2 citations · 2 across the 4 of their papers we have counts for
7 papers
Lower Complexity Bounds for Minimizing Regularized Functions
Nikita Doikov
In this paper, we establish lower bounds for the oracle complexity of the first-order methods minimizing regularized convex functions. We consider the composite representation of t…
Optimization Methods for Fully Composite Problems
Nikita Doikov, Yurii Nesterov
In this paper, we propose a new Fully Composite Formulation of convex optimization problems. It includes, as a particular case, the problems with functional constraints, max-type m…
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…
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…
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…
Inexact Tensor Methods with Dynamic Accuracies
Nikita Doikov, Yurii Nesterov
In this paper, we study inexact high-order Tensor Methods for solving convex optimization problems with composite objective. At every step of such methods, we use approximate solut…