Marcello Sanguineti
Informazioni generali e contatti
Dipartimento: DIBRIS
Ufficio: ViaOperaPia, 13
Orario di ricevimento: su appuntamento
Telefono: 010353 2071-2983
Email: marcello@dist.unige.it
Homepage: http://www.dist.unige.it/msanguineti/index.htm
Insegnamenti
- Ricerca Operativa (A.A. 2019/2020) (3° LT; 1° LM)
Insegnamenti precedenti
A.A 2018/2019
- Ricerca Operativa (A.A. 2018/2019) (3° LT; 1° LM)
Curriculum
Professore Associato di Ricerca Operativa (SSD MAT/09), con abilitazione Scientifica Nazionale a Professore Ordinario.
Associato Esterno di Ricerca presso l’Istituto di Studi sui Sistemi Intelligenti per l’Automazione (ISSIA) del CNR.
Guest Scholar presso l’Institute for Advances Studies (IMT) di Lucca.
1997 - 2000: vari periodi di perfezionamento presso l’Institute of Computer Science della Czech Academy of Sciences.
1996: conseguimento del titolo di Dottore di Ricerca in Ingegneria Elettronica e Informatica.
1992: conseguimento dell’idoneità nel concorso per l’ammissione all’VIII Ciclo del Corso di Dottorato di Ricerca in Matematica (Univ. di Torino).
1992: Laurea in Ingegneria Elettronica presso l’Univ. di Genova con punti 110/110, lode e dignità di stampa.
1986: Diploma di Maturità Scientifica presso il Liceo Scientifico Statale G. Marconi di Chiavari (Genova), con votazione 60/60.
TEMATICHE DI RICERCA
-Apprendimento computazionale neurale: complessità e trattabilità.
-Approssimazione non-lineare mediante modelli connessionistici.
-Metodologie per la risoluzione di problemi di ottimizzazione a dimensione infinita e stime dell’accuratezza di soluzioni subottime.
-Algoritmi di programmazione non-lineare per l’ottimizzazione di reti approssimanti e reti neurali.
-Ottimizzazione nell’apprendimento da dati con metodi kernel e reti neurali.
-Apprendimento da dati con vincoli.
-Modelli di knapsack stocastico generalizzato.
-Ottimizzazione a stadi e programmazione dinamica.
-Ottimizzazione a squadra.
-Ricoprimento e approssimazione di gusci convessi.
COORDINAMENTO DI PROGETTI INTERNAZIONALI
- 2000-2003: Coordinatore e “Principal Investigator” del NATO Collaborative Linkage Grant fra Italia e Rep. Ceca, progetto “Approximation and functional optimization by neural networks”, finanziato dalla NATO (Science Programme – Cooperative Science & Technology Sub–Programme).
- 2002-2004: Coordinatore italiano del progetto di collaborazione scientifica triennale fra Italia e Rep. Ceca “Functional optimization and nonlinear approximation by neural networks” (Area MC6 – Mathematics and Information. Technology and Computer Science), finanziato dai Ministeri degli Esteri della Rep. Italiana e della Rep. Ceca.
- 2004-2006: Coordinatore italiano del progetto di scambio scientifico triennale “Learning from data by neural networks and kernel methods: An approach based on approximate optimization”, tra Univ. di Genova, CNR e Accademia delle Scienze della Rep. Ceca (visite scientifiche reciproche, mobilità di studenti di dottorato e scambio di competenze scientifiche e tecnologiche).
- 2007-2009: Coordinatore italiano del rinnovo triennale del progetto di cui al punto precedente.
- 2010-2012: Coordinatore italiano del progetto di scambio scientifico triennale “Complexity of neural-network and kernel computational models”, tra Univ. di Genova, CNR e Accademia delle Scienze della Rep. Ceca (visite scientifiche reciproche, mobilità di studenti di dottorato e scambio di competenze scientifiche e tecnologiche).
-Dal 2010: membro COST (European Cooperation in Science and Technology) - IntelliCIS Action.
PARTECIPAZIONE A PROGETTI INTERNAZIONALI
EU Projects H2020
– ICT WhoLoDance (H2020-ICT-2015): “Whole-Body Interaction Learning for Dance Education”. Sviluppo di modelli computazionali e algoritmi per l’analisi real time del comportamento umano sulla base di segnali multimodali non-verbali.
– ICT DANCE (IA, 2015-2017). Analisi di come qualità emozionali e relazionali del movimento del corpo umano possano essere espresse e rappresentate mediante il canale uditivo.
EU Projects, 7th Framework Program
–ICT FET SIEMPRE (STREP, 2010-2012): “Social Interaction and Entrainment using Music PeRformancE”. Modelli computazionali e algoritmi innovativi per l’analisi della comunicazione creativa e dell’interazione fra gruppi di individui.
Altri
-2002: “Improving the performance of neural networks”, fra Georgetown Univ., Washington D.C., USA (Dept. of Mathematics) e Univ. di Genova (DIST).
-2001: membro della campagna di ricerche Baratti 2001, attivata su richiesta del Ministero dei Beni Culturali per l’acquisizione ed elaborazione di dati archeologici (Sovrintendenza Archeologica di Firenze, Accademia Americana di Roma, Massachusetts Inst. of Technology, Boston, USA, ISME-Interuniversity Centre of Systems for the Marine Environment).
COLLABORAZIONI SCIENTIFICHE CON GRUPPI DI RICERCA STRANIERI
- Inst. of Computer Science, Academy of Sciences of the Czech Republic (Praga): Vera Kurkova.
- Dept. of Mathematics, Georgetown Univ. (Washington DC - USA): Paul C. Kainen e Andrew Vogt.
- Dept. of Industrial & Manufacturing Systems Engineering, Univ. of Texas at Arlington (USA): Victoria C. P. Chen.
- Commission for Scientific Visualization, Austrian Academy of Sciences (Vienna): Katerina Hlavackova-Schindler.
- Dept. of Management, Bar-Ilan Univ. (USA): Konstantin Kogan e Yuval Hadas.
COORDINAMENTO DI PROGETI NAZIONALI E FINANZIAMENTI PER VISITING PROFESSOR
- 2013: coordinatore del progetto “Metodologie di risoluzione approssimata per problemi di ottimizzazione a squadra e di interazione strategica, finanziato dallo GNAMPA (Gruppo Nazionale per l’Analisi Matematica, la Probabilità e le loro Applicazioni) dell’INdAM (Istituto Nazionale di Alta Matematica).
- 2015: finanziamento da parte dello GNAMPA di un visiting professor (K. Kogan, Bar-Ilan University - Tel Aviv, Israele).
• 2014: finanziamento da parte dello GNAMPA di un visiting professor (V. Kurkova, Academy of Sciences of the Czech Republic).
PARTECIPAZIONE A PROGETTI NAZIONALI
– Dal 1995 al 200: programmi di Programma di ricerca MURST 40 % poi PRIN
– 2002: Progetto strategico MIUR SP7 “Simulazione e Ottimizzazione per Reti: Software e Applicazioni (SORSA)”. Corrispondente per le Unità Operative 25 (linea di ricerca: Progettazione di reti con vincoli di capacità) e 26 (linea di ricerca: Analisi e simulazione del traffico).
– 2000-02: Progetto Coordinato CNR-Agenzia 2000 “Nuovi algoritmi e metodologie per la risoluzione approssimata di problemi non lineari di ottimizzazione funzionale in ambiente stocastico”.
– 2001-02: Progetto di Ricerca ASI (Agenzia Spaziale Italiana) “TEMA: Team-Based Exploration by Mobile Agents”.
– Progetto FIRB 2001 “Algoritmi e modelli dell’ingegneria del traffico per l’ottimizzazione di reti IP di nuova concezione”.
– 1996: Contratto di Ricerca dell’Agenzia Spaziale Italiana ASI-ARS-96-111: “Modellizzazione simulazione e sintesi ottima delle manovre di berthing in ambito spaziale”.
PROGETTI LOCALI
- 2008: responsabile del Progetto Ricerca di Ateneo “Risoluzione di problemi di ottimizzazione funzionale mediante approssimatori non-lineari e tecniche di apprendimento da dati”.
- 1996-2014: membro di vari Progetti di Ricerca di Ateneo.
- 1999: progetto fra DIST, Comune di Arenzano (Genova) e Museo Vivo delle Tecnologie per l’Ambiente (Arenzano) per la realizzazione di un sistema basato su un veicolo sottomarino filoguidato per la ricerca di reperti archeologici sommersi.
TRASFERIMENTO IN AMBITO TECNOLOGICO DI COMPETENZE SCIENTIFICHE
- 2014: membro del gruppo dei proponenti e dei referenti DIBRIS-Univ. di Genova per la Convenzione Quadro stipulata fra Qui! Group S.p.A, Ianuatech e DIBRIS-Univ. di Genova.
- 2001-2004: trasferimento di competenze scientifiche al Polo Tecnologico Sud (Genova) per la realizzazione di un impianto prototipale di maricoltura off shore.
- 2001: contratto di ricerca fra DIST e con X-Istituto di Calcolo Scientifico (Genova) per lo sviluppo di algoritmi di programmazione non lineare per la minimizzazione di funzioni di costo multivariabili e multimodali nell’ottimizzazione di processi chimici.
