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Fasano  Giovanni

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M. Eng, Ph.D. (Operations Research)
Associate Professor

Department of Management - Venice School of Management
University of Venice  "Ca' Foscari"
San Giobbe, Cannaregio 873,
30121 Venice, Italy

Phone:  +39-0412346922

MAIL    fasano(at)unive(dot)it   

Short CV (Italian)

TEACHING (Italian & English):


ITALIAN:
Il docente e' disponibile anche per assegnare tesi di Laurea e tesi Specialistiche/Magistrali sui seguenti argomenti:
  1. argomenti suggeriti dagli studenti e concordati con i medesimi (Laurea e Specialistica/Magistrale);
  2. argomenti su metodi quantitativi di ottimizzazione e/o analisi numerica (Laurea e Specialistica/Magistrale);
  3. argomenti relativi a comunicazione sociale (Laurea e Specialistica/Magistrale);
  4. criptovalute: analisi ed ottimizzazione del trading (Laurea e Specialistica/Magistrale).
Gli studenti per lo svolgimento della tesi possono avvalersi anche del servizio di tutorato di ateneo organizzato dall'Universita' Ca' Foscari Venezia.

ENGLISH:
The teacher is also available to assign degree theses and specialist/master's theses on the following topics:

   1. topics suggested by the students and agreed upon with them (Bachelor's and Master's degrees);
   2. topics on quantitative methods of Optimization and/or Numerical Analysis (Bachelor's and Master's degrees);
   3. topics relating to Social Communication (Bachelor's and Master's degrees);
   4. Cryptocurrencies: analysis and optimization of trading (Bachelor's and Master's degrees).

GENERAL  RESEARCH  INTERESTS:

  1. Unconstrained Optimization, 
  2. Nonlinear Least Squares, 
  3. Global Optimization, 
  4. Neural Networks,
  5. Derivative-Free Optimization
  6. Stochastic Processes.

CURRENT  INTERESTS:

  1. Conjugate Gradient Schemes,
  2. Iterative Methods for Indefinite and Singular Linear Systems,
  3. Model-based Derivative-Free Optimization, Linesearch-based Derivative-Free Optimization,
  4. Multidisciplinary Optimization,
  5. Evolutionary Algorithms,
  6. Stochastic Processes for Information Spreading,
  7. Quantitative Models for Cryptoassets.

WORKING  GROUPS:


RECENT  PAPERS: 


Papers on International Journals: 

