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.
- Google Scholar profile.
- ORCID number: 0000-0002-1394-0937.
Publications
- 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. - 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. - Memetic differential evolution methods for clustering problems.
P. Mansueto, F. Schoen.
Pattern Recognition (2021).
DOI: 10.1016/j.patcog.2021.107849. - 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.
Talks
- 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
- NSMA v1.0.0
Github Repository: github.com/pierlumanzu/nsma.
DOI: 10.5281/zenodo.7299857.
Contacts
Via di Santa Marta, 3 – 50139 Firenze FI (Italy)
E-mails: pierluigi dot mansueto at unifi dot it, pierluigimansueto at gmail dot com
