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pro vyhledávání: '"Lapucci A"'
Autor:
Lapucci, Matteo, Pucci, Davide
In this work, we address unconstrained finite-sum optimization problems, with particular focus on instances originating in large scale deep learning scenarios. Our main interest lies in the exploration of the relationship between recent line search a
Externí odkaz:
http://arxiv.org/abs/2411.07102
Autor:
Lapucci, Matteo, Pucci, Davide
In this paper, we deal with algorithms to solve the finite-sum problems related to fitting over-parametrized models, that typically satisfy the interpolation condition. In particular, we focus on approaches based on stochastic line searches and emplo
Externí odkaz:
http://arxiv.org/abs/2408.03199
In this paper we consider bound-constrained mixed-integer optimization problems where the objective function is differentiable w.r.t.\ the continuous variables for every configuration of the integer variables. We mainly suggest to exploit derivative
Externí odkaz:
http://arxiv.org/abs/2407.14416
In this manuscript, we address continuous unconstrained optimization problems and we discuss descent type methods for the reconstruction of the Pareto set. Specifically, we analyze the class of Front Descent methods, which generalizes the Front Steep
Externí odkaz:
http://arxiv.org/abs/2405.08450
In this work, we consider smooth unconstrained optimization problems and we deal with the class of gradient methods with momentum, i.e., descent algorithms where the search direction is defined as a linear combination of the current gradient and the
Externí odkaz:
http://arxiv.org/abs/2403.17613
Autor:
Busoni, Lorenzo, Agapito, Guido, Ballone, Alessandro, Puglisi, Alfio, Goncharov, Alexander, Petrella, Amedeo, Di Cianno, Amico, Balestra, Andrea, Baruffolo, Andrea, Bianco, Andrea, Di Dato, Andrea, Valentini, Angelo, Di Francesco, Benedetta, Sassolas, Benoit, Salasnich, Bernardo, Arcidiacono, Carmelo, Plantet, Cedric, Eredia, Christian, Fantinel, Daniela, Selvestrel, Danilo, Malone, Deborah, Magrin, Demetrio, D'Auria, Domenico, Redaelli, Edoardo, Carolo, Elena, Costa, Elia, Portaluri, Elisa, Cascone, Enrico, Giro, Enrico, Battaini, Federico, Annibali, Francesca, Laudisio, Fulvio, Rodeghiero, Gabriele, Umbriaco, Gabriele, Chauvin, Gael, Di Rico, Gianluca, Pariani, Giorgio, Carlà, Giulia, Capasso, Giulio, Cosentino, Giuseppe, Correia, Jean Jacques, Foppiani, Italo, Di Antonio, Ivan, Farinato, Jacopo, Radhakrishnan, Kalyan Kumar, Gluck, Laurence, Pinard, Laurent, Marafatto, Luca, Scalera, Marcello Agostino, Gullieuszik, Marco, Bonaglia, Marco, Riva, Marco, Xompero, Marco, Bergomi, Maria, Aliverti, Matteo, Genoni, Matteo, Munari, Matteo, Dolci, Mauro, Christophe, Michel, Cantiello, Michele, Colapietro, Mirko, Devaney, Nicholas, Azzaroli, Nicolò, Grani, Paolo, Ciliegi, Paolo, Rabou, Patrick, Feautrier, Philippe, Schipani, Pietro, Ragazzoni, Roberto, Sordo, Rosanna, Briguglio, Runa, Lampitelli, Salvatore, Savarese, Salvatore, Benedetti, Simone, Di Filippo, Simone, Esposito, Simone, Chinellato, Simonetta, Oberti, Sylvain, Rochat, Sylvain, Lapucci, Tommaso, Di Giammatteo, Ugo, Cianniello, Vincenzo, De Caprio, Vincenzo, Hubert, Zoltan
MORFEO (Multi-conjugate adaptive Optics Relay For ELT Observations, formerly MAORY), the MCAO system for the ELT, will provide diffraction-limited optical quality to the large field camera MICADO. MORFEO has officially passed the Preliminary Design R
Externí odkaz:
http://arxiv.org/abs/2310.09005
Autor:
Aldinucci, Tommaso, Lapucci, Matteo
The Classification Tree (CT) is one of the most common models in interpretable machine learning. Although such models are usually built with greedy strategies, in recent years, thanks to remarkable advances in Mixer-Integer Programming (MIP) solvers,
Externí odkaz:
http://arxiv.org/abs/2306.00857
Autor:
Lapucci, Matteo, Mansueto, Pierluigi
Publikováno v:
Journal of Optimization Theory and Applications, 2024
In this paper, we consider multi-objective optimization problems with a sparsity constraint on the vector of variables. For this class of problems, inspired by the homonymous necessary optimality condition for sparse single-objective optimization, we
Externí odkaz:
http://arxiv.org/abs/2304.02369
Autor:
Lapucci, Matteo, Mansueto, Pierluigi
Publikováno v:
Operations Research Letters Volume 51, Issue 3, May 2023, Pages 242-247
In this paper, we deal with the Front Steepest Descent algorithm for multi-objective optimization. We point out that the algorithm from the literature is often incapable, by design, of spanning large portions of the Pareto front. We thus introduce so
Externí odkaz:
http://arxiv.org/abs/2301.03310
Autor:
Lapucci, Matteo, Kanzow, Christian
This paper provides a theoretical and numerical investigation of a penalty decomposition scheme for the solution of optimization problems with geometric constraints. In particular, we consider some situations where parts of the constraints are noncon
Externí odkaz:
http://arxiv.org/abs/2210.05379