  1. Mathematical Programming for the Dynamics of Opinion Diffusion, A.Ellero, G.Fasano, D.Favaretto, Physics (Switzerland), vol 5(3) pp. 936-951,  https://www.mdpi.com/journal/physics/special_issues/SergeGalam70, 2023.
  2. From regression models to Machine Learning approaches for long term Bitcoin price forecast, A.Caliciotti, M.Corazza, G.Fasano,  accepted for publication on Annals of Operations Research, https://doi.org/10.1007/s10479-023-05444-w, 2023.
  3. MURAME parameter setting for creditworthiness evaluation: data-driven optimization, M.Corazza, G.Fasano, S.Funari, R.Gusso, Decisions in Economics and Finance, open access, https://doi.org/10.1007/s10203-021-00322-1, 2021.
  4. A novel hybrid PSO-based metaheuristic for costly portfolio selection problems , M.Corazza, G.Di Tollo, G.Fasano, R.Pesenti,  Annals of Operations Research, vol. 304(1-2), pp. 109-137, https://doi.org/10.1007/s10479-021-04075-3, 2021.
  5. Dense Conjugate Initialization for Deterministic PSO in Applications: ORTHOinit+, C.Leotardi, A.Serani, M.Diez, E.F.Campana, G.Fasano, R.Gusso, Applied Soft Computing, vol. 104, article number 107121, 2021.
  6. Polarity and Conjugacy for Quadratic Hypersurfaces: a unified framework with recent advances, G.Fasano, R.Pesenti, Journal of Computational and Applied Mathematics, vol. 390, article number 113248,  DOI: 10.1016/j.cam.2020.113248, 2021.
  7. Issues on the use of a modified Bunch and Kaufman decomposition for large scale Newton's equation, A.Caliciotti, G.Fasano, F.Potra, M.Roma, Computational Optimization and Applications, vol. 77, pp. 627-651, DOI: 10.1007/s10589-020-00225-8, 2020.
  8. Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization, R. De Leone, G. Fasano, M. Roma, Ya.D. Sergeyev, Journal of Optimization Theory and Applications, 186(2), pp. 554-589, DOI: 10.1007/s10957-020-01717-7, 2020.
  9.  A Class of Approximate Inverse Preconditioners based on Krylov-subspace methods, for Large scale nonconvex optimization, M.Al-Baali, A.Caliciotti, G.Fasano, M.Roma, SIAM Journal on Optimization, vol. 30(3), pp. 1954-1979, DOI: 10.1137/19M1256907, 2020.
  10. Preconditioned Nonlinear Conjugate Gradient methods based on a modified secant equation, A.Caliciotti, G.Fasano, M.Roma,  Applied Mathematics and Computation, vol. 318, pp. 196-214, DOI: 10.1016/j.amc.2017.08.029, 2018.
  11. An adaptive truncation criterion for Newton-Krylov methods in large scale nonconvex optimization, A.Caliciotti, G.Fasano, S.Nash, M.Roma, Operations Research Letters, vol. 46(1), pp. 7-12, DOI: 10.1016/j.orl.2017.10.014, 2018.
  12. Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods, in large scale nonconvex optimization, A.Caliciotti, G.Fasano, S.Nash, M.Roma, Data in Brief, vol. 17, pp. 246-255, 2018.
  13. , R.De Leone, G.Fasano, Y.D.Sergeyev,  Computational Optimization and Applications, vol. 71, pp. 73-93, DOI: 10.1007/s10589-017-9957-y, 2018.
  14. Conjugate Direction Methods and Polarity for Quadratic Hypersurfaces, G.Fasano, R.Pesenti, Journal of Optimization Theory and Applications, vol. 175(3), pp. 764-794, DOI: 10.1007/s10957-017-1180-6, 2017.
  15. Exploiting damped techniques for nonlinear conjugate gradient methods, M.Al-Baali, A.Caliciotti, G.Fasano, M.Roma, Mathematical Methods of Operations Research, vol. 86, pp. 501-522, DOI: 10.1007/s00186-017-0593-1, 2017.
  16. Novel preconditioners based on quasi-Newton updates for nonlinear conjugate gradient methods, A. Caliciotti, G. Fasano, M. Roma, Optimization Letters, vol. 11, pp. 835-853,  DOI: 10.1007/s11590-016-1060-2, 2017.
  17. Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems, A.Serani, C.Leotardi, U.Iemma, E.F.Campana, G.Fasano, M.Diez, Applied Soft Computing, vol. 49(1), pp. 313-334, 2016.
  18. Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms, M. Diez, A.Serani, G.Fasano, G.Liuzzi, S.Lucidi, U.Iemma, E.F.Campana, Applied Ocean Research, vol. 159, pp. 115-128, DOI: 10.1016/j.apor.2016.04.006, 2016.
  19. A novel class of Approximate Inverse Preconditioners  for large positive definite systems, G.Fasano, M.Roma, Computational Optimization and Applications, vol. 65, pp. 399-429, DOI 10.1007/s10589-015-9765-1, 2016.
