1 citations · 1 across the 6 of their papers we have counts for
6 papers
Spectral Preconditioning for Gradient Methods on Graded Non-convex Functions
Nikita Doikov, Sebastian U. Stich, Martin Jaggi
The performance of optimization methods is often tied to the spectrum of the objective Hessian. Yet, conventional assumptions, such as smoothness, do often not enable us to make fi…
First and zeroth-order implementations of the regularized Newton method with lazy approximated Hessians
Nikita Doikov, Geovani Nunes Grapiglia
In this work, we develop first-order (Hessian-free) and zero-order (derivative-free) implementations of the Cubically regularized Newton method for solving general non-convex optim…
Minimizing Quasi-Self-Concordant Functions by Gradient Regularization of Newton Method
Nikita Doikov
We study the composite convex optimization problems with a Quasi-Self-Concordant smooth component. This problem class naturally interpolates between classic Self-Concordant functio…
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…
Super-Universal Regularized Newton Method
Nikita Doikov, Konstantin Mishchenko, Yurii Nesterov
We analyze the performance of a variant of Newton method with quadratic regularization for solving composite convex minimization problems. At each step of our method, we choose reg…
Gradient Regularization of Newton Method with Bregman Distances
Nikita Doikov, Yurii Nesterov
In this paper, we propose a first second-order scheme based on arbitrary non-Euclidean norms, incorporated by Bregman distances. They are introduced directly in the Newton iterate…