Publications

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.

Pre-prints

1. Combining Gradient Information and Primitive Directions for High-Performance Mixed-Integer Optimization.
   M. Lapucci, G. Liuzzi, S. Lucidi, P. Mansueto.
   ArXiv pre-print (2024).
   DOI: 10.48550/arXiv.2407.14416.
2. Effective Front-Descent Algorithms with Convergence Guarantees.
   M. Lapucci, P. Mansueto, D. Pucci.
   ArXiv pre-print (2024).
   DOI: 10.48550/arXiv.2405.08450.
3. Memetic Differential Evolution Methods for Semi-Supervised Clustering.
   P. Mansueto, F. Schoen.
   ArXiv pre-print (2024).
   DOI: 10.48550/arXiv.2403.04322.
4. A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization.
   F. Carciaghi, S. Magistri, P. Mansueto, F. Schoen.
   ArXiv pre-print (2024).
   DOI: 10.48550/arXiv.2402.00726.

Talks

• 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 MOO 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

• s_mdeclust v1.0.0 — github.com/pierlumanzu/s_mdeclust.
• g_dfl v1.0.0 — github.com/pierlumanzu/g_dfl.
• fd_framework v1.0.0 — github.com/pierlumanzu/fd_framework.
• biobj_acquistion_function_for_BO v1.0.0 — github.com/FranciC19/biobj_acquistion_function_for_BO.
• cc-moo v1.0.0 — github.com/pierlumanzu/cc-moo.
• front-alamo v1.0.0 — github.com/pierlumanzu/front-alamo.
• ifsd v1.0.1 — github.com/pierlumanzu/ifsd.
• limited_memory_method_for_MOO v1.0.2 — github.com/pierlumanzu/limited_memory_method_for_MOO.
• nsma v1.0.7 — github.com/pierlumanzu/nsma.

Contacts

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

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