Toggle navigation
Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti"
Consiglio Nazionale delle Ricerche
Home
About us
Research Groups
People
Projects
Publications
Software
repository
Seminars
News
Project Observatory
Software Repository
Home
About us
Research Groups
People
Projects
Publications
Software
repository
Seminars
News
Project Observatory
Software Repository
Home
/
People
Address
Sapienza
Stanza: n.a.
Laura Palagi
Role:
Research Associate
Research group:
OPTIMA
Recent publications
2022
Optimal Network Design for Waste Management in Regional Districts: the PIPER Project
Marco Boresta
,
Diego Maria Pinto
,
Anna Livia Croella
, Gentile G.,
Laura Palagi
,
Giuseppe Stecca
,
Paolo Ventura
2020
Data of patients undergoing rehabilitation programs
Ruggiero Seccia,
Marco Boresta
, Federico Fusco, Edoardo Tronci, Emanuele Di Gemma,
Laura Palagi
, Massimiliano Mangone, Francesco Agostini, Andrea Bernetti, Valter Santilli, Carlo Damiani, Michela Goffredo, Marco Franceschini
2020
Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming
Marianna De Santis, Giorgio Grani,
Laura Palagi
2020
Block layer decomposition schemes for training deep neural networks
Laura Palagi
, Ruggiero Seccia
2019
Global optimization issues in deep network regression: an overview
Laura Palagi
,
Laura D'Orsi
2019
A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications
Laura Palagi
, Enrico Sciubba, Lorenzo Tocci
2012
SpeeDP: an algorithm to compute SDP bounds for very large Max-Cut instances
L. Grippo,
Laura Palagi
, M. Piacentini, V. Piccialli,
Giovanni Rinaldi
2011
SpeeDP: An algorithm to compute SDP bounds for very large Max-Cut instances
L. Grippo,
Laura Palagi
, M. Piacentini, V. Piccialli,
Giovanni Rinaldi
2007
A convergent hybrid decomposition algorithm model for SVM training
S. Lucidi,
Laura Palagi
, A. Risi,
Marco Sciandrone
2006
On the convergence of hybrid decomposition methods for SVM training
S. Lucidi,
Laura Palagi
, A. Risi,
Marco Sciandrone