PhD Student of Smart Computing at Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze.

My research interests include constrained, multi-objective and sparse non-linear optimization and optimization methods for machine learning and statistics.


  1. A study on sequential minimal optimization methods for standard quadratic problems.
    R. Bisori, M. Lapucci, M. Sciandrone.
    4OR (2021).
    DOI: 10.1007/s10288-021-00496-9
  2. Can machine learning models support physicians in systemic lupus erythematosus diagnosis? Results from a monocentric cohort.
    F. Ceccarelli, M. Lapucci, G. Olivieri, A. Sortino, M. Sciandrone et al.
    Joint Bone Spine (2021).
    DOI: 10.1016/j.jbspin.2021.105292
  3. Pareto front approximation through a multi-objective augmented
    Lagrangian method.
    G. Cocchi, M. Lapucci, P. Mansueto.
    EURO Journal on Computational Optimization (2021).
    DOI: 10.1016/j.ejco.2021.100008
  4. 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).
    DOI: 10.1007/s10589-021-00288-1
  5. 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).
  6. 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).
    DOI: 10.1016/j.semarthrit.2021.02.005
  7. Convergent Inexact Penalty Decomposition Methods for Cardinality-Constrained Problems.
    M. Lapucci, T. Levato, M. Sciandrone.
    Journal of Optimization Theory and Applications (2020).
    DOI: 10.1007/s10957-020-01793-9
  8. An augmented Lagrangian algorithm for multi-objective optimization.
    G. Cocchi, M. Lapucci.
    Computational Optimization and Applications (2020).
    DOI: 10.1007/s10589-020-00204-z
  9. 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).
    DOI: 10.1007/s10589-019-00134-5
  10. On the convergence of inexact Augmented Lagrangian methods for problems with convex constraints.
    G. Galvan, M. Lapucci
    Operations Research Letters (2019).
    DOI: 10.1016/j.orl.2019.03.006
  11. An Alternating Augmented Lagrangian method for constrained nonconvex optimization.
    G. Galvan, M. Lapucci, T. Levato, M. Sciandrone
    Optimization Methods and Software (2019).
    DOI: 10.1080/10556788.2019.1576177

My ORCID number: 0000-0002-2488-5486


  1. ODS 2021 (Rome)A Unifying Framework for Sparsity Constrained Optimization
  2. SIMAI 2020+2021 (Parma)A Two-Level Decomposition Framework  Exploiting First and Second Order Information for SVM Training  Problems
  3. SIAM Conference on Optimization (OP21)A Penalty Decomposition Approach for Multi-Objective Cardinality-Constrained Optimization Problems.
  4. EUROPT 2021 (Toulouse, virtual)A Derivative-free Adaptation of the Penalty Decomposition Method for Sparse Optimization.
  5. ODS 2019 (Genova) An Efficient Optimization Approach for Subset Selection, with Application to Linear Regression and Auto-Regressive Time Series.


Via di Santa Marta, 3 – 50139 Firenze FI (Italy)

E-mail: matlapucci at gmail dot com