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Marcello Sanguineti - Publications

Books

  1. R. Zoppoli,  T. Parisini, M. Baglietto,M. Sanguineti, “Neural Approximations for Optimal Control and Decision”. Control and Communications Systems Series, Springer-Verlag, in preparation.

International journals

  1. T. Parisini, M. Sanguineti, R. Zoppoli, “Nonlinear Stabilization by Receding-Horizon Neural Regulators”, International Journal of Control, vol. 70, pp. 341-362, 1998.

  2. V. Kủrková, M. Sanguineti, “Bounds on Rates of Variable-Basis and Neural-Network Approximation”, IEEE Transactions on Information Theory, vol. 47, pp. 2659-2665, 2001.

  3. V. Kủrková, M. Sanguineti, “Comparison of Worst Case Errors in Linear and Neural Network Approximation”, IEEE Transactions on Information Theory, vol. 48, pp. 264-275, 2002.

  4. R. Zoppoli, M. Sanguineti, T. Parisini, “Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems”, Journal of Optimization Theory and Applications, vol. 112, pp. 403-440, 2002.

  5. 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.

  6. K. Hlaváčková-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.

  7. P. C. Kainen, V. Kủrková, M. Sanguineti, “Minimization of Error Functionals Over Variable-Basis Functions”, SIAM Journal on Optimization, vol. 14, pp. 732-742, 2003.

  8. A. Alessandri, M. Sanguineti, “Optimization of Approximating Networks for Optimal Fault Diagnosis”, Optimization Methods and Software, vol. 20, pp. 241-266, 2005.

  9. V. Kủrková, M. Sanguineti, “Error Estimates for Approximate Optimization by the Extended Ritz Method”, SIAM Journal on Optimization, vol. 15, pp. 461-487, 2005.

  10. V. Kủrková, M. Sanguineti, “Learning with Generalization Capability by Kernel Methods of Bounded Complexity”, Journal of Complexity, vol. 21, pp. 350-367, 2005.

  11. P. C. Kainen, V. Kủrková, 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.

  12. A. Alessandri, M. Sanguineti, “Input-Output Stability for Optimal Estimation Problems”, International Mathematical Forum, vol. 2, no. 13, pp. 593 - 617, 2007. 

  13. 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.

  14. 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.

  15. 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. 

  16. 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.

  17. V. Kủrková, M. Sanguineti, “Estimates of Covering Numbers of Convex Sets with Slowly Decaying Orthogonal Subsets''. Discrete Applied Mathematics, vol. 155, pp. 1930-1942, 2007.

  18. 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.

  19. G. Gnecco, M. Sanguineti, “Approximation Error Bounds via Rademacher Complexity''. Applied Mathematical Sciences, vol. 2, pp. 153-176, 2008.

  20. A . Alessandri, M. Cuneo, M. Sanguineti, "Nonlinear Programming Algorithm with Exponentially Bounded Error for the Optimization of Connectionstic Models", International Journal of Computational Intelligence in Control, to appear.

  21. G . Gnecco, M. Sanguineti, "Value and Policy Functions Approximations in Infinite-Horizon Optimization Problems", Journal of Dynamical Systems and Geometric Theories, vol. 6, pp. 123-147, 2008.

  22. G. Gnecco, M. Sanguineti, "Editorial for the Special Issue: Mathematical Problems in Engineering, Aerospace and Sciences'', Applied Mathematical Sciences, to appear.

  23. G. Gnecco, M. Sanguineti, “Accuracy of Suboptimal Solutions to Kernel Principal Component Analysis'', Computational Optimization and Applications,  vol. 42, pp. 265-287, 2009.

  24. G. Gnecco, M. Sanguineti, “The Weight-Decay Learning Technique in Learning from Data: An Optimization Point of View'', Computational Management Science, vol. 6, pp. 53-79, 2009.

  25. M. Sanguineti, “Universal Approximation by Ridge Computational Models: A Survey'', The Open Applied Mathematics Journal, vol. 2, pp.31-58, 2008.

  26. E. Messina, M. Sanguineti, “Editorial for the Special Issue: Operations Research and Data Mining in Biological Systems''. Computers and Operations Research, to appear.

  27. 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.

  28. S. Giulini, M. Sanguineti, “Approximation Schemes for Functional Optimization Problems'', Journal of Optimization Theory and Applications, vol. 140, pp. 33-54, 2009.

  29. 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, DOI: 10.1007/s10589-008-9221-6, 2008.

  30. A. Alessandri, G. Gnecco, M. Sanguineti, ``Computationally Efficient Approximation Schemes for Functional Optimization''.  International Journal of Computer Research, vol. 17, to appear.

