Imagen de Portada

Decentralized Finite-Sum Optimization over Time-Varying Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Metelev, Dmitry, Chezhegov, Savelii, Rogozin, Alexander, Beznosikov, Aleksandr, Sholokhov, Alexander, Gasnikov, Alexander, Kovalev, Dmitry
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
por: Solodkin, Vladimir, et al.
Publicado: (2024)

Decentralized Distributed Optimization for Saddle Point Problems
por: Rogozin, Alexander, et al.
Publicado: (2021)

Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
por: Sadchikov, Andrey, et al.
Publicado: (2024)

Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
por: Kovalev, Dmitry, et al.
Publicado: (2024)

Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
por: Bylinkin, Dmitry, et al.
Publicado: (2025)

Decentralized Optimization with Coupled Constraints
por: Yarmoshik, Demyan, et al.
Publicado: (2024)

Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
por: Pichugin, Alexander, et al.
Publicado: (2024)

Method with Batching for Stochastic Finite-Sum Variational Inequalities in Non-Euclidean Setting
por: Pichugin, Alexander, et al.
Publicado: (2024)

Convergence of Clipped-SGD for Convex $(L_0,L_1)$-Smooth Optimization with Heavy-Tailed Noise
por: Chezhegov, Savelii, et al.
Publicado: (2025)

Incorporating Preconditioning into Accelerated Approaches: Theoretical Guarantees and Practical Improvement
por: Trifonov, Stepan, et al.
Publicado: (2025)

Stochastic Decentralized Optimization of Non-Smooth Convex and Convex-Concave Problems over Time-Varying Networks
por: Divilkovskiy, Maxim, et al.
Publicado: (2025)

Decentralized Optimization with Mixed Affine Constraints
por: Yarmoshik, Demyan, et al.
Publicado: (2026)

Dual Smoothing for Decentralized Optimization
por: Rogozin, Alexander, et al.
Publicado: (2025)

Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
por: Chezhegov, Savelii, et al.
Publicado: (2024)

Strengthening the finite characterizations of smooth min-max games
por: Krivchenko, Valery, et al.
Publicado: (2026)

Bregman Proximal Method for Efficient Communications under Similarity
por: Beznosikov, Aleksandr, et al.
Publicado: (2023)

Decentralized Inexact Cubic Newton Method with Consensus Procedure
por: Agafonov, Artem, et al.
Publicado: (2026)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
por: Medyakov, Daniil, et al.
Publicado: (2025)

Adaptive Regularized Newton Method with Inexact Hessian
por: Shestakov, Aleksandr, et al.
Publicado: (2025)

Gradient-Type Methods For Decentralized Optimization Problems With Polyak-Łojasiewicz Condition Over Time-Varying Networks
por: Kuruzov, Ilya, et al.
Publicado: (2022)

Optimal Data Splitting in Distributed Optimization for Machine Learning
por: Medyakov, Daniil, et al.
Publicado: (2024)

A Parameter-Free Zeroth-Order Algorithm for Decentralized Stochastic Convex Optimization
por: Chen, Jiawei, et al.
Publicado: (2026)

Extragradient Sliding for Composite Non-Monotone Variational Inequalities
por: Emelyanov, Roman, et al.
Publicado: (2024)

Accelerated Methods with Compression for Horizontal and Vertical Federated Learning
por: Stanko, Sergey, et al.
Publicado: (2024)

The Mirror-Prox Sliding Method for Non-smooth decentralized saddle-point problems
por: Kuruzov, Ilya, et al.
Publicado: (2022)

One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
por: Beznosikov, Aleksandr, et al.
Publicado: (2021)

Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
por: Lobanov, Aleksandr, et al.
Publicado: (2025)

Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
por: Beznosikov, Aleksandr, et al.
Publicado: (2020)

Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
por: Lobanov, Aleksandr, et al.
Publicado: (2023)

Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
por: Prokhorov, Boris, et al.
Publicado: (2026)

Average-case optimization analysis for distributed consensus algorithms on regular graphs
por: Nguyen, Nhat Trung, et al.
Publicado: (2024)

Exploring New Frontiers in Vertical Federated Learning: the Role of Saddle Point Reformulation
por: Beznosikov, Aleksandr, et al.
Publicado: (2026)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
por: Bylinkin, Dmitry, et al.
Publicado: (2024)

Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
por: Nazykov, Ruslan, et al.
Publicado: (2024)

Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
por: Beznosikov, Aleksandr, et al.
Publicado: (2023)

Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
por: Lobanov, Aleksandr, et al.
Publicado: (2024)

The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
por: Lobanov, Aleksandr, et al.
Publicado: (2023)

Activations and Gradients Compression for Model-Parallel Training
por: Rudakov, Mikhail, et al.
Publicado: (2024)

About some works of Boris Polyak on convergence of gradient methods and their development
por: Ablaev, Seydamet, et al.
Publicado: (2023)

Randomized gradient-free methods in convex optimization
por: Gasnikov, Alexander, et al.
Publicado: (2022)