasquinelli, M. Porcelli, NOSA-ITACA documentation, 2013-TR-050, ISTI-CNR.

• D. Brandoni, M. Porcelli, V. Simoncini, A spectral PALM algorithm for matrix and tensor-train based Dictionary Learning, pp. 1-27, 2021 (arxiv:2107.11644).

Posters

• V. Binante, M. Girardi, M. Lucchesi, C. Padovani, G. Pasquinelli, D. Pellegrini, M. Porcelli, Progetto NOSA-ITACA - caso studio: il Voltone, piazza della Repubblica, Livorno. Poster in: Salone dell'Arte e del Resaturo di Firenze. Florence (Italy), November 8-10, 2012. [pdf]

• M. Porcelli, V. Binante, M. Girardi, C. Padovani, G. Pasquinelli, D. Pellegrini, On the numerical solution of constrained eigenvalue problems in structural engineering. Poster in: Recent Advances on Optimization. Toulouse (France) July, 24-26 2013. [pdf]

Theses

• M. Porcelli,  Trust-region quadratic methods for bound-constrained nonlinear least-squares and nonlinear feasibility problems, PhD Thesis, 2010 [pdf].

• M. Porcelli, A new trust-region method for nonsquare systems of nonlinear equations with simple bounds, Master Thesis, 2006 (italian version) [pdf].

• M. Porcelli, The Newton method for the Riccati’s algebraic equations, Bachelor Thesis 2004.