  20.  A Framework of Conjugate Direction Methods for Symmetric Linear Systems in Optimization, G.Fasano, Journal of Optimization Theory and Applications, vol.  164(3), pp. 883-914, DOI:10.1007/s10957-014-0600-0, 2015.
  21. A Linesearch-based Derivative-free Approach for Nonsmooth Constrained Optimization, G. Fasano, G. Liuzzi, S. Lucidi, F. Rinaldi, SIAM Journal  on Optimization, vol. 24(3), pp. 959-992, 2014.
  22. An Artificial Neural Network-based technique for on-line hotel booking, M.Corazza, G. Fasano,  F.Mason,  Procedia of Economics and Finance, vol. 15, pp. 45-55, 2014, WOS:000357094000006, DOI: 10.1016/S2212-5671(14)00444-4.
  23. Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem,  M.Corazza, G.Fasano, R.Gusso, Applied Mathematics and Computation, vol. 224, pp. 611-624,  DOI: 10.1016/j.amc.2013.07.091, 2013.
  24. Preconditioning Newton-Krylov Methods in Non-Convex Large Scale Optimization, G.Fasano, M.Roma, Computational Optimization and Applications, vol. 56(2), pp. 253-290, DOI: 10.1007/s10589-013-9563-6, 2013.
  25. Stochastic Model for Agents Interaction with Opinion-Leaders, A.Ellero, G.Fasano, A.Sorato, Physical Review E, vol. 87(4),  n. 042806, DOI: 10.1103/PhysRevE.87.042806, 2013.
  26. Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design, M.Diez, D.Peri, G.Fasano, E.F.Campana, Structural and Multidisciplinary Optimization, vol.  46, pp. 613-625, DOI: 10.1007/s00158-012-0783-7, 2012.
  27. Preconditioning Large Indefinite Linear Systems, G.Fasano, M.Roma, SQU Journal for Science, vol.  17(1), pp. 63-79, ISSN 1027-524X, 2012 (neither in Scopus nor in WOS).
  28. Penalty Function approaches for Ship Multidisciplinary Design Optimization (MDO), E.F.Campana, G.Fasano, D.Peri, European Journal of Industrial Engineering, vol.  6(6), pp. 765-784, 2012.
  29. Dynamic analysis for the selection of parameters and initial population, in Particle Swarm Optimization, E. F.Campana, G.Fasano,  A.Pinto, Journal of Global Optimization, vol. 48(3), pp. 347-397. DOI: 10.1007/s10898-009-9493-0, 2010.
  30. A nonmonotone truncated Newton-Krylov method exploiting negative curvature directions, for large scale unconstrained optimization, G.Fasano, S.Lucidi,  Optimization Letters, vol. 3(4), pp. 521-535, DOI10.1007/s11590-009-0132-y, 2009.
  31. A Modified Galam's Model for Word-of-Mouth  Information Exchange, A.Ellero, G.Fasano, A.Sorato, Physica A: Statistical Mechanics and its Applications, vol. 388(18), pp. 3901-3910, DOI 10.1016/j.physa.2009.06.002, 2009. 
  32.  On the Geometry Phase in Model-Based Algorithms for Derivative-Free Optimization, G. Fasano, J. L. Morales, J. Nocedal, Optimization Methods and Software, vol. 24(1), pp. 145-154, DOI: 10.1080/10556780802409296, 2009. 
  33.  Iterative Computation of Negative Curvature Directions in Large Scale Optimization, G.Fasano, M.Roma, Computational Optimization and Applications, vol. 38(1), pp. 81-104, DOI: 10.1007/s10589-007-9034-z, 2007. 
  34.  Lanczos-Conjugate Gradient method and pseudoinverse computation, on indefinite and singular systems, G.Fasano, Journal of Optimization Theory and Applications, vol. 132(2), pp. 267-285, DOI: 10.1007/s10957-006-9119-3, 2007. 
  35.  A Truncated Nonmonotone Gauss-Newton  Method for Large-Scale Nonlinear Least-Squares Problems, G.Fasano, F.Lampariello, M.Sciandrone, Computational Optimization and Applications, vol. 34(3), pp. 343-358, DOI: 10.1007/s10589-006-6444-2, 2006. 
  36.  Planar-Conjugate Gradient algorithm for Large Scale Unconstrained Optimization, Part 1: Theory, G.Fasano,  Journal of Optimization Theory and Applications, vol. 125(3), pp. 523-541, 2005. 
  37.  Planar-Conjugate Gradient algorithm for Large Scale Unconstrained Optimization, Part 2: Application, G.Fasano,  Journal of Optimization Theory and Applications, vol. 125(3), pp. 543-558, 2005. 
  38.  Conjugate Gradient (CG)-type Method for the Solution of Newton's equation within  Optimization Frameworks, G.Fasano,  Optimization Methods and Software, vol. 19(3-4), pp. 267-290, 2004.
  39. Uso delle Direzioni Coniugate negli algoritmi per l'Ottimizzazione Non Vincolata a grande dimensione, G.Fasano, Bollettino della Unione Matematica Italiana A, vol. 4(3), pp. 447-450, 2001.