  31. A. Alessandri, C. Cervellera, D. Macciň, M. Sanguineti, "Optimization based on quasi-Monte Carlo sampling to design state estimators for nonlinear systems''. Optimization, to appear.

  32. P. C. Kainen, V. Kủrková, M. Sanguineti, "Complexity of Gaussian Radial-Basis Networks Approximating Smooth Functions''. Journal of Complexity, vol. 25, pp. 63-74, 2009.

  33. 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.

  34. G. Gnecco, M. Sanguineti, "Weight-Decay Regularization in Reproducing Kernel Hilbert Spaces by Variable-Basis Schemes'', WSEAS Transactions on Mathematics, vol. 8 n. 11, pp. 625-634, 2009.

  35. G.  Gnecco, M. Sanguineti, "Approximation schemes for functional optimization: Information complexity issues''. Mathematics in Engineering, Science and Aerospace, to appear.

  36. G. Gnecco, M. Sanguineti, “Suboptimal Solutions to Dynamic Optimization Problems via Approximations of the Policy Functions'', Journal of Optimization Theory and Applications, to appear.

  37. V. Kủrková, M. Sanguineti, ``Approximate Minimization of the Regularized Expected Error Over Kernel Models'', Mathematics of Operations Research, vol. 33, pp.747-756, 2008.

  38. V. Kủrková, M. Sanguineti, "Geometric Upper Bounds on Rates of Variable-Basis Approximation''. IEEE Transactions on Information Theory, vol. 54, pp. 5681-5688, 2008.

  39. G. Gnecco, M. Sanguineti, “Estimates of Variation with Respect to a Set and Applications to Optimization Problems'', Journal of Optimization Theory and Applications, DOI 10.1007/s10957-009-9620-6.

  40. G. Gnecco, M. Sanguineti, “Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data'', Neural Computation, DOI 10.1162/neco.2009.05-08-786, vol. 22, n. 3, 2010.

  41.  G. Gnecco, M. Sanguineti, “Error Bounds for Suboptimal Solutions to Kernel Principal Component Analysis'', Optimization Letters, DOI 10.1007/s11590-009-0158-1.

  42. G. Gnecco, V. Kủrková, M. Sanguineti, “Some Comparisons of Complexity in Dictionary-Based and Linear Computational Models'', Neural Networks, to appear.

  43. G. Gnecco, M. Sanguineti, “On a Variational Norm Tailored to Variable-Basis Approximation Schemes'', IEEE Transactions on Information Theory, to appear.

  44. G. Gnecco, M. Sanguineti, “On Spectral Windows in Supervised Learning From Data'', Information Processing Letters, to appear.

  45. A. Alessandri, G. Gnecco, M. Sanguineti, ``Minimizing Sequences for a Family of Functional Optimal Estimation Problems'', Journal of Optimization Theory and Applications, vol. 147, no. 2, November 2010, DOI: 10.1007/s10957-010-9720-3DOI: 10.1007/s10957-010-9720-3.

  46. G. Gnecco, M. Sanguineti, ``Team Optimization Problems with Lipschitz Continuous Strategies'', Optimization Letters, to appear, DOI: 10.1007/s11590-010-0213-y.

Chapters in books and monographs

  1. V. Kủrková, M. Sanguineti, “Tight Bounds on Rates of Neural-Network Approximation”, Lecture Notes in Computer Science (G. Dorffner, H. Bischof, and K. Hornik, Eds.), vol. 2130, pp. 277-282. Springer, 2001.

  2. 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 and A. Murli, Eds., pp. 23-56. Kluwer Academic Publishers, 2003.

  3. 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, 2004.

  4. 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 Science Ltd., 2006.

  5. P. C. Kainen, V. Kủrková, M. Sanguineti, ``Estimates of Approximation Rates by Gaussian Radial-Basis Functions'', Lecture Notes in Computer Science, vol. 4432, pp, 11-18. Springer, Berlin-Heidelberg, 2007.

  6. V. Kủrková, M. Sanguineti, ``Geometric Rates of Approximation by Neural Networks'', Lecture Notes in Computer Science, vol. 4910, pp. 541-550. Springer, Berlin Heidelberg, 2008.

  7. 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.), Cambridge Scientific Publishers, to appear.

  8. A. Alessandri, G. Gnecco, M. Sanguineti, ``Computationally Efficient Approximation Schemes for Functional Optimization''. In "Computational Optimization: New Research Developments", F. Columbus, Ed. Nova Science Publishers, to appear.