- 1999-2001: trasferimento di competenze scientifiche alle ditte Ecotec e Telema (Genova), per lo sviluppo sperimentale di tecniche di controllo basate su ottimizzazione mediante approssimatori non lineari.
ATTIVITA' EDITORIALE
Membro degli Editorial Boards delle riviste internazionali:
- IEEE Transactions on Neural Networks, poi IEEE Transactions on Neural Networks and Learning Systems (fino al 2012)
- Neurocomputing
- Neural Processing Letters
- Mathematics in Engineering, Science and Aerospace
COMITATI DI PROGRAMMA E COMITATI ORGANIZZATORI
Membro degli Int. Organizing Committees di:
-World Congress 2012 on Mathematical Problems in Engineering, Aerospace, and Sciences - IX ICNPAAConf. (Vienna, Austria, 11-14 luglio 2012)
-World Congress 2014 on Mathematical Problems in Engineering, Aerospace, and Sciences - X ICNPAA
Conf. (Narvik, Norvegia, 15-18 luglio 2014)
Membro dei Program Committees di:
- Int. Conf. on Non-Linear Problems in Aviation and Aerospace (2000, 2002)
- Int. Conf. on Artificial Neural Networks and Genetic Algorithms (2001, 2003)
- Int. Conf. on Adaptive and Natural Computing Algorithms (2005, 2007)
- Int. Conf. on Artificial Neural Networks (2008, 2009, 2010, 2014)
- Int. Conf. on Informatics in Control, Automation and Robotics (2010, 2011, 2012, 2013, 2014, 2015, 2016)
- Int. Joint Conf. on Neural Networks (2011)
- Int. Symposium on Neural Networks (2011, 2012)
- Int. Conf. on Engineering Applications of Neural Networks (2011, 2012, 2013, 2014, 2015, 2016)
- Int. Joint Conf. on Neural Networks (2011, 2013, 2014)
- Int. Conf. on Operations Research and Enterprise Systems (2012, 2013, 2014, 2015, 2016, 2017)
- Int. Conf. on Knowledge Based and Intelligent Information & Engineering Systems (2012, 2013, 2014)
- Int. Conf. on Intelligent Decision Technologies (2015)
- Int. Conf. on Knowledge-Based and Intelligent Information & Engineering Systems (2016)
- Int. Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines: Theory and Applications (2013)
- Int. Conf. on Artificial Intelligence Applications and Innovations (2016)
Session chair a:
- Int. Symp. on Mathematical Theory of Networks and Systems (2000)
- Int. Conf. on Non-Linear Problems in Aviation and Aerospace (2000)
- European Control Conf. (2001)
- AIRO Conf. (2003, 2005, 2006, 2007, 2012, 2015)
- Applied Mathematical Programming and Modelling (2006)
- Mathematical Problems in Engineering, Aerospace, and Sciences (2006, 2008)
- Cologne-Twente Workshop (2011)
Organizzatore di invited session alle conferenze:
– APMOD 2002
– EURO 2003, 2006, 2007
– AIRO 2003, 2005, 2006, 2007, 2008
– AIRO Winter 2011
ATTIVITA' IN SCUOLE DI DOTTORATO
-2004-2012: membro Advisory Board Scuola di Dottorato presso l'Università di Genova “Sciences & Technologies for the Society of Information”, poi “Sciences & Technologies for Information and Knowledge”.
-2013-2014: membro Collegio dei Docenti Corso di Dottorato Ing. Sistemi (XXIX Ciclo; curriculum in Analisi e Ottimizzazione di Sistemi Complessi).
-Dal 2014: membro Collegio dei Docenti del Dottorato Informatica e Ing. dei Sistemi.
-Dal 2014: Coordinatore Commissione Offerta Formativa del Dottorato Informatica e Ing. dei Sistemi.
ATTIVITA' DI “LECTURER”
-1997-attualmente: vari interventi come “lecturer'' all'Institute of Computer Science (ICS) dell'Accademia delle Scienze della Rep. Ceca
-2000-attualmente: “invited lecturer'' a scuole, seminari e conferenze in Europa e USA
-2012: invito a tenere un talk al World 2012 Congress on Mathematical Problems in Engineering, Aerospace, and Sciences (Vienna)
-2013: invited talk al Dept. of Management della Bar-lian-University (Tel Aviv, Israele).
-2014: invito come relatore al seminario su apprendimento da dati di Dagstuhl
-2014: invited speaker al Workshop on Optimization, Game Theory and Related Topics, Univ. di Genova
-2015: invited talk al IV Workshop Machine Learning e Data Mining (MDML), XVI Conf. dell’Associazione Italiana per l’Intelligenza Artificiale (Ferrara)
VALUTATORE DI PROGETTI
- Project referee per Czech Academy of Sciences (AVCR)
- Project referee per Georgia National Science Foundation (GNSF)
- Rapporteur di progetti "SIR 2014" del MIUR
- Valutatore di progetti "SIR 2014" del MIUR
- Membro dell’Albo dei RevisoriMIUR per la valutazione dei programmi e prodotti di ricerca ministeriale (PE1 16, PE1 18, PE1 19)
ORGANIZZAZIONE DI SEMINARI INTERNAZIONALI
1999-2016: organizzazione di seminari internazionali presso l’Università di Genova. Fra i relatori:
- V. Kurkova, Czech Academy of Sciences of teh Czech Republic
- P.C. Kinen, Georgetown University, Washington, DC
- K, Kogan, Bar-Ilan University, Israele
- Y. Hadash, Bar-Ilan University, Israele
ATTIVITA' DI REVISORE
- Revisore di libri e articoli su rivista per Mathematical Reviews
- "Postodoctoral Positions Referee" per la Research Foundation - Flanders (Belgio) (FWO)
- Revisore per Mathematical Reviews
- Revisore per le riviste internazionali: SIAM J. on Control and Optimization, Operations Research, SIAM J. on Optimization, Discrete Applied Mathematics, Annals of Operations Research, European J. of Operational Research, Computational Management Science, Computers and Operations Research, Maritime Economics & Logistics, J. of Industrial and Management Optimization, Mathematical Communications, Hacettepe Journal of Mathematics and Statistics, Mathematical Methods in the Applied Sciences, Information Sciences, Applied Mathematics and Computation, IEEE Trans. on Information Theory, IEEE Trans. On Automatic Control, IEEE Trans. on Neural Networks, IEEE Trans. on Biomedical Engineering, IEEE Trans. on NanoBiosciences, IEEE Trans. on Circuits and Systems II, IEEE Trans. on Circuits and Systems for Video Technology, IEE Proc. on Control Theory and Applications, Computers in Industry, Neurocomputing, Neural Networks World, Neural Computing and Applications, Control and Intelligent Systems, Int. J. of Control, Int. J. of Robotics Research, Int. J. of Modelling, Identification, and Control, The Open Software Engineering J., Int. J. of Information Technology & Decision Making, Chemical Engineering Science, Linear Algebra and Applications, Abstract and Applied Analysis
- Revisore per l'Edited Book: Handbook of Neural Information Processing
- Revisore per le conferenze internazionali: Conf. on Decision and Control, Int. Conf. on Adaptive and Natural Computing Algorithms, Int. Conf. on Artificial Neural Networks, Int. Conf. on Artificial Neural Networks and Genetic Algorithms, American Control Conf., IEEE Int. Symp. on Circuits and Systems, German Conf. on Artificial Intelligence, Int. Conf. on Informatics in Control, Automation and Robotics, Int. Conf. on Informatics in Control, Automation and Robotics, Int. Conf. on Engineering Applications of Neural Networks
Pubblicazioni
LIBRI INTERNAZIONALI
[L1] R. Zoppoli, M. Sanguineti, T. Parisini, “Neural Approximations for Optimal Control and Decision”, in fase di completamento. Contratto con Springer (London), per la collana “Control and Communications Systems Series”.
RIVISTE INTERNAZIONALI
[R1] G. Gnecco, R. Morisi, G. Roth, M. Sanguineti, A.C. Taramasso, “Supervised and Semi-Supervised
Classifiers for the Detection of Flood-Prone Areas”. Soft Computing, to appear (doi: 10.1007/s00500-
015-1983-z).
[R2] G. Gnecco, M. Sanguineti, “Neural Approximations of the Solutions to a Class of Stochastic Optimal Control Problems”. J. of NeuroTechnology, to appear.
[R3] V. Kůrková, M. Sanguineti, “Model Complexities of Shallow Networks Representing Highly-Varying Functions”. Neurocomputing, vol. 171, pp. 598-604, 2016.
[R4] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning with Mixed Hard/Soft Pointwise Constraints”. IEEE Transactions on Neural Networks and Learning Systems, vol. 26, pp. 2019-2032,
2015.
[R5] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Narrowing the Search for Optimal Call-
Admission Policies via a Nonlinear Stochastic Knapsack Model”, Journal of Optimization Theory
and Applications, vol. 164, pp. 819-841, 2015.
[R6] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Foundations of Support Constraint Machines”.
Neural Computation, vol. 27, pp. 388-480, 2015.
[R7] M. Gaggero, G. Gnecco, M. Sanguineti “Approximate Dynamic Programming for Stochastic N-
Stage Optimization with Application to Optimal Consumption Under Uncertainty”. Computational
Optimization and Applications, vol. 58, pp. 31-85, 2014.
