My research interests include constrained, multi-objective and sparse non-linear optimization and optimization methods for machine learning and statistics.
- Pareto front approximation through a multi-objective augmented
G. Cocchi, M. Lapucci, P. Mansueto.
EURO Journal on Computational Optimization (2021).
- An effective procedure for feature subset selection in logistic regression based on information criteria.
E. Civitelli, M. Lapucci, F. Schoen, A. Sortino.
Computational Optimization and Applications (2021).
- A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems.
G. Galvan, M. Lapucci, C.-J. Lin, M. Sciandrone.
Journal of Machine Learning Research (2021).
- Comprehensive Disease Control in Systemic Lupus Erythematosus.
F. Ceccarelli, G. Olivieri, A. Sortino, M. Lapucci, M. Sciandrone et al..
Seminars in Arthritis and Rheumatism (2021).
- Convergent Inexact Penalty Decomposition Methods for Cardinality-Constrained Problems.
M. Lapucci, T. Levato, M. Sciandrone.
Journal of Optimization Theory and Applications (2020).
- An augmented Lagrangian algorithm for multi-objective optimization.
G. Cocchi, M. Lapucci.
Computational Optimization and Applications (2020).
- An efficient optimization approach for best subset selection in linear regression, with application to model selection and fitting in autoregressive time-series.
L. Di Gangi, M. Lapucci, F. Schoen, A. Sortino
Computational Optimization and Applications (2019).
- On the convergence of inexact Augmented Lagrangian methods for problems with convex constraints.
G. Galvan, M. Lapucci
Operations Research Letters (2019).
- An Alternating Augmented Lagrangian method for constrained nonconvex optimization.
G. Galvan, M. Lapucci, T. Levato, M. Sciandrone
Optimization Methods and Software (2019).
My ORCID number: 0000-0002-2488-5486
- ODS 2021 (Rome) – A Unifying Framework for Sparsity Constrained Optimization
- SIMAI 2020+2021 (Parma) – A Two-Level Decomposition Framework Exploiting First and Second Order Information for SVM Training Problems
- SIAM Conference on Optimization (OP21) – A Penalty Decomposition Approach for Multi-Objective Cardinality-Constrained Optimization Problems.
- EUROPT 2021 (Toulouse, virtual) – A Derivative-free Adaptation of the Penalty Decomposition Method for Sparse Optimization.
- ODS 2019 (Genova) – An Efficient Optimization Approach for Subset Selection, with Application to Linear Regression and Auto-Regressive Time Series.
E-mail: matlapucci at gmail dot com