Papers under Peer-Revision:


Other Peer Reviewed Papers:
  1. On the use of SYMMBK algorithm for computing negative curvature directions within Newton-Krylov methods, G.Fasano, C.Piermarini, M.Roma, AIRO Springer Series, Vol. 12, pp. 95-105, 2024.
  2. Misurare le Prestazioni al Tempo del Machine Learning G.Fasano, A.Pontiggia, Sviluppo & Organizzazione, Vol. 313, Ottobre/Novembre/Dicembre 2023.
  3. Krylov-Subspace Methods for Quadratic Hypersurfaces: a Grossone-based Perspective G.Fasano, Chapter 4 in the volume Numerical Infinities and Infinitesimals in Optimization - (Emergence, Complexity and Computation, 43), Yaroslav D. Sergeyev and R. De Leone (Eds.), Springer, 1st ed. 2022 (May 29, 2022), ISBN-10: 3030936414, ISBN-13: 978-3030936414, 2022.
  4. An improvement of the Pivoting Strategy in the Bunch and Kaufman Decomposition, Within Truncated Newton Methods, G. Fasano, M. Roma,  AIRO Springer book series (AIROSS, vol. 8, pp. 48-59), E-ISSN: 2523-7055, P-ISSN: 2523-7047, 2022 (Scopus indexed).
  5. Bitcoin Price Prediction: Mixed Integer Quadratic Programming Versus Machine Learning Approaches, M. Corazza, G. Fasano,  Mathematical and Statistical Methods for Actuarial Sciences and Finance (MAF 2022), Springer Conference proceedings, ISBN: 978-3-030-99637-6, 2022.
  6. Comparing RL applications for Financial Trading Systems, M. Corazza, G. Fasano, R. Gusso, R. Pesenti,  Mathematical and Statistical Methods for Actuarial Sciences and Finance (eMAF 2020), Springer Cham, ISBN: 978-3-030-78964-0, 2021.
  7. An application of Linear Programming to sociophysics models, A.Ellero, G.Fasano, D.Favaretto, accepted for publication on CEUR Workshop Proceedings (CEUR-WS.org), 2020 (Scopus indexed).
  8. How grossone can be helpful to iteratively compute negative curvature directions, R. De Leone, G. Fasano, M. Roma, Y.D. Sergeyev, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11353 LNCS, 2019, pp. 180-183, DOI: 10.1007/978-3-030-05348-2_16 (Scopus indexed).
  9. A PSO-based framework for nonsmooth portfolio selection problems, M. Corazza, G. di Tollo, G. Fasano, R. Pesenti, Springer International Publishing, Neural Advances in Processing Nonlinear Dynamic Signals, Smart Innovations, Systems and Technologies, vol. 102, pp. 265-275, 2019, ISBN: 978-3-319-95097-6 (Scopus indexed).
  10. Quasi-Newton based preconditioning and Damped quasi-Newton schemes, for Nonlinear Conjugate Gradient methods, M. Al-Baali, A. Caliciotti, G. Fasano, M. Roma, Springer Proceedings in Mathematics and Statistics (PROMS),  Numerical Analysis and Optimization, NAO-IV, Muscat, Oman, January 2018, vol. 235, pp. 1-21, DOI: 10.1007/978-3-319-90026-1_1 (Scopus indexed).
  11. Preconditioning strategies for Nonlinear Conjugate Gradient methods, based on Quasi-Newton updates, A.Caliciotti, G.Fasano, M.Roma,  Numerical Computations:Theory and Algorithms, The 2nd International Conference and Summer School (NUMTA 2016), Pizzo Calabro 19- June 2016, The American Institute of Physics (AIP) Conference Proceedings, vol. 1776, 090007, DOI: 10.1063/1.4965371 (Scopus indexed).
