26 citations · 91 across the 27 of their papers we have counts for
5 papers · 2 filters
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…
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…