  9. 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), pp. 113-154 (M. Bianchini, M. Maggini, F. Scarselli, Eds.). Springer, 2009.

  10. P.C. Kainen, V. Kủrková, M. Sanguineti, ``On Tractability of Neural-Network Approximation'', Lecture Notes in Computer Science, vol. 5495, pp. 11-21. Springer, Berlin Heidelberg, 2009.

  11. G. Gnecco, M. Sanguineti, ``Smooth Optimal Decision Strategies for Static Team Optimization Problems and their Approximations'', Lecture Notes in Computer Science, vol. 5901, pp. 440-451. Springer, Berlin Heidelberg, 2010.

  12. G. Gnecco, V. Kủrková, M. Sanguineti, ``Some Comparisons of Model Complexity in Linear and Neural-Network Approximation'', Lecture Notes in Computer Science, vol. 6354. Springer, Berlin-Heidelberg, to appear.

  13. P. C. Kainen, V. Kủrková, M. Sanguineti, ``Approximation of Multivariable Functions by Neural Networks'', in Handbook of Neural Information Processing. Springer, to appear

 

Book Reviews

  1. 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.

  2. M. Sanguineti, "Review of `Learning Theory: An Approximation Theory Viewpoint ' (by F. Cucker and D.-X. Zhou, Cambridge University Press, 2007)''. Mathematical Reviews, 2008.

Paper Reviews

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, MR2357573, MR2371992, MR2385835, MR2397161, MR2401192, MR2422203, MR2428976, MR2424165, MR2416789, MR2433285, MR2415826,  MR2434101, MR2462570, MR2426053,  MR2460286,   MR2503313,  MR2510835, MR2527756, MR2532464, MR2547230,  MR2577685,  MR2534877,   MR2554374, MR2558684.

Proceedings of international conferences: invited full papers

  1. T. Parisini, M. Sanguineti, and R. Zoppoli, “Nonlinear Stabilization by Receding-Horizon Neural Regulators”, Proc. 34th IEEE Conf. on Decision and Control (CDC), pp. 2433-2441, 1995.

  2. M. Baglietto, T. Parisini, M. Sanguineti, and R. Zoppoli, “Neural Networks and RBF Networks for Approximate Receding-Horizon Regulators”,  Proc. IEEE-SMC CESA96, pp. 280-285, 1996.

  3. V. Kủrková and  M. Sanguineti, “Dimension-Independent Approximation by Neural Networks: How Can we Cope With the Curse of Dimensionality?”, Proc. 3rd Int. Conf. on Non-Linear Problems in Aviation and Aerospace (ICNPAA), pp. 355-364, European Conference Publications, Cambridge, UK, 2002.

  4. R. Zoppoli, M. Sanguineti, and T. Parisini, “Approximating Networks for Functional Optimization Problems”, Proc. 3rd Int. Conf. on Non-Linear Problems in Aviation and Aerospace (ICNPAA), pp. 769-778, European Conference Publications, Cambridge, UK, 2002.

  5. M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, and R. Zoppoli, “Neural Approximators, Dynamic Programming and Stochastic Approximation”, Invited Session "Approximating Networks, Dynamic Programming, and Stochastic Approximation”, Proc. 19th American Control Conf. (ACC), pp. 3304-3308, 2000.

  6. R. Zoppoli, M. Sanguineti, and T. Parisini “Can We Cope with the Curse of Dimensionality in Optimal Control by Using Neural Approximators?”, Proc. 40th Conf. on Decision and Control (CDC), pp. 3540-3545, 2001.

  7. M. Sanguineti, “Complexity and Regularization Issues in Kernel Methods”, Invited Symposium ”Learning Theory & Kernel Methods”, Proc. 16th Int. Symp. on Mathematical Theory of Networks and Systems (MTNS), 2004.

  8. 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.

Proceedings of international conferences with full paper revision

  1. A. Alessandri, T. Parisini, M. Sanguineti, and R. Zoppoli, “Neural Strategies for Nonlinear Optimal Filtering”, Proc. IEEE Int. Conf. on Systems Engineering, pp. 44-49, 1992.

  2. A. Alessandri, T. Parisini, M. Sanguineti, and R. Zoppoli, “Neural Networks for Nonlinear Finite-Memory State-Estimators”, Proc. World Conf. on Neural Networks, pp. III123-III126, 1993.

  3. R. Zoppoli, T. Parisini, and M. Sanguineti, “Neural Approximators for Functional Optimization”, Proc. 35th IEEE Conf. on Decision and Control (CDC), pp. 3290-3293, 1996.