  12. Polarity for Quadratic Hypersurfaces and Conjugate Gradient Method: Relation between Degenerate and Nondegenerate Cases, G.Fasano, S.Giove, R.Gusso,  Numerical Computations: Theory and Algorithms, The 2nd International Conference and Summer School (NUMTA 2016), Pizzo Calabro 19- June 2016, The American Institute of Physics (AIP) Conference Proceedings, vol. 1776, 090031, DOI: 10.1063/1.4965395 (Scopus indexed).
  13. Dense Orthogonal Initialization for Deterministic PSO: ORTHOinit+, M. Diez, A. Serani, C. Leotardi, E.F. Campana, G. Fasano, R. Gusso,  The seventh International Conference on Swarm Intelligence (IC-SI 2016), Springer Lecture Notes in Computer Science 9713, Part I, pp. 322-330, 2016. Springer International Publishing Switzerland 2016), DOI 10.1007/978-3-319-41000-5, Softcover ISBN: 978-3-319-40999-3 (Scopus indexed).
  14. Application of derivative-free multi-objective algorithms to reliability-based robust design optimization of a high-speed catamaran in real ocean environment, R.Pellegrini, E.F.Campana, M.Diez, A.Serani, F.Rinaldi, G.Fasano, U.Iemma, G.Liuzzi, S.Lucidi, F.Stern, on Rodrigues et al. (Eds), Engineering Optimization IV, pp. 15-20,  Taylor & Francis Group, London, 2015, ISBN 9781138027251 (Scopus indexed).
  15. Globally convergent hybridization of particle swarm optimization using line search-based derivative-free techniques, A. Serani, M. Diez, E.F. Campana, G.Fasano, D. Peri, U. Iemma,  on Xin-She Yang (Eds.),  Recent Advances in Swarm Intelligence and Evolutionary Computation,  in Studies in Computational Intelligence  (SCI), vol. 585, Springer, ISSN 1860-949X, 2015 (Scopus indexed).
  16. On the use of Synchronous and Asynchronous Single-objective Deterministic Particle Swarm Optimization in Ship Design Problems, A.Serani, M.Diez, C.Leotardi, D.Peri, G.Fasano, U.Iemma, E.F.Campana, OPT-i 2014 - 1st International Conference on Engineering and Applied Sciences Optimization, Proceedings, N.D. Lagaros, M.G. Karlaftis, M. Papadrakakis (Eds.),  pp. 1218-1240, ISBN 978-960999946-5, 2014 (Scopus indexed).
  17.  A proposal of PSO particles' initialization  for costly unconstrained optimization problems: ORTHOinit, M.Diez, A.Serani, C.Leotardi, E.F.Campana, D.Peri, U.Iemma, G.Fasano, S.Giove, on Y. Tan et al. (Eds.): IC-SI 2014, Part I, Springer Lecture Notes in Computer Science 8794, pp. 126-133, 2014. Springer International Publishing Switzerland 2014) (Scopus indexed).
  18. Initial particles position for PSO, in Bound Constrained Optimization, E. F.Campana, M.Diez, G.Fasano, D.Peri, on Y.Tan, Y.Shi, and H.Mo (Eds.): Advances in Swarm Intelligence, Part I, Springer Lecture Notes in Computer Science 7928, pp. 112-119, 2013, Springer-Verlag Berlin Heidelberg  (Scopus indexed).
  19. Quasi-Newton updates for Preconditioned Nonlinear Conjugate Gradient methods, G.Fasano, M.Roma, Quaderni di Matematica, Dipartimento di Matematica della Seconda Universita' di Napoli, V. De Simone, D. di Serafino, G. Toraldo (eds.), Recent Advances in Nonlinear Optimization and Equilibrium  Problems: a Tribute to Marco D'Apuzzo, Vol. 27, Aracne, ISBN 978-88-548-5687-5, 2012 (refereed paper).
  20. Comparison between Deterministic and Stochastic formulations of Particle Swarm Optimization, for Multidisciplinary Design Optimization, D.Peri, G.Fasano, M.Diez, 12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 17-19 September 2012, Indianapolis, Indiana, paper AIAA 2012-5523, ISBN: 978-1-60086-930-3,  DOI:  10.2514/6.2012-5523 (Scopus indexed).