[R8] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Evaluation of the Average Packet Delivery
Delay in Highly-Disrupted Networks: The DTN and IP-like Protocol Cases”, IEEE Communications
Letters, vol. 18, pp. 519-522, 2014.
[R9] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “A Theoretical Framework for Supervised Learning
from Regions”. Neurocomputing, vol. 129, pp. 25-32, 2014.
[R10] G. Gnecco, D, Glowinski, A. Camurri, M. Sanguineti, “On the Detection of the Level of Attention in an Orchestra Through Head Movements”. International Journal of Arts and Technology, vol. 7, pp.
316-338, 2014.
[R11] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Optimality Conditions for Coordinate-Convex
Policies in CAC with Nonlinear Feasibility Boundaries”, IEEE/ACM Transactions on Networking,
vol. 21, pp. 1363-1377, 2013.
[R12] M. Gaggero, G. Gnecco, M. Sanguineti, “Dynamic Programming and Value-Function Approximation in Sequential Decision Problems: Error Analysis and Numerical Results”. Journal of Optimization Theory and Applications, vol. 156, pp. 380-416, 2013.
[R13] M . Degiorgis, G. Gnecco, S. Gorni, G. Roth, M. Sanguineti, A.C. Taramasso, “Flood Hazard Assessment via Threshold Binary Classifiers: Case Study of the Tanaro Basin”, Irrigation and Drainage,
vol. 62, pp. 1-10, 2013.
[R14] G. Gnecco, M. Gori, M. Sanguineti, “Learning with Boundary Conditions”, Neural Computation,
vol. 25, pp. 1029-1106, 2012.
[R15] G. Gnecco, M. Sanguineti, “New Insights into Witsenhausen’s Counterexample”, Optimization Letters, vol. 6, pp. 1425-1446, 2012.
[R16] M . Degiorgis, G. Gnecco, S. Gorni, G. Roth, M. Sanguineti, A.C. Taramasso, “Classifiers for the
Detection of Flood Prone Areas Using Remote Sensed Elevation Data”, Journal of Hydrology, vol.
470-471, pp. 302-315, 2012.
[R17] G. Gnecco, M. Sanguineti, M. Gaggero, “Suboptimal Solutions to Team Optimization Problems with Stochastic Information Structure”. SIAM Journal on Optimization, vol. 22, pp. 212-243, 2012.
[R18] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “A Model of Buffer Occupancy for ICNs”, IEEE
Communications Letters, vol. 16, pp. 862-865, 2012.
[R19] P . C. Kainen, V. Kůrková, M. Sanguineti, “Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks”. IEEE Transactions on Information
Theory, vol. 58, pp. 1203-1214, 2012.
[R20] G. Gnecco, V. Kůrková, M. Sanguineti, “Accuracy of Approximations of Solutions to Fredholm
Equations by Kernel Methods”, Applied Mathematics and Computation, vol. 218. pp. 7481-7497,
2012.
[R21] G. Gnecco, V. Kůrková, M. Sanguineti, “Can Dictionary-Based Computational Models Outperform the Best Linear Ones?”, Neural Networks, vol. 24, pp. 881887, 2011.
[R22] M. Cello, G. Gnecco, M. Marchese, and M. Sanguineti, “CAC with Nonlinearly-Constrained
Feasibility Regions”, IEEE Communications Letters, vol. 15, pp. 467-469, 2011.
[R23] G. Gnecco, M. Sanguineti, “On a Variational Norm Tailored to Variable-Basis Approximation
Schemes”, IEEE Transactions on Information Theory, vol. 57, pp. 549-558, 2011.
[R24] G. Gnecco, M. Sanguineti, “Team Optimization Problems with Lipschitz Continuous Strategies”,
Optimization Letters, vol. 5, pp. 333-346, 2011.
[R25] G. Gnecco, V. Kůrková, M. Sanguineti, “Some Comparisons of Complexity in Dictionary-Based and Linear Computational Models”, Neural Networks, vol. 24, pp. 171-182, 2011.
[R26] G. Gnecco, M. Sanguineti, “Suboptimal Solutions to Dynamic Optimization Problems via Approximations of the Policy Functions”, Journal of Optimization Theory and Applications, vol. 146, pp. 764-794, 2010.
[R27] A. Alessandri, C. Cervellera, D. Macciò, M. Sanguineti, “Optimization Based on Quasi-Monte Carlo Sampling to Design State Estimators for Nonlinear Systems”. Optimization, vol. 59, pp. 963-984,
2010.
[R28] G. Gnecco, M. Sanguineti, “On Spectral Windows in Supervised Learning From Data”, Information Processing Letters, vol. 110, pp. 1031-1036, 2010.
[R29] A. Alessandri, G. Gnecco, M. Sanguineti, “Minimizing Sequences for a Family of Functional Optimal Estimation Problems”, Journal of Optimization Theory and Applications, vol. 147, pp. 243-262, 2010.
[R30] G. Gnecco, M. Sanguineti, “Information Complexity of Infinite-Dimensional Optimization Problems and their Approximation Schemes”. Mathematics in Engineering, Science and Aerospace, vol. 1, pp. 303-317, 2010.
[R31] G. Gnecco, M. Sanguineti, “Editorial for the Special Issue: Mathematical Problems in Engineering, Aerospace, and Sciences”. Applied Mathematical Sciences, vol. 4, n. 73, pp. 3621-3624, 2010.
[R32] G. Gnecco, M. Sanguineti, “Error Bounds for Suboptimal Solutions to Kernel Principal Component Analysis”, Optimization Letters, vol. 4, pp. 197-210, 2010.
[R33] G. Gnecco, M. Sanguineti, “Regularization Techniques and Suboptimal Solutions to Optimization
Problems in Learning from Data”, Neural Computation, vol. 22, pp. 793-829, 2010.
[R34] G. Gnecco, M. Sanguineti, “Estimates of Variation with Respect to a Set and Applications to
Optimization Problems”, Journal of Optimization Theory and Applications, vol. 145, pp. 53-75, 2010.
[R35] E. Messina, M. Sanguineti, “Editorial for the Special Issue: Operations Research and Data Mining
in Biological Systems”. Computers and Operations Research, vol. 37, pp. 1359-1360, 2010.
[R36] M. Baglietto, C. Cervellera, M. Sanguineti, R. Zoppoli, “Management of Water Resources Systems in the Presence of Uncertainties by Nonlinear Approximators and Deterministic Sampling Techniques”,
Computational Optimization and Applications, vol. 47, pp. 349-376, 2010.
[R37] S. Giulini, M. Sanguineti, “Approximation Schemes for Functional Optimization Problems”. Journal of Optimization Theory and Applications, vol. 140, pp. 33-54, 2009.
[R38] M. Baglietto, M. Sanguineti, R. Zoppoli, “The Extended Ritz Method for Functional Optimization:
Overview and Applications to Single-Person and Team Optimal Decision Problems”. Optimization
Methods and Software, vol. 24, pp. 15-43, 2009.
[R39] P. C. Kainen, V. Kůrková, M. Sanguineti, “Complexity of Gaussian Radial-Basis Networks
Approximating Smooth Functions”. Journal of Complexity, vol. 25, pp. 63-74, 2009.
[R40] G. Gnecco, M. Sanguineti, “The Weight-Decay Technique in Learning from Data: An Optimization Point of View”, Computational Management Science, vol. 6, pp. 53-79, 2009.
[R41] E. Kundakcioglu, M. Sanguineti, T. Trafalis, “Editorial for the Special Issue: Optimization in
Learning from Data”. Computational Management Science, vol. 6, pp. 1-3, 2009.
[R42] A. Alessandri, M. Cuneo, M. Sanguineti, “Optimization of Connectionistic Models with Exponentially Bounded Error”, International Journal of Computational Intelligence in Control, vol. 1, pp. 113-122, 2009.
[R43] G. Gnecco, M. Sanguineti, “Accuracy of Suboptimal Solutions to Kernel Principal Component
Analysis”. Computational Optimization and Applications, vol. 42, pp. 265-287, 2009.
[R44] V. Kůrková, M. Sanguineti, “Geometric Upper Bounds on Rates of Variable-Basis Approximation”. IEEE Transactions on Information Theory, vol. 54, pp. 5681-5688, 2008.
[R45] V. Kůrková, M. Sanguineti, “Approximate Minimization of the Regularized Expected Error Over
Kernel Models”. Mathematics of Operations Research, vol. 33, pp. 747-756, 2008.
[R46] G. Gnecco, M. Sanguineti, “Value and Policy Function Approximations in Infinite-Horizon Optimization Problems”, Journal of Dynamical Systems and Geometric Theories, vol. 6, pp. 123-147,
2008.
[R47] M. Sanguineti, “Universal Approximation by Ridge Computational Models: A Survey”. The Open
Applied Mathematics Journal, vol. 2, pp. 31-58, 2008.
[R48] G. Gnecco, M. Sanguineti, “Estimates of the Approximation Error via Rademacher Complexity:
Learning Vector-Valued Functions”. Journal of Inequalities and Applications, vol. 2008, article ID
640758, 16 pages, 2008.
