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ORCIDMatteo Lapucci
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2020 – today
- 2025
[j21]Enrico Civitelli
, Alessio Sortino, Matteo Lapucci, Francesco Bagattini, Giulio Galvan:
A Robust Initialization of Residual Blocks for Effective ResNet Training Without Batch Normalization. IEEE Trans. Neural Networks Learn. Syst. 36(1): 1947-1952 (2025)- 2024
- [j20]Matteo Lapucci, Fabio Schoen:
Advances in nonlinear optimization and equilibrium problems - Special issue editorial. EURO J. Comput. Optim. 12: 100086 (2024) - [j19]Paola Cappanera, Leonardo Di Gangi, Matteo Lapucci, Giulia Pellegrini, Marco Roma, Fabio Schoen, Alessio Sortino:
Integrated task scheduling and personnel rostering of airports ground staff: A case study. Expert Syst. Appl. 238(Part C): 121953 (2024) - [j18]Matteo Lapucci, Pierluigi Mansueto:
Cardinality-Constrained Multi-objective Optimization: Novel Optimality Conditions and Algorithms. J. Optim. Theory Appl. 201(1): 323-351 (2024) - [j17]Matteo Lapucci, Alessio Sortino:
On the Convergence of Inexact Alternate Minimization in Problems with ℓ 0 Penalties. Oper. Res. Forum 5(2): 41 (2024) - [j16]Matteo Lapucci:
Convergence and complexity guarantees for a wide class of descent algorithms in nonconvex multi-objective optimization. Oper. Res. Lett. 54: 107115 (2024) - [i5]Matteo Lapucci, Davide Pucci:
Convergence Conditions for Stochastic Line Search Based Optimization of Over-parametrized Models. CoRR abs/2408.03199 (2024) - [i4]Matteo Lapucci, Davide Pucci:
Effectively Leveraging Momentum Terms in Stochastic Line Search Frameworks for Fast Optimization of Finite-Sum Problems. CoRR abs/2411.07102 (2024) - [i3]Pierluigi Mansueto, Mihai Dragusanu, Anjum Saeed, Monica Malvezzi, Matteo Lapucci, Gionata Salvietti:
Optimization-Driven Design of Monolithic Soft-Rigid Grippers. CoRR abs/2412.07556 (2024) - 2023
- [j15]Matteo Lapucci, Pierluigi Mansueto:
A limited memory Quasi-Newton approach for multi-objective optimization. Comput. Optim. Appl. 85(1): 33-73 (2023) - [j14]Christian Kanzow, Matteo Lapucci:
Inexact penalty decomposition methods for optimization problems with geometric constraints. Comput. Optim. Appl. 85(3): 937-971 (2023) - [j13]Matteo Lapucci, Tommaso Levato, Francesco Rinaldi, Marco Sciandrone:
A Unifying Framework for Sparsity-Constrained Optimization. J. Optim. Theory Appl. 199(2): 663-692 (2023) - [j12]Matteo Lapucci, Pierluigi Mansueto, Fabio Schoen:
A memetic procedure for global multi-objective optimization. Math. Program. Comput. 15(2): 227-267 (2023) - [j11]Matteo Lapucci, Pierluigi Mansueto:
Improved front steepest descent for multi-objective optimization. Oper. Res. Lett. 51(3): 242-247 (2023) - [i2]Tommaso Aldinucci, Matteo Lapucci:
Loss-Optimal Classification Trees: A Generalized Framework and the Logistic Case. CoRR abs/2306.00857 (2023) - 2022
- [j10]Riccardo Bisori, Matteo Lapucci, Marco Sciandrone:
A study on sequential minimal optimization methods for standard quadratic problems. 4OR 20(4): 685-712 (2022) - [j9]Matteo Lapucci:
A penalty decomposition approach for multi-objective cardinality-constrained optimization problems. Optim. Methods Softw. 37(6): 2157-2189 (2022) - 2021
- [j8]Enrico Civitelli, Matteo Lapucci, Fabio Schoen, Alessio Sortino:
An effective procedure for feature subset selection in logistic regression based on information criteria. Comput. Optim. Appl. 80(1): 1-32 (2021) - [j7]Guido Cocchi, Matteo Lapucci, Pierluigi Mansueto:
Pareto front approximation through a multi-objective augmented Lagrangian method. EURO J. Comput. Optim. 9: 100008 (2021) - [j6]Giulio Galvan, Matteo Lapucci, Chih-Jen Lin, Marco Sciandrone:
A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems. J. Mach. Learn. Res. 22: 23:1-23:38 (2021) - [j5]Matteo Lapucci, Tommaso Levato, Marco Sciandrone:
Convergent Inexact Penalty Decomposition Methods for Cardinality-Constrained Problems. J. Optim. Theory Appl. 188(2): 473-496 (2021) - [i1]Enrico Civitelli, Alessio Sortino, Matteo Lapucci, Francesco Bagattini, Giulio Galvan:
A Robust Initialization of Residual Blocks for Effective ResNet Training without Batch Normalization. CoRR abs/2112.12299 (2021) - 2020
- [j4]Guido Cocchi, Matteo Lapucci:
An augmented Lagrangian algorithm for multi-objective optimization. Comput. Optim. Appl. 77(1): 29-56 (2020) - [j3]Giulio Galvan, Matteo Lapucci, Tommaso Levato, Marco Sciandrone:
An Alternating Augmented Lagrangian method for constrained nonconvex optimization. Optim. Methods Softw. 35(3): 502-520 (2020)
2010 – 2019
- 2019
- [j2]Leonardo Di Gangi, Matteo Lapucci, Fabio Schoen, Alessio Sortino:
An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series. Comput. Optim. Appl. 74(3): 919-948 (2019) - [j1]Giulio Galvan, Matteo Lapucci:
On the convergence of inexact augmented Lagrangian methods for problems with convex constraints. Oper. Res. Lett. 47(3): 185-189 (2019)
Coauthor Index
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