PhD Student of Information Engineering at the
Information Engineering Department of University of Florence.

My research interests are machine learning, optimization applications and other topics related to them. In particular, my principal addressed problems are multi-objective optimization, clustering and anomaly detection.


  1. Improved front steepest descent for multi-objective optimization.
    M. Lapucci, P. Mansueto.
    Operations Research Letters (2023).
    DOI: 10.1016/j.orl.2023.03.001.
  2. A limited memory Quasi-Newton approach for multi-objective optimization.
    M. Lapucci, P. Mansueto.
    Computational Optimization and Applications (2023).
    DOI: 10.1007/s10589-023-00454-7.
  3. A memetic procedure for global multi-objective optimization.
    M. Lapucci, P. Mansueto, F. Schoen.
    Mathematical Programming Computation (2022).
    DOI: 10.1007/s12532-022-00231-3.
  4. 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.
  5. Memetic differential evolution methods for clustering problems.
    P. Mansueto, F. Schoen.
    Pattern Recognition (2021).
    DOI: 10.1016/j.patcog.2021.107849.
  6. Recognition of Concordances for Indexing in Digital Libraries.
    S. Marinai, S. Capobianco, Z. Ziran, A. Giuntini, P. Mansueto.
    Digital Libraries: The Era of Big Data and Data Science (2020).
    DOI: 10.1007/978-3-030-39905-4_14.




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

E-mails: pierluigi dot mansueto at unifi dot it, pierluigimansueto at gmail dot com