PhD in Ingegneria dell’Informazione at Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze. My research interests are machine learning, nonmonotone strategies, Nash Equilibrium Problems and derivative free optimization.


  1. A nonmonotone trust-region method for generalized Nash equilibrium and related problems with strong convergence properties. Galli, L. and Kanzow, C. and Sciandrone, M. Computational Optimization and  Application (2018) 69: 629-652. (
  2. Machine learning methods for short-term bid forecasting in the renewable energy market: A case study in ItalyCocchi, G. and Galli, L. and Galvan, G. and Sciandrone, M. Cantù, M. and Tomaselli GWind Energy (2018) 5: 357-371. (
  3. Modeling cancer drug response through drug-specific informative genes. Parca, L. and Pepe, G. and Pietrosanto, M. and Galvan, G. and Galli, L. and Palmeri, A. and Sciandrone, M. and Ferrè, F. and Ausiello, G. and Helmer-Citterich, M. Scientific Reports (2019) 9. (
  4. A unified convergence framework for nonmonotone inexact decomposition methods. Galli, L. and Galligari, A. and Sciandrone, M. Computational Optimization and Application (2020) 75: 113-144. (
  5. Prescriptive Analytics for Inventory Management in Health Care. Galli, L. and Levato, T. and Schoen, F and Tigli, L. Journal of the Operational Research Society (2020), In press. (

Submitted Papers

  1. A Study on Truncated Newton Methods for Linear Classification. Galli, L. and Lin, C-J. Submitted (2020). Preprint
  2. Controlling the degree of nonmonotonicity: a new line search framework combining two nonmonotone techniques. Galli, L. Operations Research Letters (2020)

My ORCID number: 0000-0002-8045-7101

Phone: +39 3333715116





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