  4. A. Alessandri and  M. Sanguineti, “An Incremental Procedure for the Training of Feedforward Neural Networks”, Proc. 1996 Int. Symp. on Nonlinear Theory and its Applications (NOLTA), pp. 233-236, 1996.

  5. A. Alessandri, M. Maggiore, and M. Sanguineti, “Training Feedforward Neural Networks With Convergence Analysis”, Proc. 1997 Int. Symp. on Nonlinear Theory and its Applications (NOLTA), pp. 751-754, 1997.

  6. T. Parisini, M. Polycarpou, M. Sanguineti, and A. T. Vemuri, “Robust Parametric and Non-Parametric Fault Diagnosis in Nonlinear Input-Output Systems”, Proc. 36th IEEE Conf. on Decision and Control (CDC), pp. 4481-4482, 1997.

  7. A. Alessandri, M. Maggiore, and M. Sanguineti, “Training Feedforward Neural Networks Through a Parameter-Estimation-Based Algorithm”, Proc. Conf. on Neural Networks & Their Applications (NEURAP), pp. 225-228, 1998.

  8. A. Alessandri, M. Maggiore, and 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.

  9. K. Hlaváčková, and 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.

  10. A. Alessandri, L. Piccardo, M. Sanguineti, and G. S. Villa, “Comparison Between Multilayer Feedforward Nets and Radial Basis Functions to Solve Approximate Nonlinear Estimation Problems”, Proc. 1998 Int. Symp. on Nonlinear Theory and its Applications (NOLTA), pp. 105-108, 1998.

  11. K. Hlaváčková and  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.

  12. M. Sanguineti and  K. Hlaváčková, “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.

  13. V. Kủrková and  M. Sanguineti, “Some Comparisons of the Worst-Case Error in Linear and Neural-Network Approximation”, CD-Proc. of the 14th Int. Symp. on Mathematical Theory of Networks and Systems (MTNS), 2000.

  14. S. Giulini and  M. Sanguineti, “On Dimension-Independent Approximation by Neural Networks and Linear Approximators”, in "Neural Computing: New Challenges and Perspectives for the New Millenium", Proc. Int. Joint Conf. on Neural Networks  (IJCNN), 2000, Special Session “Neural Networks and Geometry”, pp. I283- I288. Los Alamitos, IEEE, 2000.

  15. V. Kủrková and  M. Sanguineti, “Comparison of Linear and Neural Network Approximation”, in "Neural Computing: New Challenges and Perspectives for the New Millenium", Proc. Int. Joint Conf. on Neural Networks (IJCNN), 2000, Special Session “Neural Networks and Geometry”, pp. I277- I282. Los Alamitos, IEEE, 2000.

  16. A. Alessandri and  M. Sanguineti, “On Estimators for Nonlinear Dynamic Systems in Lp Spaces”, Proc. 39th Conf. on Decision and Control (CDC), pp. 298-303, 2000.

  17. V. Kủrková and  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. Springer-Verlag, Vienna, 2001.

  18. A. Alessandri and  M. Sanguineti, “Lp-Stable and Asymptotic Estimators for Nonlinear Dynamic Systems”, Proc. 20th American Control Conf. (ACC), pp. 1991-1996, 2001.

  19. A. Alessandri and  M. Sanguineti, “Approximate Solution of Optimal Estimation Problems in Lp Spaces”, Proc. 5th IFAC Symp. on Nonlinear Control Systems, pp. 866-871, 2001.

  20. A. Alessandri and  M. Sanguineti, “Stable Approximate Estimators for a Class of Nonlinear Systems”, CD-Proc. 1st IFAC Symp. on System Structure and Control, 2001.

  21. M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, and 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.

  22. A. Alessandri and  M. Sanguineti, “W-Stable Estimators for Nonlinear Systems”, Proc. European Control Conf. (ECC), pp. 330-335, 2001.

  23. A. Alessandri and  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.

  24. A. Alessandri, M. Sanguineti, and M. Maggiore “Batch-mode Identification of Black-Box Models Using Feedforward Neural Networks,” Proc. American Control Conf. (ACC), pp. 406-411, 2002.

  25. A. Alessandri, M. Sanguineti, and M. Maggiore, “Optimized Feedforward Neural Networks for On-Line Identification of Nonlinear Models,” Proc. 41st IEEE Conf. on Decision and Control, pp. 1751-1756, 2002.

  26. V. Kủrková and M. Sanguineti, “Neural Network Learning as Approximate Optimization”, Proc. Int. Conf. on Artificial Neural Networks and Genetic Algorithms (ICANNGA) (D. W. Pearson, N. C. Steele, and R. F. Albrecht, Eds.), pp. 53-57. Springer-Verlag, Wien, 2003.