  21. Portfolio Selection with an Alternative Measure of Risk: Computational Performances of Particle Swarm Optimization and Genetic Algorithms, M.Corazza, G.Fasano, R.Gusso, Mathematical and Statistical Methods for Actuarial Sciences and Finance, XII, 2011, pp. 123-130, Perna, Cira; Sibillo, Marilena (Eds.), Springer Verlag, Hardcover, ISBN 978-88-470-2341-3 (Scopus indexed).
  22. Globally convergent modifications of Particle Swarm Optimization for Unconstrained Optimization, E. F.Campana, G.Fasano,  D.Peri, book chapter in  Particle Swarm Optimization: Theory, Techniques and Applications,  Nova Publishers, Series: Advances in Engineering Mechanics (Series Editor: Dr. Bohua Sun), ISBN: 978-1-61668-527-0, 2011 (Scopus indexed). 
  23. Methods for large scale unconstrained optimization, G.Fasano, Section 1.2.1.8 for Wiley Encyclopedia of Operations Research and Management Science, James J. Cochran (Editor-in-Chief), Sherry Wasserman Editorial Assistant, John Wiley & Sons, Inc., NJ, ISBN: 978-0-470-40063-0, hardcover, 2010 (refereed paper). 
  24. Multidisciplinary Robust Optimization for Ship Design, M.Diez, D.Peri, G.Fasano, E.F.Campana, 28th Symposium on Naval Hydrodynamics, Pasadena, California, 12-17 September 2010 (refereed paper).
  25. Global Optimization Algorithms in Multidisciplinary Design Optimization , D.Peri, G.Fasano, D.Dessi, E.F.Campana, 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, MAO 2008, Article number 2008-5888, ISBN: 978-156347947-2, DOI: 10.2514/6.2008-5888 (Scopus indexed). 
  26. Nonlinear Programming Approaches in the Multidisciplinary Design Optimization of a Sailing Yacht Keel Fin, E.F.Campana, G.Fasano, D.Peri, A.Pinto, 9th International Conference on Numerical Ship Hydrodynamics, Ann  Arbor, Michigan, August 5-8, 2007, (Scopus code: 2-s2.0-84883423273) (Scopus indexed). 
  27. Particle Swarm Optimization: efficient globally convergent modifications, E. F.Campana, G.Fasano, D.Peri,  A.Pinto, III European Conference On Computational Mechanics - Solids, Structures and Coupled Problems in Engineering, Lisbon 05-09/06/2006 (refereed volume).
  28. Dynamic system analysis and initial particles position in Particle Swarm Optimization, E.F.Campana, G.Fasano, A.Pinto, IEEE Swarm Intelligence Symposium 2006, Indianapolis 12-14/05/2006 (refereed volume). 
  29. Issues on Nonlinear Programming for Multidisciplinary Design Optimization (MDO) in Ship Design Framework, E.F.Campana, G.Fasano, D.Peri, 8th Numerical Towing Tank Symposium (NuTTS '05), Varna, Bulgaria 2-4 October 2005  (invited lecture on conference volume).
  30. Planar-CG methods and Matrix Tridiagonalization in Large scale Unconstrained Optimization, G.Fasano,  in  High Performance Algorithms and Software for Nonlinear Optimization, G. Di Pillo and A. Murli, Eds., Kluwer Academic Publishers, pp. 243-263, 2003  (refereed volume). 
  31. Use of Conjugate Directions inside Newton-type Algorithms for Large Scale Unconstrained Optimization,G.Fasano, PhD final dissertation in Operations Research, XIII course, 2001.


Other Papers (incomplete):

My Music:  "Mirror pieces" (unavailable)

     This site is still under construction, for any comments contact  fasano(at)unive(dot)it   Last update April 24th, 2024.