Cover Image

Markovian Compression: Looking to the Past Helps Accelerate the Future

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Veprikov, Andrey, Solodkin, Vladimir, Rudakov, Mikhail, Babkin, Petr, Beznosikov, Aleksandr
Format:	Preprint
Published:	2026
Subjects:	Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Methods for Optimization Problems with Markovian Stochasticity and Non-Euclidean Geometry
by: Solodkin, Vladimir, et al.
Published: (2024)

WeightLoRA: Keep Only Necessary Adapters
by: Veprikov, Andrey, et al.
Published: (2025)

Methods for Solving Variational Inequalities with Markovian Stochasticity
by: Solodkin, Vladimir, et al.
Published: (2024)

New Aspects of Black Box Conditional Gradient: Variance Reduction and One Point Feedback
by: Veprikov, Andrey, et al.
Published: (2024)

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

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

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

Where Does Warm-Up Come From? Adaptive Scheduling for Norm-Constrained Optimizers
by: Riabinin, Artem, et al.
Published: (2026)

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

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)

Zero-Order Optimization for LLM Fine-Tuning via Learnable Direction Sampling
by: Parfenov, Valery, et al.
Published: (2026)

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

Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
by: Petrov, Egor, et al.
Published: (2025)

Effective Method with Compression for Distributed and Federated Cocoercive Variational Inequalities
by: Medyakov, Daniil, et al.
Published: (2024)

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)

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

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

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

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

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

Unified Theory of Adaptive Variance Reduction
by: Shestakov, Aleksandr, et al.
Published: (2025)

Accelerated Stochastic ExtraGradient: Mixing Hessian and Gradient Similarity to Reduce Communication in Distributed and Federated Learning
by: Bylinkin, Dmitry, et al.
Published: (2024)

Random-reshuffled SARAH does not need a full gradient computations
by: Beznosikov, Aleksandr, et al.
Published: (2021)

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

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
by: Ustimenko, Aleksei, et al.
Published: (2023)

On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)

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

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
by: Beznosikov, Aleksandr, et al.
Published: (2023)

DyKAF: Dynamical Kronecker Approximation of the Fisher Information Matrix for Gradient Preconditioning
by: Yudin, Nikolay, et al.
Published: (2025)

Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
by: Bylinkin, Dmitry, et al.
Published: (2026)

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

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

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

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

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

Hierarchical Mixture-of-Experts with Two-Stage Optimization
by: Molodtsov, Gleb, et al.
Published: (2026)

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

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

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

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