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math.OC2024
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
Anton Rodomanov, Xiaowen Jiang, Sebastian Stich
We present adaptive gradient methods (both basic and accelerated) for solving convex composite optimization problems in which the main part is approximately smooth (a.k.a. …
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…
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…