[R49] A. Alessandri, G. Gnecco, M. Sanguineti, “Computationally Efficient Approximation Schemes for
Functional Optimization”, International Journal of Computer Research, vol. 17, pp. 153-189, 2008.
[R50] A. Alessandri, M. Sanguineti, “Connections Between Lp Stability and Asymptotic Stability of
Nonlinear Switched Systems”. Nonlinear Analysis: Hybrid Systems, vol. 1, pp. 501-509, 2007.
[R51] A. Alessandri, C. Cervellera, M. Sanguineti, “Design of Asymptotic Estimators: An Approach Based on Neural Networks and Nonlinear Programming”. IEEE Transactions on Neural Networks, vol. 18, pp. 86-96, 2007.
[R52] A. Alessandri, C. Cervellera, M. Sanguineti, “Functional Optimal Estimation Problems and Their
Approximate Solution”. Journal of Optimization Theory and Applications, vol. 134, pp. 445-466, 2007.
[R53] V. Kůrková, M. Sanguineti “Estimates of Covering Numbers of Convex Sets with Slowly Decaying Orthogonal Subsets”. Discrete Applied Mathematics, vol. 155, pp. 1930-1942, 2007.
[R54] A. Alessandri, M. Cuneo, S. Pagnan, M. Sanguineti, “A Recursive Algorithm for Nonlinear Least-
Squares Problems”. Computational Optimization and Applications, vol. 38, pp. 195-216, 2007.
[R55] A. Alessandri, M. Sanguineti, “Optimization of Approximating Networks for Optimal Fault
Diagnosis”. Optimization Methods and Software, vol. 20, pp. 241-266, 2005.
[R56] V. Kůrková, M. Sanguineti, “Error Estimates for Approximate Optimization by the Extended Ritz
Method”. SIAM Journal on Optimization, vol. 15, pp. 461-487, 2005.
[R57] V. Kůrková,M. Sanguineti, “Learning with Generalization Capability by Kernel Methods of Bounded Complexity”. Journal of Complexity, vol. 21, pp. 350-367, 2005.
[R58] P . C. Kainen, V. Kůrková, M. Sanguineti, “Rates of Approximate Minimization of Error Functionals over Boolean Variable-Basis Functions”. Journal of Mathematical Modelling and Algorithms, vol. 4, pp. 355-368, 2005.
[R59] K . Hlav´aˇckov´a-Schindler, M. Sanguineti, “Bounds on the Complexity of Neural-Network Models and Comparison with Linear Methods”. International Journal of Adaptive Control and Signal Processing, vol. 17, pp. 179-194, 2003.
[R60] P . C. Kainen, V. Kůrková, M. Sanguineti, “Minimization of Error Functionals Over Variable-Basis
Functions”. SIAM Journal on Optimization, vol. 14, pp. 732-742, 2003.
[R61] V. Kůrková, M. Sanguineti, “Comparison of Worst-Case Errors in Linear and Neural-Network
Approximation”. IEEE Transactions on Information Theory, vol. 48, pp. 264-275, 2002.
[R62] R. Zoppoli, M. Sanguineti, T. Parisini, “Approximating Networks and Extended Ritz Method for theSolution of Functional Optimization Problems”. Journal of Optimization Theory and Applications,
vol. 112, pp. 403-440, 2002.
[R63] A. Alessandri, M. Sanguineti, M. Maggiore, “Optimization-Based Learning with Bounded Error for Feedforward Neural Networks”. IEEE Transactions on Neural Networks, vol. 13, pp. 261-273, 2002.
[R64] V. Kůrková, M. Sanguineti, “Bounds on Rates of Variable-Basis and Neural-Network
Approximation”. IEEE Transactions on Information Theory, vol. 47, pp. 2659-2665, 2001.
[R65] T. Parisini, M. Sanguineti, R. Zoppoli, “Nonlinear Stabilization by Receding-Horizon Neural
Regulators”. International Journal of Control, vol. 70, pp. 341-362, 1998.
CAPITOLI DI LIBRI E CONTRIBUTI IN COLLANE SCIENTIFICHE
[CL1] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning as Constraint Reactions”, Artificial Neural Networks: Methods and Applications. P. Koprinkova-Hristova, V. Mladenov, and N. Kasabov, Eds. Springer Series in Bio/Neuroinformatics. Springer, pp. 245-270, 2015.
[CL2] V. Kůrková, M. Sanguineti, “Complexity of Shallow Networks Representing Functions with Large Variations”. Lecture Notes in Computer Science (Proc. di [AC3]), vol. 8681, , pp. 331-338. Springer, Switzerland, 2014.
[CL3] A. Camurri, F. Dardard, S. Ghisio, D. Glowinski, G. Gnecco, M. Sanguineti, “Exploiting the Shapley
Value in the Estimation of the Position of a Point of Interest for a Group of Individuals”, Procedia -
Social and Behavioral Sciences, vol. 108, pp. 249-259, 2014.
[CL4] M. Gaggero, G. Gnecco, M. Sanguineti, “Suboptimal Policies for Stochastic N-Stage Optimization Problems: Accuracy Analysis and a Case Study from Optimal Consumption”, Models and Methods in Economics and Management, F. El Ouardighi and K. Kogan, Eds. International Series in Operations Research and Management Science, vol. 198. Springer, 2014, pp. 27-50.
[CL5] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning with Hard Constraints”, Lecture Notes in
Computer Science (Proc. di [AC7]), vol. 8131, pp. 146-153, Springer, 2013.
[CL6] V. Kůrková, M. Sanguineti, “Can Two Hidden Layers Make a Difference?”, Lecture Notes in
Computer Science (Proc. di [AC6]), vol. 7824, pp. 30–39. Springer, Berlin Heidelberg, 2013.
[CL7] P. C. Kainen, V. Kůrková, M. Sanguineti, “Approximating Multivariable Functions by Feedforward Neural Nets”, in Handbook of Neural Information Processing, M. Bianchini, M. Maggini, F. Scarselli, and L. Jain, Eds. Springer, Berlin Heidelberg 2013, Chapter 5, pp. 143-181.
[CL8] G. Gnecco, L. Badino, A. Camurri, A. D’Ausilio, L. Fadiga, M. Sanguineti, G. Varni, G. Volpe,
“Towards Automated Analysis of Joint Music Performance in the Orchestra”, Lecture Notes of the
Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, vol. 116,
G. De Michelis et Al., Eds., pp. 120-127 (Proc. di [AC5]), Springer, Berlin Heidelberg, 2013.
[CL9] G. Gnecco, V. Kůrková, M. Sanguineti, “Bounds for Approximate Solutions of Fredholm Integral
Equations using Kernel Networks”, Lecture Notes in Computer Science (Proc. di [AC9]), vol. 6791,
pp. 126–133. Springer, Heidelberg, 2011.
[CL10] G. Gnecco, V. Kůrková, M. Sanguineti, “Some Comparisons of Model Complexity in Linear and
Neural-Network Approximation”, Lecture Notes in Computer Science (Proc. di [AC13]), vol. 6354,
pp. 358-367. Springer, Berlin Heidelberg, 2010.
[CL11] G. Gnecco, M. Sanguineti, “Smooth Optimal Decision Strategies for Static Team Optimization
Problems and their Approximations”, Lecture Notes in Computer Science (Proc. di [AC12]), vol.
5901, pp. 440-451. Springer, Berlin Heidelberg, 2010.
[CL12] A. Alessandri, G. Gnecco, M. Sanguineti, “Computationally Efficient Approximation Schemes for
Functional Optimization”. In Computational Optimization: New Research Developments, R. F. Linton
and T. B. Carroll Jr., Eds. Nova Science Publishers, pp. 169-205, 2010.
[CL13] P. C. Kainen, V. Kůrková, M. Sanguineti, “On Tractability of Neural-Network Approximation”,
Lecture Notes in Computer Science (Proc. di [AC15]), vol. 5495, pp. 11-21. Springer, Berlin Heidelberg,
2009.
[CL14] G. Gnecco, M. Sanguineti, “Regularization and Suboptimal Solutions in Learning from Data”. In
Innovations in Neural Information Paradigms and Applications (Series “Studies in Computational
Intelligence”, vol. 247/2009), M. Bianchini, M. Maggini, F. Scarselli, Eds., pp. 113-154 Springer,
2009.
[CL15] A. Alessandri, M. Cuneo, M. Sanguineti, “An Algorithm for Nonlinear Least-Squares: Exponential Boundedness and Numerical Results”. In Mathematical Problems in Engineering and Aerospace Sciences, S. Sivasundaram, Ed., pp. 319-329 (Chapter 22). Cambridge Scientific Publishers, Cambridge, UK, 2009.
[CL16] V. Kůrková, M. Sanguineti, “Geometric Rates of Approximation by Neural Networks”, Lecture
Notes in Computer Science (Proc. di [AC18]), vol. 4910, pp. 541-550. Springer, Berlin Heidelberg,
2008.
[CL17] P. C. Kainen, V. Kůrková, M. Sanguineti, “Estimates of Approximation Rates by Gaussian Radial-Basis Functions”, Lecture Notes in Computer Science (Proc. di [AC19]), vol. 4432, pp. 11-18. Springer, Berlin-Heidelberg, 2007.
