| A mathematical programming approach for the solution of the railway yield management problem A Ciancimino, G Inzerillo, S Lucidi, L Palagi Transportation science 33 (2), 168-181, 1999 | 222 | 1999 |
| A MILP methodology to optimize sizing of PV-Wind renewable energy systems R Lamedica, E Santini, A Ruvio, L Palagi, I Rossetta Energy 165, 385-398, 2018 | 89 | 2018 |
| A truncated Newton algorithm for large scale box constrained optimization F Facchinei, S Lucidi, L Palagi SIAM Journal on Optimization 12 (4), 1100-1125, 2002 | 78 | 2002 |
| Machine Learning for the prediction of the dynamic behavior of a small scale ORC system L Palagi, A Pesyridis, E Sciubba, L Tocci Energy 166, 72-82, 2019 | 61 | 2019 |
| Decomposition algorithm model for singly linearly-constrained problems subject to lower and upper bounds CJ Lin, S Lucidi, L Palagi, A Risi, M Sciandrone Journal of Optimization Theory and Applications 141, 107-126, 2009 | 60 | 2009 |
| On the convergence of a modified version of SVM light algorithm L Palagi, M Sciandrone Optimization methods and Software 20 (2-3), 317-334, 2005 | 58 | 2005 |
| On some properties of quadratic programs with a convex quadratic constraint S Lucidi, L Palagi, M Roma SIAM Journal on Optimization 8 (1), 105-122, 1998 | 56 | 1998 |
| Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis R Seccia, D Gammelli, F Dominici, S Romano, AC Landi, M Salvetti, ... PloS one 15 (3), e0230219, 2020 | 55 | 2020 |
| Machine learning use for prognostic purposes in multiple sclerosis R Seccia, S Romano, M Salvetti, A Crisanti, L Palagi, F Grassi Life 11 (2), 122, 2021 | 45 | 2021 |
| On exact augmented Lagrangian functions in nonlinear programming G Di Pillo, S Lucidi Nonlinear Optimization and Applications, 85-100, 1996 | 44 | 1996 |
| A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications L Palagi, E Sciubba, L Tocci Applied Energy 237, 210-226, 2019 | 42 | 2019 |
| Data of patients undergoing rehabilitation programs R Seccia, M Boresta, F Fusco, E Tronci, E Di Gemma, L Palagi, ... Data in brief 30, 105419, 2020 | 38 | 2020 |
| A convergent decomposition algorithm for support vector machines S Lucidi, L Palagi, A Risi, M Sciandrone Computational Optimization and Applications 38, 217-234, 2007 | 38 | 2007 |
| Neural networks for small scale ORC optimization A Massimiani, L Palagi, E Sciubba, L Tocci Energy Procedia 129, 34-41, 2017 | 36 | 2017 |
| An exact algorithm for nonconvex quadratic integer minimization using ellipsoidal relaxations C Buchheim, M De Santis, L Palagi, M Piacentini SIAM Journal on Optimization 23 (3), 1867-1889, 2013 | 34 | 2013 |
| Optimal siting and sizing of wayside energy storage systems in a DC railway line R Lamedica, A Ruvio, L Palagi, N Mortelliti Energies 13 (23), 6271, 2020 | 31 | 2020 |
| Computational approaches to max-cut L Palagi, V Piccialli, F Rendl, G Rinaldi, A Wiegele Handbook on semidefinite, conic and polynomial optimization, 821-847, 2012 | 31 | 2012 |
| Convergence to second-order stationary points of a primal-dual algorithm model for nonlinear programming G Di Pillo, S Lucidi, L Palagi Mathematics of Operations Research 30 (4), 897-915, 2005 | 29 | 2005 |
| Global optimization issues in deep network regression: an overview L Palagi Journal of Global Optimization 73 (2), 239-277, 2019 | 28 | 2019 |
| A convergent hybrid decomposition algorithm model for SVM training S Lucidi, L Palagi, A Risi, M Sciandrone IEEE Transactions on Neural Networks 20 (6), 1055-1060, 2009 | 28 | 2009 |
Veronica PiccialliProfessore, Dipartimento di Ingegneria Informatica, Automatica e Gestionale Antonio RubertiEmail verificata su uniroma1.it
