19 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…
Sliding Methods for Hölder-Smooth Convex--Concave Minimax Optimization with Bilinear Coupling
Nhat Trung Nguyen, Alexander Gasnikov
We study convex-concave minimax optimization problems with bilinear coupling of the form w…
Decentralized Inexact Cubic Newton Method with Consensus Procedure
Artem Agafonov, Anton Novitskii, Alexander Rogozin +5
Distributed optimization is widely used in large-scale and privacy-preserving machine learning, where each agent stores a local objective and communicates only with its neighbors i…
Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
Maxim Divilkovskiy, Alexander Gasnikov
We study non-smooth stochastic decentralized optimization problems over time-varying networks, where objective functions are distributed across nodes and network connections may in…
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…
Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
Aleksandr Beznosikov, Georgiy Kormakov, Alexander Grigorievskiy +7
The objective of Vertical Federated Learning (VFL) is to collectively train a model using features available on different devices while sharing the same users. This paper focuses o…