81 citations · 376 across the 46 of their papers we have counts for
48 papers
An Optimal Second-Order Algorithm for Finite-Sum Optimization under Average Smoothness and Convexity
Artem Tsedenov, Dmitry Kovalev
In this paper, we consider a finite-sum optimization problem, where the objective function is convex, the component functions are twice continuously differentiable and their He…
Convex-Concave Interpolation and Application of PEP to Bilinear-Coupled Saddle-Point Problem
Valery Krivchenko, Alexander Gasnikov, Dmitry Kovalev
The Performance estimation problem (PEP) approach reformulates finding the exact worst-case performance of an algorithm as the solution to an optimization problem. Tractable formul…
Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
Dmitry Kovalev
Much of the existing theory on first-order non-smooth optimization is built on a restrictive assumption that the gradients of the objective function are uniformly bounded. We intro…
Optimal Projection-Free Adaptive SGD for Matrix Optimization
Dmitry Kovalev
Recently, Jiang et al. [2026] developed Leon, a practical variant of One-sided Shampoo [Xie et al., 2025a, An et al., 2025] algorithm for online convex optimization, which does not…
Muon is Provably Faster with Momentum Variance Reduction
Xun Qian, Hussein Rammal, Dmitry Kovalev +1
Recent empirical research has demonstrated that deep learning optimizers based on the linear minimization oracle (LMO) over specifically chosen Non-Euclidean norm balls, such as Mu…
Understanding Gradient Orthogonalization for Deep Learning via Non-Euclidean Trust-Region Optimization
Dmitry Kovalev
Optimization with matrix gradient orthogonalization has recently demonstrated impressive results in the training of deep neural networks (Jordan et al., 2024; Liu et al., 2025). In…