[CL18] M. Baglietto, C. Cervellera, M. Sanguineti, R. Zoppoli, “Water Reservoirs Management Under
Uncertainty by Approximating Networks and Learning from Data”. Chapter 6 in Topics on System
Analysis and Integrated Water Resource Management (A. Castelletti and R. Soncini-Sessa, Eds.), pp.
117-139. Elsevier, 2006.
[CL19] M. Sanguineti, R. Zoppoli, “Le Reti Neurali e le Altre Reti Approssimanti nei Problemi di Ottimizzazione Funzionale”. In Modelli e Algoritmi per l’Ottimizzazione di Sistemi Complessi - Atti della Scuola CIRO 2002 (A. Agnetis and G. Di Pillo, Eds.), pp. 335-392. Pitagora Editrice, 2004.
[CL20] M. Baglietto, M. Sanguineti, R. Zoppoli, “The Extended Ritz Method in Stochastic Functional Optimization: An Example of Dynamic Routing in Traffic Networks”. In High Performance Algorithms
and Software for Nonlinear Optimization (G. Di Pillo e A. Murli, Eds.), pp. 23-56. Kluwer Academic
Publishers, 2003.
[CL21] V. Kůrková, M. Sanguineti, “Tight Bounds on Rates of Neural-Network Approximation”. Lecture Notes in Computer Science (Proc. di [AC32]), vol. 2130, pp. 277-282. Springer, 2001.
RECENSIONI DI LIBRI
[RL1] M. Sanguineti, “Review of ‘Support vector machines. Optimization based theory, algorithms, and
extensions.’ - by Naiyang Deng, Yingjie Tian, and Chunhua Zhang, Chapman & Hall/CRC Boca
Raton, FL, 2013, Mathematical Reviews, to appear.
[RL2] M. Sanguineti, “Review of ‘Greedy approximation’ - by Vladimir Temlyakov, Cambridge University Press, Cambridge, 2011, Mathematical Reviews, 2012.
[RL3] M. Sanguineti, “Review of ‘Evolutionary Statistical Procedures. An Evolutionary Computation
Approach to Statistical Procedures Designs and Applications’ - by R. Baragona, F. Battaglia, and I.
Poli, Springer, Heidelberg, 2011 ”, Mathematical Reviews, 2011.
[RL4] M. Sanguineti, “Review of ‘Learning theory: An approximation theory viewpoint ’- by F. Cucker
and D.-X. Zhou, Cambridge University Press, 2007 ”, Mathematical Reviews, 2008.
[RL5] M. Sanguineti, “Review of ‘Dynamic networks and evolutionary variational inequalities. New Dimensions in Networks’- by P. Daniele, Edward Elgar Publishing Limited, Cheltenham, 2006 ”,
Mathematical Reviews, 2008.
RECENSIONI DI ARTICOLI
M. Sanguineti, Mathematical Reviews MR2062913, MR2114380, MR2125823, MR2132029, MR2142499, MR2132029, MR2147059, MR2168882, MR2179289, MR2187418, MR2187881, MR2190676, MR2198503, MR2214065, MR2216500, MR2221152, MR2234924, MR2228737, MR2251577, MR2255915, MR2263006, MR2266608, MR2267329, MR2299424, MR2312313, MR2317190, MR2325760, MR2326506, MR2318715, MR2317808, MR2349428, MR2344664, MR2357573, MR2371992, MR2385835, MR2397161, MR2401192, MR2422203, MR2428976, MR2424165, MR2416789, MR2433285, MR2415826, MR2434101, MR2462570, MR2426053, MR2460286, MR2503313, MR2510835, MR2527756, MR2532464, MR2547230, MR2577685,
MR2534877, MR2554374, MR2558684, MR2579912, MR2594755, MR2600635, MR2677883, MR2740624, MR2724176, MR2672481, MR2727771, MR2747641, MR2818564, MR2825312, MR2922000, MR2947555, MR2933662, MR2979626, MR2956341, MR3020275, MR3042920, MR2909333, MR3039681, MR3085292, MR3254506, MR3273290, MR3339014, MR3352615, MR3360490.
TUTORIAL SU INVITO
[TI1] M. Sanguineti, “Functional Optimization for Operations Research: A Guided Tour PART 1: Motivations and Methodology. PART 2: Case Studies”, 6th AIRO Winter Conf., Cortina d’Ampezzo,
Italy, 7-12 February 2011.
ATTI DI CONFERENZE INTERNAZIONALI, CON PRESENTAZIONI SU INVITO
[ACI1] G. Gnecco, T. Parisini, M. Sanguineti, R. Zoppoli, “Approximation Structures with Moderate
Complexity in Functional Optimization and Dynamic Programming”, 51th Conf. on Decision and
Control (CDC), pp. 1902-1908, 2012.
[ACI2] A. Alessandri, M. Cuneo, M. Sanguineti, “An Algorithm for Nonlinear Least-Squares: Exponential Boundedness and Numerical Results”, 6th Int. Conf. on Non-Linear Problems in Aviation and Aerospace (ICNPAA), Budapest, Hungary, 2006.
[ACI3] M. Sanguineti, “Complexity and Regularization Issues in Kernel Methods”, Invited Symposium
”Learning Theory & Kernel Methods”, 16th Int. Symp. on Mathematical Theory of Networks and
Systems (MTNS), 2004.
[ACI4] R. Zoppoli, M. Sanguineti, T. Parisini, “Can We Cope with the Curse of Dimensionality in Optimal
Control by Using Neural Approximators?”. Articolo Invitato, 40th Conf. on Decision and Control
(CDC), pp. 3540-3545, 2001.
[ACI5] M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, R. Zoppoli, “Neural Approximators, Dynamic
Programming and Stochastic Approximation”. 19th American Control Conf. (ACC), Sessione a Invito
“Approximating Networks, Dynamic Programming, and Stochastic Approximation”, pp. 3304-3308,
2000.
[ACI6] R. Zoppoli, M. Sanguineti, T. Parisini, “Approximating Networks for Functional Optimization
Problems”. Articolo invitato, 3rd Int. Conf. on Non-Linear Problems in Aviation and Aerospace
(ICNPAA), pp. 769-778, European Conference Publications, Cambridge, UK, 2002.
[ACI7] V. Kůrková, M. Sanguineti, “Dimension-Independent Approximation by Neural Networks: How
Can we Cope With the Curse of Dimensionality?”. Articolo invitato, 3rd Int. Conf. on Non-Linear
Problems in Aviation and Aerospace (ICNPAA), pp. 355-364, European Conference Publications,
Cambridge, UK, 2002.
WORKSHOP INTERNAZIONALI, CON PRESENTAZIONI SU INVITO
[WII1] M. Degiorgis, G. Gnecco, S. Gorni, G. Roth, M. Sanguineti, A.C. Taramasso, “Classifiers for the
Detection of Flood Prone Areas from Remote Sensed Elevation Data”, 5th CNR-Princeton Workshop
on Next Frontiers in Hydrology, Miami, Florida, 2012.
ATTI DI CONFERENZE INTERNAZIONALI, CON PROCESSO DI REVISIONE DEL “FULL PAPER”
[AC1] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning with Hard Constraints as a Limit Case of
Learning with Soft Constraints”. 24th European Symposium on Artificial Neural Networks, Compu-
tational Intelligence, and Machine Learning. (ESANN), Bruges, Belgio, 27-29 aprile 2016, accettato
per la presentazione.
[AC2] G. Gnecco, A. Bemporad, M. Gori, R. Morisi, M. Sanguineti, “Online Learning as an LQG Optimal
Control Problem with Random Matrices”. Proc. 14th European Control Conf. (ECC), pp. 2487-2494.
[AC3] V. Kůrková, M. Sanguineti, “Complexity of Shallow Networks Representing Functions with Large
Variations”. Proc. 24rd Int. Conf. on Artificial Neural Networks (ICANN), 2014 (pubblicato come
[CL2]).
[AC4] D . Punta, G. Puri, F. Tollini, G. Gnecco, M. Sanguineti, A. Camurri, “Evaluation of Individual
Contributions in a Group Estimate of the Position of a Moving Point of Common Interest”. Proc. 6th
Int. Conf. of Students of Systematic Musicology (SysMus), 2013, pp. 23-32.
[AC5] G. Gnecco, L. Badino, A. Camurri, A. D’Ausilio, L. Fadiga, D. Glowinski, M. Sanguineti, G. Varni,
G. Volpe “Towards Automated Analysis of Joint Music Performance in the Orchestra”, 3rd Int. Conf.
on Arts and Technology (ArtsIT 2013), Milano, 21-23 marzo 2013 (pubblicato come [CL8]).
[AC6] V. Kůrková, M. Sanguineti, “Can Two Hidden Layers Make a Difference?”. Proc. Int. Conf. on
Adaptive and Natural Computing Algorithms (ICANNGA), 2013 (pubblicato come [CL6]).
[AC7] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning with Hard Constraints”, 23rd Int. Conf.
on Artificial Neural Networks (ICANN), 2013 (pubblicato come [CL5]).
[AC8] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “An Application to Two-Hop Forwarding of a
Model of Buffer Occupancy in ICNs”, Proc. 7th IEEE Int. Conf. on System of Systems Engineering
(SOSE), 2012.
