26 citations · 91 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
On Convergence of Incremental Gradient for Non-Convex Smooth Functions
Anastasia Koloskova, Nikita Doikov, Sebastian U. Stich +1
In machine learning and neural network optimization, algorithms like incremental gradient, and shuffle SGD are popular due to minimizing the number of cache misses and good practic…
Linearization Algorithms for Fully Composite Optimization
Maria-Luiza Vladarean, Nikita Doikov, Martin Jaggi +1
This paper studies first-order algorithms for solving fully composite optimization problems over convex and compact sets. We leverage the structure of the objective by handling its…
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
El Mahdi Chayti, Nikita Doikov, Martin Jaggi
We study stochastic Cubic Newton methods for solving general possibly non-convex minimization problems. We propose a new framework, which we call the helper framework, that provide…
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…