About

I am currently an Assistant Professor at IMT Lucca (Italy). My research interests revolve around nonsmooth and nonconvex optimization, game theory, and bilevel optimization, with applications in machine learning, systems control, and economics. I received my PhD in 2020 jointly from KU Leuven (Belgium) and IMT Lucca (Italy). Prior to joining IMT Lucca, I held a Postdoc position with Research Foundation Flanders (FWO) at KU Leuven. You can read more about my research in my publications.

Publications

Preprints

• [P1]Adeoye, A. D., Latafat, P., Bemporad, A. (2025). “A proximal augmented Lagrangian method for nonconvex optimization with equality and inequality constraints,” arXiv:2509.02894
• [P2]Pethick, T., Latafat, P., Lewandowski, B., Xu, Z., Kairouz, P., Patrinos, P., Cevher, V. (2025). “iFedDR: Auto-tuning local computation with inexact Douglas–Rachford splitting in federated learning”

Journal Papers

• [J1]Upadhyaya, M., Latafat, P., Giselsson, P. “A Lyapunov analysis of Korpelevich's extragradient method with fast and flexible extensions,” Mathematical Programming (published online), 2026
• [J2]Evens, B., Latafat, P., Patrinos, P. “Spingarn's method and progressive decoupling beyond elicitable monotonicity,” Computational Optimization and Applications (to appear), 2026
• [J3]Ou, H., Latafat, P., Themelis, A. “Linesearch-free adaptive Bregman proximal gradient for convex minimization without relative smoothness,” Journal of Optimization Theory and Applications (to appear), 2026
• [J4]Latafat, P., Themelis, A., Stella, L., Patrinos, P. “Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient,” Mathematical Programming 213(1):433–471, 2025
• [J5]Evens, B., Latafat, P., Patrinos, P. “Convergence of the Chambolle–Pock algorithm in the absence of monotonicity,” Journal of Optimization Theory and Applications 206(1):7, 2025
• [J6]Latafat, P., Themelis, A., Villa, S., Patrinos, P. “On the convergence of proximal gradient methods for convex simple bilevel optimization,” Journal of Optimization Theory and Applications 204(3):51, 2025
• [J7]Evens, B., Pas, P., Latafat, P., Patrinos, P. “Convergence of the preconditioned proximal point method and Douglas–Rachford splitting in the absence of monotonicity,” Mathematical Programming 214(1–2):247–301, 2025
• [J8]Behmandpoor, P., Latafat, P., Themelis, A., Moonen, M., Patrinos, P. “SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization,” Computational Optimization and Applications 88(1):71–106, 2024
• [J9]Latafat, P., Themelis, A., Ahookhosh, M., Patrinos, P. “Bregman Finito/MISO for nonconvex regularized finite sum minimization without Lipschitz gradient continuity,” SIAM Journal on Optimization 32(3):2230–2262, 2022
• [J10]Latafat, P., Themelis, A., Patrinos, P. “Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems,” Mathematical Programming 193(1):195–224, 2022
• [J11]Latafat, P., Patrinos, P. “Primal-dual algorithms for multi-agent structured optimization over message-passing architectures with bounded communication delays,” Optimization Methods and Software 37(6):2052–2079, 2022
• [J12]Latafat, P., Freris, N. M., Patrinos, P. “A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization,” IEEE Transactions on Automatic Control 64(10):4050–4065, 2019
• [J13]Latafat, P., Patrinos, P. “Asymmetric forward–backward–adjoint splitting for solving monotone inclusions involving three operators,” Computational Optimization and Applications 68(1):57–93, 2017

Conference Papers

• [C1]Oikonomidis, K. A., Laude, E., Latafat, P., Themelis, A., Patrinos, P. “Adaptive proximal gradient methods are universal without approximation,” ICML 2024 (spotlight award)
• [C2]Latafat, P., Themelis, A., Patrinos, P. “On the convergence of adaptive first order methods: Proximal gradient and alternating minimization algorithms,” L4DC 2024, pp. 197–208
• [C3]Pethick, T., Fercoq, O., Latafat, P., Patrinos, P., Cevher, V. “Solving stochastic weak Minty variational inequalities without increasing batch size,” ICLR 2023
• [C4]Pethick, T., Latafat, P., Patrinos, P., Fercoq, O., Cevher, V. “Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems,” ICLR 2022 (spotlight award)
• [C5]Evens, B., Latafat, P., Themelis, A., Suykens, J., Patrinos, P. “Neural network training as an optimal control problem: An augmented Lagrangian approach,” CDC 2021, pp. 5136–5143
• [C6]Latafat, P., Patrinos, P. “Multi-agent structured optimization over message-passing architectures with bounded communication delays,” CDC 2018, pp. 1688–1693
• [C7]Latafat, P., Bemporad, A., Patrinos, P. “Plug and play distributed model predictive control with dynamic coupling: A randomized primal-dual proximal algorithm,” ECC 2018, pp. 1160–1165
• [C8]Latafat, P., Stella, L., Patrinos, P. “New primal-dual proximal algorithm for distributed optimization,” CDC 2016, pp. 1959–1964
• [C9]Latafat, P., Palumbo, P., Pepe, P., Kovács, L., Panunzi, S., De Gaetano, A. “An LMI-based controller for the glucose-insulin system,” ECC 2015, pp. 7–12

Contribution to Books

• [B1]Latafat, P., Patrinos, P. “Primal-dual proximal algorithms for structured convex optimization: A unifying framework,” in Large-Scale and Distributed Optimization (Giselsson, P., Rantzer, A., eds.), vol. 2227, Lecture Notes in Mathematics, Springer, pp. 97–120, 2018

PhD Thesis

• [PT]Latafat, P. (2020) “Distributed proximal algorithms for large-scale structured optimization,” Joint PhD, KU Leuven & IMT Lucca (“Excellent” evaluation with Doctor Europaeus certificate)

Teaching

Software

• AdaProx: Julia implementation of the adaptive proximal gradient (adaPGM) and adaptive primal-dual (adaPDM) methods, based on the results in [J4].
• CIAOAlgorithms: Julia implementation of several stochastic and incremental methods for finite-sum minimization, based on the results in [J10].
• AdaBiM: Julia solver for simple bilevel optimization problems, based on the results in [J6].
• ProximalAlgorithms: generic Julia solver for nonsmooth optimization, implementing, among other methods, algorithms from [J13], [J12], and [B1].
• fs-palm: Python implementation of the feasible-start proximal augmented Lagrangian method, based on [P1].

See also JuliaFirstOrder and KUL-OPTEC on GitHub for related packages.

Contact

• Email
• Address IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
• GitHub github.com/pylat
• Google Scholar scholar.google.com
• ORCID 0000-0002-7969-8565
• ResearchGate Profile