500 citations · 1.2k across the 44 of their papers we have counts for
9 papers · 1 filter
Affine-invariant contracting-point methods for Convex Optimization
Nikita Doikov, Yurii Nesterov
In this paper, we develop new affine-invariant algorithms for solving composite convex minimization problems with bounded domain. We present a general framework of Contracting-Poin…
Online analysis of epidemics with variable infection rate
Yurii Nesterov
In this paper, we continue development of the new epidemiological model HIT, which is suitable for analyzing and predicting the propagation of COVID-19 epidemics. This is a discret…
Convex optimization based on global lower second-order models
Nikita Doikov, Yurii Nesterov
In this paper, we present new second-order algorithms for composite convex optimization, called Contracting-domain Newton methods. These algorithms are affine-invariant and based o…
New Results on Superlinear Convergence of Classical Quasi-Newton Methods
Anton Rodomanov, Yurii Nesterov
We present a new theoretical analysis of local superlinear convergence of classical quasi-Newton methods from the convex Broyden class. As a result, we obtain a significant improve…
Rates of superlinear convergence for classical quasi-Newton methods
Anton Rodomanov, Yurii Nesterov
We study the local convergence of classical quasi-Newton methods for nonlinear optimization. Although it was well established a long time ago that asymptotically these methods conv…
Stochastic Subspace Cubic Newton Method
Filip Hanzely, Nikita Doikov, Peter Richtárik +1
In this paper, we propose a new randomized second-order optimization algorithm---Stochastic Subspace Cubic Newton (SSCN)---for minimizing a high dimensional convex function . Ou…