1. Emanuele Frandi, Confronto Numerico tra Algoritmi MEB per l’Addestramento di SVM nei Problemi di Classificazione, Master’s Thesis (in Italian), Università degli Studi di Firenze, 2010.
  2. Emanuele Frandi, Enhancing Direct Search Methods by Multilevel Techniques: Algorithms and Applications, PhD Thesis, Università degli Studi dell’Insubria, 2014.


  1. Emanuele Frandi, Maria Grazia Gasparo, Stefano Lodi, Ricardo Nanculef and Claudio Sartori, A new algorithm for training SVMs using approximate minimal enclosing balls, in: Proceedings of the 15th Iberoamerican Congress on Pattern Recognition, Lecture Notes in Computer Science, 6419, 87-95, Springer, 2010.
  2. Emanuele Frandi, Maria Grazia Gasparo, Ricardo Nanculef and Alessandra Papini, Solution of classification problems via computational geometry methods, Recent Advances in Nonlinear Optimization and Equilibrium Problems: a Tribute to Marco D’Apuzzo, Quaderni di Matematica, 27, 201–226, 2012.
  3. Emanuele Frandi, Ricardo Nanculef, Maria Grazia Gasparo, Stefano Lodi and Claudio Sartori, Training support vector machines using Frank-Wolfe optimization methods, International Journal of Pattern Recognition and Artificial Intelligence 27(3), 2013.
  4. Emanuele Frandi and Alessandra Papini, Coordinate search algorithms in multilevel optimization, Optimization Methods and Software 29(5), 1020-1041, 2014.
  5. Ricardo Nanculef, Emanuele Frandi, Claudio Sartori and Hector Allende, A novel Frank-Wolfe algorithm. Analysis and applications to large-scale SVM training, Information Sciences 285, 66-99, 2014.
  6. Marco Signoretto, Emanuele Frandi, Zahra Karevan and Johan Suykens, High Level High Performance Computing for Multitask Learning of Time-varying Models, Proceedings of the IEEE Symposium on Computational Intelligence in Big Data (IEEE-CIBD), 2014.
  7. Emanuele Frandi and Alessandra Papini, Improving direct search algorithms by multilevel optimization techniques, Optimization Methods and Software 30(5), 1077--1094, 2015.
  8. Emanuele Frandi, Ricardo Nanculef and Johan A. K. Suykens, Complexity issues and randomization strategies in Frank-Wolfe algorithms for Machine Learning, 2014 NIPS Workshop on Optimization for Machine Learning.
  9. Emanuele Frandi, Ricardo Nanculef and Johan A. K. Suykens, A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification, Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2015.
  10. Emanuele Frandi, Ricardo Nanculef, Stefano Lodi, Claudio Sartori and Johan A. K. Suykens, Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee, Machine Learning 104(2), 195-221, 2016.