  27. A. Alessandri, G. Cirimele, M. Cuneo, S. Pagnan, and M. Sanguineti, “EKF Learning for Feedforward Neural Networks,” Proc. European Control Conference (ECC), 2003.

  28. V. Kủrková and M. Sanguineti, “Learning From Data by Neural Networks with Limited Complexity”, Proc. 1st IAPR-TC3 Workshop on Artificial Neural Networks in Pattern Recognition, pp. 146-151, 2003.

  29. A. Alessandri, M. Cuneo, S. Pagnan, and M. Sanguineti, “On the Convergence of EKF-Based Parameters Optimization for Neural Networks,” Proc. 42st IEEE Conf. on Decision and Control, pp. 6181-6186, 2003.

  30. A. Alessandri, C. Cervellera, F. A. Grassia, and M. Sanguineti, “Stable Approximate Estimators for a Class of Nonlinear Dynamic Systems,” Proc. American Control Conf. (ACC), pp. 2433-2438, 2004.

  31. A. Alessandri, C. Cervellera, F. A. Grassia, and M. Sanguineti, “An Approximate Solution to Optimal LpState Estimation Problems,” Proc. American Control Conf. (ACC), pp. 4204-4209, 2005.

  32. A. Alessandri, C. Cervellera, F. A. Grassia, and M. Sanguineti, “On Optimal Estimation Problems for Nonlinear Systems and Their Approximate Solution,” Proc. 16th IFAC World Congress, Prague, July 2005.

  33. M. Baglietto, C. Cervellera, M. Sanguineti, R. Zoppoli, ``Water Reservoirs Management in the Presence of Uncertainties: The Extended Ritz Method Versus Dynamic Programming'', International Conf. on Reservoir Operation and River Management (ICROM), Cina, September 2005.

  34. A. Alessandri, C. Cervellera, D. Macciň, M. Sanguineti, ``Design of Parametrized State Observers and Controllers for a Class of Nonlinear Continuous-Time Systems''. Proc. 45st IEEE Conf. on Decision and Control, pp. 5388-5393, 2006.

  35. V. Kủrková, M. Sanguineti, ``Geometric Rates of Approximation by Neural Networks'', 34th Int. Conf. on Current Trends in Theory and Practice of Computer Science (SOFSEM), 2008.

  36. G. Gnecco, M. Sanguineti, ``Estimates on Weight-Decay Regularization by Variable-Basis Schemes'', Proc. 9th Int. Conf. on Applied Computer Science (ACS), 2009.

  37. P. C. Kainen, V. Kủrková, M. Sanguineti, ``On Tractability of Neural-Network Approximation'', Int. Conf. On Adaptive and Natural Computing Algorithms, 2009.

  38. G. Gnecco, M. Sanguineti, ``Suboptimal Solutions to Network Team Optimization Problems'', CD-Proc. Int. Network Optimization Conf. (INOC), 2009.

  39. G. Gnecco, M. Sanguineti, ``Lipschitz Continuity of the Solutions to Team Optimization Problems Revisited'', Proc. Int. Conf. on Mathematical Science and Engineering (ICMSE), 2009.

  40. G. Gnecco, M. Sanguineti, ``Smooth Optimal Decision Strategies for Static Team Optimization Problems and their Approximations'', 36th Int. Conf. on Current Trends in Theory and Practice of Computer Science (SOFSEM), 2010.


Other presentations at international conferences

  1. R. Zoppoli, M. Sanguineti, and T. Parisini, “The Extended Ritz Method and the Curse of Dimensionality in Functional Optimization”, 33rd Workshop on High Performance Algorithms and Software for Nonlinear Optimization, 2001.

  2. V. Kủrková, M. Sanguineti,``Tight Bounds on Rates of Neural-Network Approximation'', Int. Conf. on Artificial Neural Networks (ICANN), 2001.

  3. M. Baglietto, M. Sanguineti, and R. Zoppoli, “The Extended Ritz Method: an Example in Communication Networks”, Applied Mathematical Programming and Modelling (APMOD), 2002 (Book of Abstracts pp. 70-72).

  4. P. C. Kainen, V. Kủrková, and M. Sanguineti, “Minimization Rates of Error Functionals Over Boolean Variable-Basis Functions”, Applied Mathematical Programming and Modelling (APMOD), 2002 (Book of Abstracts pp. 75-77).

  5. M. Sanguineti, “Error Estimates for Approximate Solution of Optimization Problems by Approximating Networks”, Workshop Mathematical Diagnostics, 2002 (Book of Abstracts pp. 11-12).

