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

My research interests are optimization applications, machine learning, and other related topics. My principal addressed problems concern multi-objective optimization, clustering, and Bayesian optimization.

Pierluigi Mansueto

Publications

  1. Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization.
    M. Lapucci, G. Liuzzi, S. Lucidi, P. Mansueto.
    Accepted in Journal of Global Optimization (2025). DOI: 10.48550/arXiv.2407.14416
  2. Effective Front-Descent Algorithms with Convergence Guarantees.
    M. Lapucci, P. Mansueto, D. Pucci.
    Accepted in SIAM Journal on Optimization (2025). DOI: 10.48550/arXiv.2405.08450
  3. Efficiently Solving Semi-supervised Clustering Problems Through Differential Evolution and Local Optimization.
    P. Mansueto, F. Schoen.
    Journal of Classification (2025). DOI: 10.1007/s00357-025-09528-z
  4. On the computation of the efficient frontier in advanced sparse portfolio optimization.
    A. Annunziata, M. Lapucci, P. Mansueto, D. Pucci.
    4OR (2025). DOI: 10.1007/s10288-025-00600-3
  5. Optimization-Driven Design of Monolithic Soft-Rigid Grippers.
    P. Mansueto, M. Dragusanu, A. Saeed, M. Malvezzi, M. Lapucci, G. Salvietti.
    Soft Robotics (2025). DOI: 10.1177/21695172251359016
  6. A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization.
    F. Carciaghi, S. Magistri, P. Mansueto, F. Schoen.
    Computational Optimization and Applications (2025). DOI: 10.1007/s10589-025-00696-7
  7. 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
  8. Improved front steepest descent for multi-objective optimization.
    M. Lapucci, P. Mansueto.
    Operations Research Letters (2023). DOI: 10.1016/j.orl.2023.03.001
  9. 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
  10. 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
  11. 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
  12. Memetic differential evolution methods for clustering problems.
    P. Mansueto, F. Schoen.
    Pattern Recognition (2021). DOI: 10.1016/j.patcog.2021.107849
  13. 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

Preprints

  1. A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization.
    P. Mansueto.
    ArXiv pre-print (2025). DOI: 10.48550/arXiv.2509.02409
  2. Efficient globalization of heavy-ball type methods for unconstrained optimization based on curve searches.
    F. Donnini, M. Lapucci, P. Mansueto.
    ArXiv pre-print (2025). DOI: 10.48550/arXiv.2505.19705
  3. Projection-based curve pattern search for black-box optimization over smooth convex sets.
    X. Jia, M. Lapucci, P. Mansueto.
    ArXiv pre-print (2025). DOI: 10.48550/arXiv.2503.20616

Talks

  • EUROPT 2025, Southampton — Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization
  • EURO 2025, Leeds — Pareto Front Reconstruction of Multi-Objective Optimization Problems
  • ODS 2024, Badesi — Pareto Front Reconstruction of Multi-Objective Optimization Problems
  • EURO 2024, Copenhagen — A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization
  • EUROPT 2024, Lund — Memetic Differential Evolution Methods for Semi-Supervised Clustering
  • RAMOO 2023, Roma — Improved Front Steepest Descent for Multi-Objective Optimization
  • ODS 2023, Ischia — Improved Front Steepest Descent for Multi-Objective Optimization
  • EUROPT 2023, Budapest — Improved Front Steepest Descent for Multi-Objective Optimization
  • SIAM OP23, Seattle — Cardinality-Constrained Multi-Objective Optimization Problems: Novel Optimality Conditions & Algorithms
  • ODS 2022, Florence — A Quasi-Newton Approach for Large Scale Multi-Objective Optimization
  • EUROPT 2022, Lisbon — A Quasi-Newton Approach for Large Scale Multi-Objective Optimization
  • ODS 2021, Rome — Improving the NSGA-II Algorithm with Descent Steps
  • EUROPT 2021, Toulouse (virtual) — Pareto Front Approximation through a Multi-Objective Augmented Lagrangian Method

Software

Contacts

Via di Santa Marta, 3 – 50139 Firenze (FI), Italy
E-mail: pierluigi dot mansueto at unifi dot it