PhD in Information Engineering , Department of Information Engineering (DINFO),
University of Florence

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


  1. Cardinality-Constrained Multi-objective Optimization: Novel Optimality Conditions and Algorithms.
    M. Lapucci, P. Mansueto.
    Journal of Optimization Theory and Applications (2024).
    DOI: 10.1007/s10957-024-02397-3.
  2. Improved front steepest descent for multi-objective optimization.
    M. Lapucci, P. Mansueto.
    Operations Research Letters (2023).
    DOI: 10.1016/j.orl.2023.03.001.
  3. 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.
  4. 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.
  5. 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.
  6. Memetic differential evolution methods for clustering problems.
    P. Mansueto, F. Schoen.
    Pattern Recognition (2021).
    DOI: 10.1016/j.patcog.2021.107849.
  7. 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