  6. M. Sanguineti, M. Baglietto, and R. Zoppoli, “The Extended Ritz Method for Dynamic Routing in Traffic Networks”, EURO/INFORMS Joint Int. Meeting, 2003.

  7. M. Sanguineti and  V. Kủrková, “Bounding the Error in Approximate Functional Optimization”, EURO/INFORMS Joint Int. Meeting, 2003.

  8. M. Sanguineti and V. Kủrková, “Approximate Functional Optimization in Kernel Methods”, EURO/INFORMS Joint Int. Meeting, 2003.

  9. A. Alessandri, M. Cuneo, and M. Sanguineti, “A Nonlinear Programming Algorithm for Neural-Network Learning Based on the Kalman Filter”, Mathematical Methods for Learning (MML) - Advances in Data Mining and Knowledge Discovery, 2004.

  10. M. Sanguineti, “Regularization and A-Priori Bounds on Complexity in Learning from Data by Kernel Methods”, Mathematical Methods for Learning (MML) - Advances in Data Mining and Knowledge Discovery, 2004.

  11. 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'', 2004.

  12. A. Alessandri, M. Cuneo, M. Sanguineti, “A Least-Squares Algorithm for Nonlinear Regression: Error Analysis and Numerical Results '', Applied Mathematical Programming and Modelling (APMOD),  2006.

  13. 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).

  14. M. Sanguineti, “Rates of Growth of Covering Numbers of Certain Convex Hulls'', 21st European Conf. on Operational Research (EURO 2006).

  15. P. C. Kainen, V. Kủrková, M. Sanguineti, “Estimates of Approximation Rates by Gaussian Radial-Basis Functions'', Int. Conference on Adaptive and Natural Computing Algorithms, 2007.

  16. G. Gnecco, M. Sanguineti, “Deriving Approximation Error Bounds via Rademacher's Complexity and Learning Theory'', 22nd European Conf. on Operational Research (EURO 2007).

  17. G. Gnecco, M. Sanguineti, R. Zoppoli, “Exploiting Structural Results in Approximate Dynamic Programming'',  22nd European Conf. on Operational Research (EURO 2007).

  18. G. Gnecco, M. Sanguineti, “Suboptimal Solutions to Team Optimization Problems with Statistical Information Structure'',  24th European Conf. on Operational Research (EURO 2010)

  19. 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)

Proceedings of national conferences - invited full papers

  1. R. Zoppoli, T. Parisini, and M. Sanguineti, “From Functional Optimization to Nonlinear Programming Through Neural Approximations”, Giornate di Lavoro AIRO, pp. 83-110, 1996.

  2. A. Alessandri, T. Parisini, M. Sanguineti, and R. Zoppoli, “Neural Nonlinear Controllers and Observers: Stability Results”, Proc. 8th Italian Workshop on Neural Nets (WIRN), pp. 80-90, 1996.

Proceedings of national conferences

  1. A. Alessandri, M. Maggiore, and M. Sanguineti, “Training Feedforward Neural Networks: Convergence and Robustness Analysis”, Proc. 10th Italian Workshop on Neural Nets (WIRN), pp. 267-272, 1998.

Other presentations at conferences

  1. T. Parisini, M. Sanguineti, and R. Zoppoli, “Ottimizzazione Funzionale e Soluzioni Parametriche Approssimate”, CIRA Meeting 1999.

  2. M. Sanguineti, T. Parisini, and R. Zoppoli, “Problemi di Ottimizzazione Funzionale: č Possibile Mitigare la Maledizione della Dimensionalitŕ?”, CIRA Meeting 2000.

  3. T. Parisini, M. Sanguineti, and R. Zoppoli, “Problemi di Ottimizzazione: č Possibile Contrastare la Maledizione della Dimensionalitŕ con le Reti Approssimanti?”, CIRA Meeting 2001.

  4. V. Kủrková and M. Sanguineti, “From Functional Optimization to Nonlinear Programming by Nonlinear Approximators: Bounds on the Error of Approximate Optimization”, XXXIII Annual Conf. of the Italian Operations Research Society (AIRO), 2002 (Book of Abstracts p. 156).

  5. M. Baglietto, M. Sanguineti, and R. Zoppoli, “Optimization in Large-Scale Traffic Networks: Neural and Other Approximating Networks Can Mitigate the Curse of Dimensionality”, XXXIII Annual Conf. of the Italian Operations Research Society (AIRO), 2002 (Book of Abstracts p. 138).

