Bregman Proximal Method for Efficient Communications under Similarity
Fuente:
arXiv
Saved in:
| Main Authors: | Beznosikov, Aleksandr, Dvinskikh, Darina, Bylinkin, Dmitry, Semenov, Andrei, Gasnikov, Alexander |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
by: Bylinkin, Dmitry, et al.
Published: (2024)
by: Bylinkin, Dmitry, et al.
Published: (2024)
Decentralized Distributed Optimization for Saddle Point Problems
by: Rogozin, Alexander, et al.
Published: (2021)
by: Rogozin, Alexander, et al.
Published: (2021)
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)
by: Bylinkin, Dmitry, et al.
Published: (2024)
Randomized gradient-free methods in convex optimization
by: Gasnikov, Alexander, et al.
Published: (2022)
by: Gasnikov, Alexander, et al.
Published: (2022)
About some works of Boris Polyak on convergence of gradient methods and their development
by: Ablaev, Seydamet, et al.
Published: (2023)
by: Ablaev, Seydamet, et al.
Published: (2023)
Gradient-free algorithm for saddle point problems under overparametrization
by: Statkevich, Ekaterina, et al.
Published: (2024)
by: Statkevich, Ekaterina, et al.
Published: (2024)
Adaptive Regularized Newton Method with Inexact Hessian
by: Shestakov, Aleksandr, et al.
Published: (2025)
by: Shestakov, Aleksandr, et al.
Published: (2025)
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
by: Bychkov, Georgii, et al.
Published: (2024)
by: Bychkov, Georgii, et al.
Published: (2024)
Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
by: Bylinkin, Dmitry, et al.
Published: (2026)
by: Bylinkin, Dmitry, et al.
Published: (2026)
Optimal Analysis of Method with Batching for Monotone Stochastic Finite-Sum Variational Inequalities
by: Pichugin, Alexander, et al.
Published: (2024)
by: Pichugin, Alexander, et al.
Published: (2024)
Enhancing Stability of Physics-Informed Neural Network Training Through Saddle-Point Reformulation
by: Bylinkin, Dmitry, et al.
Published: (2025)
by: Bylinkin, Dmitry, et al.
Published: (2025)
Accelerated Methods with Compression for Horizontal and Vertical Federated Learning
by: Stanko, Sergey, et al.
Published: (2024)
by: Stanko, Sergey, et al.
Published: (2024)
Local SGD for Near-Quadratic Problems: Improving Convergence under Unconstrained Noise Conditions
by: Sadchikov, Andrey, et al.
Published: (2024)
by: Sadchikov, Andrey, et al.
Published: (2024)
Stochastic Optimization and Data Science
by: Avetisyan, Arutyun, et al.
Published: (2026)
by: Avetisyan, Arutyun, et al.
Published: (2026)
Method with Batching for Stochastic Finite-Sum Variational Inequalities in Non-Euclidean Setting
by: Pichugin, Alexander, et al.
Published: (2024)
by: Pichugin, Alexander, et al.
Published: (2024)
Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
by: Prokhorov, Boris, et al.
Published: (2026)
by: Prokhorov, Boris, et al.
Published: (2026)
Accelerated Stochastic Gradient Method with Applications to Consensus Problem in Markov-Varying Networks
by: Solodkin, Vladimir, et al.
Published: (2024)
by: Solodkin, Vladimir, et al.
Published: (2024)
Accelerated Zero-Order SGD Method for Solving the Black Box Optimization Problem under "Overparametrization" Condition
by: Lobanov, Aleksandr, et al.
Published: (2023)
by: Lobanov, Aleksandr, et al.
Published: (2023)
Extragradient Sliding for Composite Non-Monotone Variational Inequalities
by: Emelyanov, Roman, et al.
Published: (2024)
by: Emelyanov, Roman, et al.
Published: (2024)
Decentralized Finite-Sum Optimization over Time-Varying Networks
by: Metelev, Dmitry, et al.
Published: (2024)
by: Metelev, Dmitry, et al.
Published: (2024)
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
by: Kornilov, Nikita, et al.
Published: (2025)
by: Kornilov, Nikita, et al.
Published: (2025)
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
by: Chezhegov, Savelii, et al.
Published: (2024)
by: Chezhegov, Savelii, et al.
Published: (2024)
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
by: Beznosikov, Aleksandr, et al.
Published: (2020)
by: Beznosikov, Aleksandr, et al.
Published: (2020)
Optimal Data Splitting in Distributed Optimization for Machine Learning
by: Medyakov, Daniil, et al.
Published: (2024)
by: Medyakov, Daniil, et al.
Published: (2024)
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
by: Nazykov, Ruslan, et al.
Published: (2024)
by: Nazykov, Ruslan, et al.
Published: (2024)
Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values
by: Lobanov, Aleksandr, et al.
Published: (2024)
by: Lobanov, Aleksandr, et al.
Published: (2024)
One-Point Feedback for Composite Optimization with Applications to Distributed and Federated Learning
by: Beznosikov, Aleksandr, et al.
Published: (2021)
by: Beznosikov, Aleksandr, et al.
Published: (2021)
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Power of Generalized Smoothness in Stochastic Convex Optimization: First- and Zero-Order Algorithms
by: Lobanov, Aleksandr, et al.
Published: (2025)
by: Lobanov, Aleksandr, et al.
Published: (2025)
The Black-Box Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation
by: Lobanov, Aleksandr, et al.
Published: (2023)
by: Lobanov, Aleksandr, et al.
Published: (2023)
Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under $(L_0,L_1)$-Smoothness
by: Lobanov, Aleksandr, et al.
Published: (2024)
by: Lobanov, Aleksandr, et al.
Published: (2024)
Activations and Gradients Compression for Model-Parallel Training
by: Rudakov, Mikhail, et al.
Published: (2024)
by: Rudakov, Mikhail, et al.
Published: (2024)
Nesterov's method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square
by: Chervonenkis, Boris, et al.
Published: (2024)
by: Chervonenkis, Boris, et al.
Published: (2024)
Inertial Bregman Proximal Gradient under Partial Smoothness
by: Godeme, Jean-Jacques
Published: (2025)
by: Godeme, Jean-Jacques
Published: (2025)
Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
by: Medyakov, Daniil, et al.
Published: (2024)
by: Medyakov, Daniil, et al.
Published: (2024)
Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry
by: Solodkin, Vladimir, et al.
Published: (2024)
by: Solodkin, Vladimir, et al.
Published: (2024)
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
by: Suvorikova, Alexandra, et al.
Published: (2026)
by: Suvorikova, Alexandra, et al.
Published: (2026)
Unified Theory of Adaptive Variance Reduction
by: Shestakov, Aleksandr, et al.
Published: (2025)
by: Shestakov, Aleksandr, et al.
Published: (2025)
OPTAMI: Global Superlinear Convergence of High-order Methods
by: Kamzolov, Dmitry, et al.
Published: (2024)
by: Kamzolov, Dmitry, et al.
Published: (2024)
Similar Items
-
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
by: Bylinkin, Dmitry, et al.
Published: (2024) -
Decentralized Distributed Optimization for Saddle Point Problems
by: Rogozin, Alexander, et al.
Published: (2021) -
Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024) -
Randomized gradient-free methods in convex optimization
by: Gasnikov, Alexander, et al.
Published: (2022) -
About some works of Boris Polyak on convergence of gradient methods and their development
by: Ablaev, Seydamet, et al.
Published: (2023)