Showing math.OCShow all
3 papers · 1 filter
math.OC2025
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
Artem Agafonov, Vladislav Ryspayev, Samuel Horváth +3
Quasi-Newton methods are widely used for solving convex optimization problems due to their ease of implementation, practical efficiency, and strong local convergence guarantees. Ho…
math.OC2025
Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis
Ruichen Luo, Sebastian U Stich, Samuel Horváth +1
LocalSGD and SCAFFOLD are widely used methods in distributed stochastic optimization, with numerous applications in machine learning, large-scale data processing, and federated lea…
math.OC2024
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…