  6. M. Baglietto, M. Sanguineti, and R. Zoppoli, “Il Metodo di Ritz Esteso dei Problemi di Routing Dinamico”, CIRA Meeting 2002.

  7. M. Sanguineti and R. Zoppoli, “Approximate Solution of Freeway Optimal Control Problems by Approximation Schemes With Bounded Complexity”, I Winter Conf. of the Italian Operations Research Society (AIRO Winter), 2003.

  8. M. Sanguineti and R. Zoppoli, “A Framework for Approximate Infinite-Dimensional Optimization: Recent Results and Open Problems in the Extended Ritz Method”, XXXIII Annual Conf. of the Italian Operations Research Society (AIRO), 2003.

  9. M. Baglietto, C. Cervellera, T. Parisini, M. Sanguineti, and R. Zoppoli, “Approximate Dynamic Programming and Polynomially-Complex Approximating Networks for T-Stage Stochastic Optimal Control”, XXXIII Annual Conf. of the Italian Operations Research Society (AIRO), 2003.

  10. V. Kủrková and M. Sanguineti, “Upper Bounds on Complexity in Kernel Methods”, XXXIII Annual Conf. of the Italian Operations Research Society (AIRO), 2003.

  11. M. Sanguineti, C. Cervellera, M. Muselli, and R. Zoppoli, “Approximate Dynamic Programming with Bounds on Model Complexity and Sample Complexity: An Application to an Inventory Forecasting Problem”, II Winter Conf. of the Italian Operations Research Society (AIRO Winter), 2004.

  12. M. Baglietto, M. Sanguineti, and R. Zoppoli, “Decentralized Optimal Dynamic Routing in Telecommunication Networks by Parametrization of the Decision Strategies”, XXXV Annual Conf. of the Italian Operations Research Society (AIRO), 2004.

  13. M. Sanguineti and R. Zoppoli, “Efficient Approximation Schemes for Functional Optimization”, XXXV Annual Conf. of the Italian Operations Research Society (AIRO), 2004.

  14. M. Sanguineti, “Learning and Generalization by Kernel Methods with Bounded Complexity”, XXXV Annual Conf. of the Italian Operations Research Society (AIRO) 2004.

  15. A. Alessandri, M. Cuneo, and M. Sanguineti, “An Algorithm for Parameters Estimation: Bounds on the Estimation Error and Application to Time-Series Prediction”, XXXV Annual Conf. of the Italian Operations Research Society (AIRO), 2004.

  16. M. Sanguineti and R. Zoppoli, “Approximation Schemes for Functional Optimization Problems”, VII SIMAI Congress  (Italian Society for Industrial and Applied Mathematics), 2004.

  17. M. Sanguineti, A. Alessandri, M. Cuneo, and S. Pagnan, “An Incremental Algorithm for Nonlinear Least-Squares Problems in Learning Functional Relationships From Data ”, III Winter Conf. of the Italian Operations Research Society (AIRO Winter), 2005.

  18. M. Sanguineti, ``Learning with Kernels: and Overview'', XXXVI Annual Conf. of the Italian Operations Research Society (AIRO) 2005.

  19. G. Gnecco, M. Sanguineti, ``Accuracy of Suboptimal Solutions to Kernel Principal Component Analysis'', XXXVI Annual Conf. of the Italian Operations Research Society (AIRO), 2005.

  20. V. Kủrková, M. Sanguineti, ``Estimates of Covering Numbers of Convex Hulls'', XXXVI Annual Conf. of the Italian Operations Research Society (AIRO), 2005.

  21. M. Baglietto, G. Battistelli, M. Sanguineti, R. Zoppoli, ``Shortest Path Problems on Stochastic Graphs: Challenges and Promising Approaches'', XXXVI Annual Conf. of the Italian Operations Research Society (AIRO), 2005.

  22. M. Sanguineti, R. Zoppoli, ``An Optimization Point of View of Learning Tasks'', XXXVII Annual Conf. of the Italian Operations Research Society (AIRO), 2006.

  23. A. Alessandri, C. Cervellera, M. Sanguineti, ``Approximate Solution of a Class of Optimal Estimation Problems via Nonlinear Programming and Sampling Techniques'', XXXVII Annual Conf. of the Italian Operations Research Society (AIRO), 2006.

  24. M. Sanguineti, A. Alessandri, C. Cervellera, D. Maccio', ``Optimizing Parametrized Estimators and Controllers via Low-Discrepancy Sampling Techniques'', IV Winter Conf. of the Italian Operations Research Society (AIRO Winter), 2007.