[AC9] G. Gnecco, V. Kůrková, M. Sanguineti, “Bounds for Approximate Solutions of Fredholm Integral
Equations using Kernel Networks”, Proc. 21st Int. Conf. on Artificial Neural Networks (ICANN),
2011 (pubblicato come [CL9]).
[AC10] M. Marchese, M. Cello, G. Gnecco, M. Sanguineti, “Structural Properties of Optimal Coordinate-
Convex Policies for CAC with Nonlinearly-Constrained Feasibility Regions”, Proc. IEEE Int. Mini-
Conf. on Computer Communications (INFOCOM), pp. 466-470, 2011.
[AC11] M. Cello, G. Gnecco, M. Marchese, andM. Sanguineti, “A Generalized Stochastic Knapsack Problem with Application in Call Admission Control”, Proc. 10th Cologne-Twente Workshop (CTW), pp.
105-108, 2011.
[AC12] G. Gnecco, M. Sanguineti, “Smooth Optimal Decision Strategies for Static Team Optimization
Problems and their Approximations”, Proc. 36th Int. Conf. on Current Trends in Theory and Practice
of Computer Science (SOFSEM), 2010 (pubblicato come [CL10]).
[AC13] G. Gnecco, V. Kůrková, M. Sanguineti, “Some Comparisons of Model Complexity in Linear and
Neural-Network Approximation”, Proc. 20th Int. Conf. on Artificial Neural Networks (ICANN), 2010
(pubblicato come [CL10]).
[AC14] G. Gnecco, M. Sanguineti, “Estimates on Weight-Decay Regularization by Variable-Basis Schemes”.
Proc. 9th Int. Conf. on Applied Computer Science (ACS), 2009.
[AC15] P. C. Kainen, V. Kůrková, M. Sanguineti, “On Tractability of Neural-Network Approximation”.
Proc. Int. Conf. on Adaptive and Natural Computing Algorithms, 2009 (pubblicato come [CL13]).
[AC16] G. Gnecco, M. Sanguineti, “Suboptimal Solutions to Network Team Optimization Problems”. CD-Proc. Int. Network Optimization Conf. (INOC), 2009.
[AC17] G. Gnecco, M. Sanguineti. “Lipschitz Continuity of the Solutions to Team Optimization Problems Revisited”. Proc. Int. Conf. on Mathematical Science and Engineering (ICMSE), 2009.
[AC18] V. Kůrková, M. Sanguineti, “Geometric Rates of Approximation by Neural Networks”. Proc.
34th Int. Conf. on Current Trends in Theory and Practice of Computer Science (SOFSEM), 2008
(pubblicato come [CL16]).
[AC19] P. C. Kainen, V. Kůrková, M. Sanguineti, “Estimates of Approximation Rates by Gaussian Radial-
Basis Functions”. Proc. Int. Conf. on Adaptive and Natural Computing Algorithms (ICANNGA), 2007
(pubblicato come [CL17]).
[AC20] A. Alessandri, C. Cervellera, D. Macci´o, M. Sanguineti, “Design of Parametrized State Observers
and Controllers for a Class of Nonlinear Continuous-Time Systems ”. Proc. 45th IEEE Conf. on
Decision and Control (CDC), pp. 5388-5393, 2006.
[AC21] A. Alessandri, C. Cervellera, F. A. Grassia, M. Sanguineti, “On Optimal Estimation Problems for
Nonlinear Systems and Their Approximate Solution,” Proc. 16th IFAC World Congress, 2005.
[AC22] A. Alessandri, C. Cervellera, F. A. Grassia, M. Sanguineti, “An Approximate Solution to Optimal
Lp State Estimation Problems,” Proc. American Control Conf. (ACC), pp. 4204-4209, 2005.
[AC23] M. Baglietto, C. Cervellera, M. Sanguineti, R. Zoppoli, “Water Reservoirs Management in the
Presence of Uncertainties: The Extended Ritz Method Versus Dynamic Programming”. Proc. Int.
Conf. on Reservoir Operation and River Management (ICROM), 2005.
[AC24] A. Alessandri, C. Cervellera, A. F. Grassia, M. Sanguineti, “Design of Observers for Continuous-
Time Nonlinear Systems Using Neural Networks”. Proc. American Control Conf., pp. 2433-2438,
2004.
[AC25] V. Kůrková M. Sanguineti, “Neural Network Learning as Approximate Optimization”. Proc. Int.
Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA), pp. 53-57, 2003.
[AC26] A. Alessandri, G. Cirimele, M. Cuneo, S. Pagnan, M. Sanguineti, “EKF Learning for Feedforward
Neural Networks”. CD-Proc. European Control Conf. (ECC), 2003.
[AC27] V. Kůrková M. Sanguineti, “Learning From Data by Neural Networks with Limited Complexity”.
Proc. 1st IAPR-TC3 Workshop “Artificial Neural Networks in Pattern Recognition”, pp. 146-151,
2003.
[AC28] A. Alessandri, M. Cuneo, S. Pagnan, M. Sanguineti, “On the Convergence of EKF-Based Parameters Optimization for Neural Networks”. Proc. 42nd IEEE Conf. on Decision and Control, pp. 6181-6186, 2003.
[AC29] A. Alessandri, M. Sanguineti, M. Maggiore “Batch-Mode Identification of Black-Box Models Using Feedforward Neural Networks”. Proc. American Control Conf. (ACC), pp. 406-411, 2002.
[AC30] A. Alessandri, M. Sanguineti, M. Maggiore, “Optimized Feedforward Neural Networks for On-Line Identification of Nonlinear Models”. Proc. 41st IEEE Conf. on Decision and Control (CDC), pp.
1751-1756, 2002.
[AC31] V. Kůrková, M. Sanguineti, “Tightness of Upper Bounds on Rates of Neural-Network Approximation”. Proc. 5th Int. Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA), pp. 35–38, 2001.
[AC32] V. Kůrková, M. Sanguineti, “Tight Bounds on Rates of Neural-Network Approximation”, Proc. Int. Conf. on Artificial Neural Networks (ICANN), 2001 (pubblicato come [CL21]).
[AC33] A. Alessandri, M. Sanguineti, “Lp-Stable and Asymptotic Estimators for Nonlinear Dynamic
Systems”. Proc. 20th American Control Conf. (ACC), pp. 1991-1996, 2001.
[AC34] A. Alessandri, M. Sanguineti, “Approximate Solution of Optimal Estimation Problems in Lp
Spaces”. Proc. 5th IFAC Symp. “Nonlinear Control Systems”, pp. 866-871, 2001.
[AC35] A. Alessandri, M. Sanguineti, “Stable Approximate Estimators for a Class of Nonlinear Systems”.
Proc. 1st IFAC Symp. on System Structure and Control, 2001.
[AC36] M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, R. Zoppoli, “Approximating Networks for
the Solution of T -Stage Stochastic Optimal Control Problems”. Proc. IFAC Workshop on Adaptation
and Learning in Control and Signal Processing, pp. 107-114, 2001.
[AC37] A. Alessandri, M. Sanguineti, “W-Stable Estimators for Nonlinear Systems”. Proc. European Control Conf. (ECC), pp. 330-335, 2001.
[AC38] A. Alessandri, M. Sanguineti “On the Convergence of Estimators for a Class of Nonlinear Systems”. Proc. 40th Conf. on Decision and Control (CDC), pp. 3372-3377, 2001.
[AC39] V. Kůrková, M. Sanguineti, “Some Comparisons of the Worst-Case Error in Linear and Neural-
Network Approximation”. Proc. 14th Int. Symp. on Mathematical Theory of Networks and Systems
(MTNS), 2000.
[AC40] S. Giulini, M. Sanguineti, “On Dimension-Independent Approximation by Neural Networks and
Linear Approximators”. In Proc. Neural Computing: New Challenges and Perspectives for the New
Millenium (IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks - IJCNN 2000), Sessione Speciale
“Neural Networks and Geometry”, pp. I283- I288. Los Alamitos, IEEE, 2000.
[AC41] V. Kůrková, M. Sanguineti, “Comparison of Linear and Neural-Network Approximation”. In Proc. Neural Computing: New Challenges and Perspectives for the New Millenium (IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks - IJCNN 2000), Sessione Speciale “Neural Networks and Geometry”, pp. I277- I282. Los Alamitos, IEEE, 2000.
[AC42] A. Alessandri, M. Sanguineti, “On Estimators for Nonlinear Dynamic Systems in Lp Spaces”. Proc. 39th Conf. on Decision and Control (CDC), pp. 298-303, 2000.
[AC43] M. Sanguineti, K. Hlav´aˇckov´a, “Some Comparisons Between Linear Approximation and Approximation by Neural Networks”. Proc. 4th Int. Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA), pp. 172-177, 1999.
[AC44] A. Alessandri, M. Maggiore, M. Sanguineti, “Training Feedforward Neural Networks Through a
Parameter–Estimation–Based Algorithm”. Proc. Conf. on Neural Networks & Their Applications
(NEURAP), pp. 225-228, 1998.
[AC45] A. Alessandri, M. Maggiore,M. Sanguineti, “Parameter–Estimation–Based Learning for Feedforward Neural Networks: Convergence and Robustness Analysis”. Proc. 6th European Symp. on Artificial Neural Networks (ESANN), pp. 285-290, 1998.
