3 papers
math.OC2024
Global Complexity Analysis of BFGS
Anton Rodomanov
In this paper, we present a global complexity analysis of the classical BFGS method with inexact line search, as applied to minimizing a strongly convex function with Lipschitz con…
cs.LG2024
Federated Optimization with Doubly Regularized Drift Correction
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
Federated learning is a distributed optimization paradigm that allows training machine learning models across decentralized devices while keeping the data localized. The standard m…
math.OC2023
Polynomial Preconditioning for Gradient Methods
Nikita Doikov, Anton Rodomanov
We study first-order methods with preconditioning for solving structured nonlinear convex optimization problems. We propose a new family of preconditioners generated by symmetric p…