  25. G. Gnecco, M. Sanguineti, R. Zoppoli, ``Suboptimal Solutions to Dynamic Optimization Problems: Extended Ritz Method Versus Approximate Dynamic Programming'', XXXVIII  Annual Conf. of the Italian Operations Research Society (AIRO), 2007.

  26. G. Gnecco, M. Sanguineti, ``Deriving Approximation Error Bounds via Rademacher Complexity and Learning Theory'',  XXXVIII Annual Conf. of the Italian Operations Research Society (AIRO), 2007.

  27. A. Alessandri, M. Cuneo, M. Sanguineti, ``Approximation of Nonlinear Least-Squares Solutions in the Presence of Disturbances'', XXXVIII Annual Conf. of the Italian Operations Research Society (AIRO), 2007.

  28. G. Gnecco, M. Sanguineti, ``Structural Properties of Stochastic Dynamic Concave Optimization Problems and Approximations of the Value and Optimal Policy Functions'',  XXXIX Annual Conf. of the Italian Operations Research Society (AIRO), 2008.

  29. M. Sanguineti, A. Alessandri, C. Cervellera, D. Maccio', ``Design of Estimators via Optimization Based on Quasi-Monte Carlo Sampling''. IV Winter Conf. of the Italian Operations Research Society (AIRO Winter),  Cortina d'Ampezzo, 26-30
    January 2009.

  30. G. Gnecco, M. Sanguineti, ``Smoothness and Approximation of Optimal Decision Strategies in Team Optimization Problems'',  XL Annual Conf. of the Italian Operations Research Society (AIRO), Siena (Italy), September 8-11, 2009.

  31. G. Gnecco, M. Sanguineti, ``Approximate Dynamic Programming by Value-Function Approximation via Variable-Basis Schemes'',  XL Annual Conf. of the Italian Operations Research Society (AIRO), Siena (Italy), September 8-11, 2009.

  32. G. Gnecco, M. Gaggero, M. Sanguineti, ``Decentralized Optimization Problems with Cooperating Decision Makers'',  XLI Annual Conf. of the Italian Operations Research Society (AIRO), Villa San Giovanni (Italy), September 1-10, 2010.

Research reports

  1. V. Kủrková and M. Sanguineti, “Dimension-Independent Approximation by Neural Networks and its Comparison with Linear Approximation”, Research Report ICS-99-789, Institute of Computer Science - Academy of Sciences of the Czech Republic, 1999.

  2. V. Kủrková and M. Sanguineti, “Covering Numbers and Rates of Neural-Network Approximation”, Research Report ICS-00-830, Institute of Computer Science. Academy of Sciences of the Czech Republic, 2000.

  3. P. C. Kainen, V. Kủrková, and M. Sanguineti, “Minimization of Error Functionals Under Weakened Compactness”, Research Report ICS-02-864, Institute of Computer Science - Academy of Sciences of the Czech Republic, 2002.

  4. V. Kủrková and M. Sanguineti, “Tight Bounds on Rates of Variable-Basis Approximation via Estimates of Covering Numbers”, Research Report ICS-02-865,  Institute of Computer Science - Academy of Sciences of the Czech Republic, 2002.

  5. V. Kủrková and M. Sanguineti, “Error Estimates for Approximate Optimization Over Variable-Basis Functions”, Research Report ICS-02-882,  Institute of Computer Science - Academy of Sciences of the Czech Republic, 2002.

  6. A. Alessandri and M. Sanguineti “Optmization of Approximating Networks for Optimal Fault Diagnosos in Nonlinear Systems”, DIST Internal Report, University of Genoa, 2002.

  7. S. Giulini and M. Sanguineti, “Approximation Schemes with Bounded Complexity for High-Dimensional Optimization”, DIST Internal Report, University of Genoa, 2002.

  8. V. Kủrková and M. Sanguineti, “Learning with Generalization Capability by Kernel Methods of Bounded Complexity”, Research Report ICS-03-901, Institute of Computer Science - Academy of Sciences of the Czech Republic, 2003.

  9. M. Sanguineti, ``Regularization and A-Priori Bounds on Complexity in Learning from Data by Kernel Methods'', DIST Internal Report, University of Genoa, 2005.

  10. M. Sanguineti and R. Zoppoli, ``Kernels for Structured Data in the Stochastic Shortest Path Problem'', DIST Internal Report, University of Genoa, 2005.

  11. P.C. Kainen, V. Kủrková, and M. Sanguineti, “Rates of Approximation of Smooth Functions by Gaussian Radial-Basis-Function Networks“. Research Report ICS--976, Institute of Computer Science - Academy of Sciences of the Czech Republic, 2006.

Ph.D dissertation

"Laurea" dissertation