[AC46] K. Hlav´aˇckov´a, M. Sanguineti, “On the Rates of Linear and Nonlinear Approximations”. Proc. 3rd IEEE European Workshop on Computer-Intensive Methods in Control and Signal Processing (CMP), pp. 211-216, 1998.
[AC47] A. Alessandri, L. Piccardo, M. Sanguineti, G. S. Villa, “Comparison Between Multilayer Feedforward Nets and Radial Basis Functions to Solve Approximate Nonlinear Estimation Problems”. Proc. Int. Symp. on Nonlinear Theory and its Applications (NOLTA), pp. 105-108, 1998.
[AC48] K. Hlav´aˇckov´a, M. Sanguineti, “Algorithm of Incremental Approximation Using Variation of a Function With Respect to a Subset”. Proc. Int. ICSC/IFAC Symp. on Neural Computation (NC), pp.
896-899, 1998.
ALTRE PRESENTAZIONI A CONFERENZE INTERNAZIONALI
[PCI1] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “A Machine-Learning Paradigm that Includes Pointwise Constraints”, 20th Conference of the International Federation of Operational Research Societies (IFORS) Barcelona, 13-18 Luglio 2014.
[PCI2] M. Sanguineti, M. Cello, G. Gnecco, M. Marchese, “Forwarding Strategies for Congestion Control in Intermittently Connected Networks”, 20th Conference of the International Federation of Operational Research Societies (IFORS) Barcelona, 13-18 Luglio 2014.
[PCI3] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Optimality Conditions for Coordinate-
Convex Policies in Call Admission Control via a Generalized Stochastic Knapsack Model”, 26th
EURO/INFORMS Joint Int. Meeting, Roma, 1-4 Luglio 2013.
[PCI4] G. Gnecco, M. Sanguineti, “Suboptimal Solutions to Team Optimization Problems with Statistical
Information Structure”, 24th European Conf. on Operational Research (EURO 2010), Lisbona,
Portogallo, 11-14 luglio 2010.
[PCI5] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “On Call Admission Control with Nonlinearly
Constrained Feasibility Regions”, 24th European Conf. on Operational Research (EURO 2010),
Lisbona, Portogallo, 11-14 luglio 2010.
[PCI6] G. Gnecco, M. Sanguineti, “Deriving Approximation Error Bounds via Rademacher’s Complexity
and Learning Theory”, 22nd European Conf. on Operational Research (EURO 2007), Praga, Rep.
Ceca, 8-11 luglio 2007.
[PCI7] G. Gnecco, M. Sanguineti, R. Zoppoli, “Exploiting Structural Results in Approximate Dynamic
Programming”, 22nd European Conf. on Operational Research (EURO 2007), Praga, Rep. Ceca, 8-11
luglio 2007.
[PCI8] A. Alessandri, M. Cuneo, M. Sanguineti, “A Least-Squares Algorithm for Nonlinear Regression: Error Analysis and Numerical Results ”, Applied Mathematical Programming and Modelling (APMOD),
Madrid, Spagna, 19-21 giugno 2006.
[PCI9] M. Sanguineti, R. Zoppoli, “New Developments on a Methodology for the Approximate Solution of Functional Optimization Problems ”, 21st European Conf. on Operational Research (EURO 2006),
Reykjavik, Islanda, 2-5 luglio 2006.
[PCI10] M. Sanguineti, “Rates of Growth of Covering Numbers of Certain Convex Hulls”, 21st European
Conf. on Operational Research (EURO 2006), Reykjavik, Islanda, 2-5 luglio 2006.
[PCI11] A. Alessandri, M. Cuneo, M. Sanguineti, “A Nonlinear Programming Algorithm for Neural-Network Learning Based on the Kalman Filter”. Mathematical Methods for Learning - Advances in Data Mining and Knowledge Discovery, Como, 21-24 giugno 2004.
[PCI12] M. Sanguineti, “Regularization and A-Priori Bounds on Complexity in Learning from Data by
Kernel Methods”. Mathematical Methods for Learning - Advances in Data Mining and Knowledge
Discovery, Como, 21-24 giugno 2004.
[PCI13] M. Baglietto, C. Cervellera, M. Sanguineti, R. Zoppoli, “ Water Reservoirs Management Under
Uncertainty by Approximating Networks and Learning from Data”.Workshop “Modelling and Control
for Participatory Planning and Managing Water Systems”, Venezia, 29 settembre - 1o ottobre 2004.
[PCI14] M. Sanguineti, M. Baglietto, R. Zoppoli, “The Extended Ritz Method for Dynamic Routing in
Traffic Networks”. EURO/INFORMS Joint Int. Meeting, Istanbul, Turchia, 6-10 luglio 2003.
[PCI15] M. Sanguineti, V. Kůrková, “Bounding the Error in Approximate Functional Optimization”.
EURO/INFORMS Joint Int. Meeting, Istanbul, Turchia, 6-10 luglio 2003.
[PCI16] M. Sanguineti, V. Kůrková, “Approximate Functional Optimization in Kernel Methods”.
EURO/INFORMS Joint Int. Meeting, Istanbul, Turchia, 6-10 luglio 2003.
[PCI17] M. Baglietto, M. Sanguineti, R. Zoppoli, “The Extended Ritz Method: An Example in Communication Networks”. Applied Mathematical Programming and Modelling (APMOD), Varenna, Lecco, 17-19 giugno 2002 (Book of Abstracts pp. 70-72).
[PCI18] P . C. Kainen, V. Kůrková, M. Sanguineti, “Minimization Rates of Error Functionals Over Boolean
Variable-Basis Functions”. Applied Mathematical Programming and Modelling (APMOD), Varenna,
Lecco, 17-19 giugno 2002 (Book of Abstracts pp. 75-77).
[PCI19] M. Sanguineti, “Error Estimates for Approximate Solution of Optimization Problems by Approximating Networks”. Workshop Mathematical Diagnostics, Erice, Italia, 17-25 giugno 2002 (Book of Abstracts pp. 11-12).
[PCI20] R. Zoppoli, M. Sanguineti, T. Parisini, “The Extended Ritz Method and the Curse of Dimensionality in Functional Optimization”. 33rd Workshop High Performance Algorithms and Software for Nonlinear Optimization, Erice, Italia, 30 giugno - 8 luglio 2001.
WORKSHOP NAZIONALI, CON PRESENTAZIONI SU INVITO
[WNI1] M. Sanguineti, V. Kůrková, “Model complexities of shallow neural networks for the approximation of input-output mappings with large variations”. 4th Italian Workshop on Machine Learning and Data Mining (MLDM), 14th Conf. of the Italian Association for Artificial Intelligence (AI*IA). Ferrara, 22 settembre 2015.
[WNI2] G. Gnecco, A. Bemporad, M. Gori, R. Morisi, and M. Sanguineti, “A model of online learning
as a Linear Quadratic Gaussian (LQG) optimal control problem with random matrices”. 4th Italian
Workshop on Machine Learning and Data Mining (MLDM), 14th Conf. of the Italian Association for
Artificial Intelligence (AI*IA). Ferrara, 22 settembre 2015.
[WNI3] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Learning with mixed hard/soft constraints by
support constraint machines”. 3rd Italian Workshop on Machine Learning and Data Mining (MLDM),
13th Symp. of the Italian Association for Artificial Intelligence (AI*IA). Pisa, 10-11 dicembre 2014.
ATTI DI CONFERENZE NAZIONALI
[ACN1] A. Alessandri, M. Maggiore, M. Sanguineti, “Training Feedforward Neural Networks: Convergence and Robustness Analysis”. 10th Italian Workshop on Neural Nets (WIRN), pp. 267-272, 1998.
ALTRE PRESENTAZIONI A CONFERENZE
[PC1] G. Gnecco, A. Bemporad, M. Gori, M. Sanguineti, “A Novel Optimal-Control-BasedModel of Online Learning from Data”. 46th AIRO Conf., Trieste, 6-9 settembre 2016.
[PC2] G. Gnecco, M. Gori, S. Melacci, M. Sanguineti, “Dealing with Mixed Hard/Soft Constraints via
Support Constraint Machines”. 45th AIRO Conf., Pisa, 7-10 settembre 2015.
[PC3] G. Gnecco, F. El Ouardighi, K. Kogan, M. Sanguineti, “A Two-Player Differential Game Model
for the Management of Transboundary Pollution and Environmental Absorption”. 45th AIRO Conf.,
Pisa, 7-10 settembre 2015.
[PC4] M . Degiorgis, G. Gnecco, S. Gorni, R. Morisi, G. Roth, M. Sanguineti, A.C. Taramasso, “Evaluating
Flood Hazard at the Catchment Scale via Machine-Learning Techniques”. 8th AIRO Winter Conf.,
Champoluc, 25-30 gennaio 2015.
[PC5] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Average Packet Delivery Delay in Intermittently-Connected Networks”. 44th AIRO Conf., Como, 2-5 settembre 2014.
[PC6] G. Gnecco, M. Gori, S.Melacci, M. Sanguineti, “Supervised Learning from Regions and Box Kernels”. 44th AIRO Conf., Como, 2-5 settembre 2014.
