8 papers
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…
Decentralized Optimization with Coupled Constraints
Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev +3
We consider the decentralized minimization of a separable objective , where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\math…
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…
On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients
Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6
First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…