A. Rogozin

Parameter-Free Zeroth-Order Optimization with Ellipsoidal Sampling

Alexander Sholokhov, Alexander Rogozin

Zeroth-order optimization methods are essential for solving black-box problems where gradient information is unavailable or expensive to compute. This paper presents POEM-ES, a nov…

Accelerated Consensus and Decentralized Optimization over Slowly Varying Networks

Darina Dvinskikh, Anton Novitskii, Demyan Yarmoshik +3

We study average consensus over slowly time-varying networks and its application to decentralized optimization. We introduce WAVE, a windowed Chebyshev method that limits the accum…

Decentralized Optimization with Cross-Coupled Mixed Affine Constraints

Ewsey Obzherin, Ilya Khomchenko, Natalia Shelegeda +4

We study decentralized optimization with cross-coupled mixed affine constraints, where local and shared variables interact through two affine channels. We show that the intrinsic d…

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…

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…

Decentralized Optimization with Mixed Affine Constraints

Demyan Yarmoshik, Nhat Trung Nguyen, Alexander Rogozin +1

This paper considers decentralized optimization of convex functions with mixed affine equality constraints involving both local and global variables. Constraints on global variable…

math.OC2025

Dual Smoothing for Decentralized Optimization

Alexander Rogozin, Nhat Trung Nguyen, Hamed Azami Zenuzagh +1

Decentralized optimization is widely used in different fields of study such as distributed learning, signal processing, and various distributed control problems. In these types of…

math.OC2025

Robustifying networks for flow problems against edge failure

Artyom Klyuchikov, Roland Hildebrand, Sergei Protasov +2

We consider the robust version of a multi-commodity network flow problem. The robustness is defined with respect to the deletion, or failure, of edges. While the flow problem itsel…

Average-case optimization analysis for distributed consensus algorithms on regular graphs

Nhat Trung Nguyen, Alexander Rogozin, Alexander Gasnikov

The consensus problem in distributed computing involves a network of agents aiming to compute the average of their initial vectors through local communication, represented by an un…

Decentralized Optimization with Coupled Constraints

Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev +3

We consider the decentralized minimization of a separable objective $\sum_{i = 1}^{n} f_{i} (x_{i})$ , where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\math…

Delay-tolerant distributed Bregman proximal algorithms

S. Chraibi, F. Iutzeler, J. Malick +1

Many problems in machine learning write as the minimization of a sum of individual loss functions over the training examples. These functions are usually differentiable but, in som…

Decentralized Finite-Sum Optimization over Time-Varying Networks

Dmitry Metelev, Savelii Chezhegov, Alexander Rogozin +4

We consider decentralized time-varying stochastic optimization problems where each of the functions held by the nodes has a finite sum structure. Such problems can be efficiently s…

math.OC2023★ 6 cited

Decentralized Optimization Over Slowly Time-Varying Graphs: Algorithms and Lower Bounds

Dmitry Metelev, Aleksandr Beznosikov, Alexander Rogozin +2

We consider a decentralized convex unconstrained optimization problem, where the cost function can be decomposed into a sum of strongly convex and smooth functions, associated with…

Decentralized conditional gradient method over time-varying graphs

Roman Vedernikov, Alexander Rogozin, Alexander Gasnikov

In this paper we study a generalization of distributed conditional gradient method to time-varying network architectures. We theoretically analyze convergence properties of the alg…

Decentralized optimization with affine constraints over time-varying networks

Demyan Yarmoshik, Alexander Rogozin, Alexander Gasnikov

The decentralized optimization paradigm assumes that each term of a finite-sum objective is privately stored by the corresponding agent. Agents are only allowed to communicate with…

Decentralized Proximal Optimization Method with Consensus Procedure

Alexander Rogozin, Anton Novitskii, Alexander Gasnikov

Decentralized optimization is well studied for smooth unconstrained problems. However, constrained problems or problems with composite terms are an open direction for research. We…