[PC7] M. Sanguineti, C. Cervellera, M. Muselli, R. Zoppoli, “Approximate Dynamic Programming with
Bounds on Model Complexity and Sample Complexity: An Application to an Inventory Forecasting
Problem”. 7th AIRO Winter Conf., Champoluc, 28 gennaio - 1 febbraio 2013.
[PC8] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “Optimality Conditions for a Nonlinear Stochastic Knapsack Problem”, 43rd AIRO Conf., Vietri sul Mare, 4-7 settembre 2012.
[PC9] M. Gaggero, G. Gnecco, M. Sanguineti, R. Zoppoli, “Dynamic Programming and Value-Function
Approximation with Application to Optimal Consumption”, 43rd AIRO Conf., Vietri sul Mare, 4-7
settembre 2012.
[PC10] G. Gnecco, M. Sanguineti, R. Zoppoli, “Functional Optimization in OR Problems with Very Large
Numbers of Variables”, 42nd AIRO Conf., Brescia, 6-9 settembre 2011.
[PC11] M. Cello, G. Gnecco, M. Marchese, M. Sanguineti, “A Stochastic Knapsack Problem with Nonlinear Capacity Constraint”, 42nd AIRO Conf., Brescia, 6-9 settembre 2011.
[PC12] G. Gnecco, M. Gaggero, M. Sanguineti, “Decentralized Optimization Problems with Cooperating Decision Makers”. 41st AIRO Conf., Villa San Giovanni, 1-10 settembre 2010.
[PC13] M. Sanguineti, A. Alessandri, C. Cervellera, D. Macciò, “Design of Estimators via Optimization
Based on Quasi-Monte Carlo Sampling”. 5th AIRO Winter Conf., Cortina d’Ampezzo, 26-30 gennaio
2009.
[PC14] G. Gnecco, M. Sanguineti, “Smoothness and Approximation of Optimal Decision Strategies in Team Optimization Problems”. 40th AIRO Conf., Siena, 8-11 settembre 2009.
[PC15] G. Gnecco, M. Sanguineti, “Approximate Dynamic Programming by Value-Function Approximation via Variable-Basis Schemes”. 40th AIRO Conf., Siena, 8-11 settembre 2009.
[PC16] G. Gnecco, M. Sanguineti, “Structural Properties of Stochastic Dynamic Concave Optimization
Problems and Approximations of the Value and Optimal Policy Functions”. 39th AIRO Conf., Ischia,
8-11 settembre 2008.
[PC17] M. Sanguineti, A. Alessandri, C. Cervellera, D. Macciò, “Optimizing Parametrized Estimators and
Controllers via Low-Discrepancy Sampling Techniques”. 4th AIRO Winter Conf., Cortina d’Ampezzo,
5-9 febbraio 2007.[PC18] G. Gnecco, M. Sanguineti, R. Zoppoli, “Suboptimal Solutions to Dynamic Optimization Problems: Extended Ritz Method Versus Approximate Dynamic Programming”. 38th AIRO Conf., Genova, 5-8 settembre 2007.
[PC19] G. Gnecco, M. Sanguineti, “Deriving Approximation Error Bounds via Rademacher Complexity
and Learning Theory”. 38th AIRO Conf., Genova, 5-8 settembre 2007.
[PC20] A. Alessandri, M. Cuneo, M. Sanguineti, “Approximation of Nonlinear Least-Squares Solutions in the Presence of Disturbances”. 38th AIRO Conf., Genova, 5-8 settembre 2007.
[PC21] M. Sanguineti, R. Zoppoli, “An Optimization Point of View of Learning Tasks”. 37th AIRO Conf.,
Cesena, 12-15 settembre 2006.
[PC22] A. Alessandri, C. Cervellera,M. Sanguineti, “Approximate Solution of a Class of Optimal Estimation Problems via Nonlinear Programming and Sampling Techniques”. 37th AIRO Conf., Cesena, 12-15 settembre 2006.
[PC23] A. Alessandri, M. Cuneo, S. Pagnan, M. Sanguineti, “An Incremental Algorithm for Nonlinear
Least-Squares Problems in Learning Functional Relationships From Data”. 3rd AIRO Winter Conf.,
Cortina d’Ampezzo, 31 gennaio - 4 febbraio 2005.
[PC24] M. Sanguineti, “Learning with Kernels: An Overview”. 36th AIRO Conf., Camerino, 6-9 settembre
2005.
[PC25] V. Kůrková, M. Sanguineti, “Estimates of Covering Numbers of Convex Hulls”. 36th AIRO Conf.,
Camerino, 6-9 settembre 2005.
[PC26] M. Baglietto, G. Battistelli, M. Sanguineti, R. Zoppoli, “Shortest Path Problems on Stochastic
Graphs: Challenges and Promising Approaches”. 36th AIRO Conf., Camerino, 6-9 settembre 2005.
[PC27] M. Sanguineti, C. Cervellera, M. Muselli, R. Zoppoli, “Approximate Dynamic Programming with
Bounds on Model Complexity and Sample Complexity: An Application to an Inventory Forecasting
Problem”. 2nd AIRO Winter Conf., Champoluc, 9-14 febbraio 2004.
[PC28] M. Baglietto, M. Sanguineti, R. Zoppoli, “Decentralized Optimal Dynamic Routing in Telecommunication Networks by Parametrization of the Decision Strategies”. 35th AIRO Conf., Lecce, 7-10 settembre 2004.
[PC29] M. Sanguineti, R. Zoppoli, “Efficient Approximation Schemes for Functional Optimization”. 35th
AIRO Conf., Lecce, 7-10 settembre 2004.
[PC30] M. Sanguineti, “Learning and Generalization by Kernel Methods with Bounded Complexity”. 35th AIRO Conf., Lecce, 7-10 settembre 2004.
[PC31] A. Alessandri, M. Cuneo, M. Sanguineti, “An Algorithm for Parameters Estimation: Bounds on
the Estimation Error and Application to Time-Series Prediction”. 35th AIRO Conf., Lecce, 7-10
settembre 2004.
[PC32] M. Sanguineti, R. Zoppoli, “Approximation Schemes for Functional Optimization Problems”. 7th
Congresso SIMAI (Societ`a Italiana di Matematica Applicata e Industriale), Isola di San Servolo,
Venezia, 20-24 settembre 2004.
[PC33] M. Sanguineti, R. Zoppoli, “Approximate Solution of Freeway Optimal Control Problems by Approximation Schemes With Bounded Complexity”. 1st AIRO Winter Conf., Champoluc, 10-15 febbraio 2003.
[PC34] M. Sanguineti, R. Zoppoli, “A Framework for Approximate Infinite-Dimensional Optimization:
Recent Results and Open Problems in the Extended Ritz Method”. 34th AIRO Conf., Venezia, 2-5
settembre 2003.
[PC35] M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, R. Zoppoli, “Approximate Dynamic
Programming and Polynomially-Complex Approximating Networks for T -Stage Stochastic Optimal
Control”. 34th AIRO Conf., Venezia, 2-5 settembre 2003.
[PC36] V. Kůrková, M. Sanguineti, “Upper Bounds on Complexity in Kernel Methods”. 34th AIRO Conf.,
Venezia, 2-5 settembre 2003.
[PC37] V. Kůrková,M. Sanguineti, “From Functional Optimization to Nonlinear Programming by Nonlinear Approximators: Bounds on the Error of Approximate Optimization”. 33rd AIRO Conf., L’Aquila, 10-13 settembre 2002 (Book of Abstracts p. 156).
[PC38] M. Baglietto, M. Sanguineti, R. Zoppoli, “Optimization in Large-Scale Traffic Networks: Neural
and Other Approximating Networks Can Mitigate the Curse of Dimensionality”. 33rd AIRO Conf.,
L’Aquila, 10-13 settembre 2002 (Book of Abstracts p. 138).
[PC39] M. Baglietto, M. Sanguineti, R. Zoppoli, “Il Metodo di Ritz Esteso dei Problemi di Routing
Dinamico”. Convegno CIRA 2002, Perugia, 11-13 settembre 2002.
[PC40] T. Parisini, M. Sanguineti, R. Zoppoli, “Problemi di Ottimizzazione: `e Possibile Contrastare la Maledizione della Dimensionalit`a con le Reti Approssimanti?”. Convegno CIRA 2001, Lecce, 12-14 settembre 2001.
[PC41] M. Sanguineti, T. Parisini, R. Zoppoli, “Problemi di Ottimizzazione Funzionale: `e Possibile Mitigare la Maledizione della Dimensionalit`a?”. Convegno CIRA 2000, Torino, 6-8 settembre 2000.
[PC42] T. Parisini, M. Sanguineti, R. Zoppoli, “Ottimizzazione Funzionale e Soluzioni Parametriche
Approssimate”. Convegno CIRA 1999, Como, 11-13 ottobre 1999.
Insegnamenti Esterni
Operations Research (CdL Magistrale in Ing. Informatica)
Ricerca Operativa 1 (CdL in Ing. Industriale e Gestionale)
Tecniche per il Modellamento di Problemi di Ottimizzazione e per la Determinazione di Decisioni Ottime in Sistemi Sanitari (Scuola di Specializzazione in Igiene e Medicina Preventiva)
Strategic Choices: Games and Team Optimization (Dottorato in Informatica e Ing. dei Sistemi - DIBRIS)