Recent publications (more in my Google Scholar profile):

  1. Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos. Newton-type alternating minimization algorithm for convex optimization. IEEE Transactions on Automatic Control, Volume 64, Issue 2, pp. 697-711, 10.1109/TAC.2018.2872203, 2019.

  2. Syama Rangapuram, and Matthias Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski. Deep State Space Models for Time Series Forecasting. Advances in Neural Information Processing Systems, 2018.

  3. Andreas Themelis, Lorenzo Stella, Panagiotis Patrinos. Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms. SIAM Journal on Optimization, Volume 28, Issue 3, pp. 2274–2303, 10.1137/16M1080240, 2018.

  4. Lorenzo Stella, Andreas Themelis, Panagiotis Patrinos. Forward-backward quasi-Newton methods for nonsmooth optimization problems. Computational Optimization and Applications, Volume 67, Issue 3, pp. 443–487, 10.1007/s10589-017-9912-y, 2017.

  5. Lorenzo Stella, Andreas Themelis, Pantelis Sopasakis, Panagiotis Patrinos. A simple and efficient algorithm for nonlinear model predictive control. 56th IEEE Conference on Decision and Control, 10.1109/CDC.2017.8263933, 2017.

  6. Puya Latafat, Lorenzo Stella, Panagiotis Patrinos. New primal-dual proximal algorithms for distributed optimization. 55th IEEE Conference on Decision and Control, pp. 1959-1964, 10.1109/CDC.2016.7798551, 2016.

  7. Panagiotis Patrinos, Lorenzo Stella, Alberto Bemporad. Douglas-Rachford splitting: complexity estimates and accelerated variants. 53rd IEEE Conference on Decision and Control, pp. 4234-4239, 10.1109/CDC.2014.7040049, 2014.

  8. Panagiotis Patrinos, Lorenzo Stella, Alberto Bemporad. Forward-backward truncated Newton methods for convex composite optimization. Working paper, arXiv:1402.6655, 2014.