Image de couverture de livre

Improved Convergence for Decentralized Stochastic Optimization with Biased Gradients

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Xu, Qing, Liao, Yiwei, Fan, Wenqi, You, Xingxing, Dian, Songyi
Format:	Preprint
Publié:	2026
Sujets:	Optimization and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Stochastic Momentum Tracking Push-Pull for Decentralized Optimization over Directed Graphs
par: Fan, Wenqi, et autres
Publié: (2026)

High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
par: Armacki, Aleksandar, et autres
Publié: (2026)

Stochastic Push-Pull for Decentralized Nonconvex Optimization
par: You, Runze, et autres
Publié: (2025)

Decentralized Min-Max Optimization with Gradient Tracking
par: You, Runze, et autres
Publié: (2025)

On the Convergence of Decentralized Stochastic Gradient-Tracking with Finite-Time Consensus
par: Fainman, Aaron, et autres
Publié: (2025)

Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization
par: Zhang, Xuan, et autres
Publié: (2023)

A Robust Compressed Push-Pull Method for Decentralized Nonconvex Optimization
par: Liao, Yiwei, et autres
Publié: (2024)

Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
par: Liu, Wei, et autres
Publié: (2025)

Convergence Analysis of Stochastic Accelerated Gradient Methods for Generalized Smooth Optimizations
par: Yu, Chenhao, et autres
Publié: (2025)

CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence
par: Huang, Kun, et autres
Publié: (2023)

Global Convergence and Error Propagation in Neural Gradient Flows: A Riemannian Optimization Framework
par: Zheng, Shixin, et autres
Publié: (2026)

Convergence of the Iterates of the Stochastic Proximal Gradient Method
par: Madariaga, Javier I.
Publié: (2026)

Optimal Gradient Tracking for Decentralized Optimization
par: Song, Zhuoqing, et autres
Publié: (2021)

Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
par: Li, Tianyou, et autres
Publié: (2023)

Multi-level Monte-Carlo Gradient Methods for Stochastic Optimization with Biased Oracles
par: Hu, Yifan, et autres
Publié: (2024)

On Linear Convergence of Distributed Stochastic Bilevel Optimization over Undirected Networks via Gradient Aggregation
par: Tak, Ajay, et autres
Publié: (2025)

A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
par: Wang, Lei, et autres
Publié: (2022)

Stochastic Optimization Algorithms for Problems with Controllable Biased Oracles
par: Liu, Yin, et autres
Publié: (2023)

Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity
par: Li, Qiang, et autres
Publié: (2024)

Enhancing Convergence of Decentralized Gradient Tracking under the KL Property
par: Chen, Xiaokai, et autres
Publié: (2024)

Learning Provably Improves the Convergence of Gradient Descent
par: Song, Qingyu, et autres
Publié: (2025)

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
par: Jiang, Zhanhong, et autres
Publié: (2025)

Policy Gradient Methods for the Cost-Constrained LQR: Strong Duality and Global Convergence
par: Zhao, Feiran, et autres
Publié: (2024)

On the Optimal Time Complexities in Decentralized Stochastic Asynchronous Optimization
par: Tyurin, Alexander, et autres
Publié: (2024)

Improved Performance of Stochastic Gradients with Gaussian Smoothing
par: Starnes, Andrew, et autres
Publié: (2023)

Asynchronous Decentralized Optimization with Constraints: Achievable Speeds of Convergence for Directed Graphs
par: Shahriari-Mehr, Firooz, et autres
Publié: (2024)

Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction and Non-Asymptotic Convergence Rates
par: Hu, Jinhui, et autres
Publié: (2023)

Decentralized Optimization over Time-Varying Row-Stochastic Digraphs
par: Liang, Liyuan, et autres
Publié: (2025)

On the Convergence and Complexity of the Stochastic Central Finite-Difference Based Gradient Estimation Methods
par: Bollapragada, Raghu, et autres
Publié: (2025)

Convergence of Policy Gradient for Stochastic Linear-Quadratic Control Problem in Infinite Horizon
par: Zhang, Xinpei, et autres
Publié: (2024)

Convergence of Decentralized Stochastic Subgradient-based Methods for Nonsmooth Nonconvex functions
par: Zhang, Siyuan, et autres
Publié: (2024)

Dual Averaging Converges for Nonconvex Smooth Stochastic Optimization
par: Liu, Tuo, et autres
Publié: (2025)

Universal Gradient Methods for Stochastic Convex Optimization
par: Rodomanov, Anton, et autres
Publié: (2024)

Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
par: Nguyen, Hoang Huy, et autres
Publié: (2024)

Intrinsic Decentralized Stochastic Riemannian Optimization on Manifolds with Bounded Sectional Curvature
par: Nguyen, Duc Toan, et autres
Publié: (2026)

Decentralized Gradient-Free Methods for Stochastic Non-Smooth Non-Convex Optimization
par: Lin, Zhenwei, et autres
Publié: (2023)

Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods
par: Liu, Zijian, et autres
Publié: (2023)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
par: Liu, Zijian, et autres
Publié: (2025)

High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
par: Yang, Yuchen, et autres
Publié: (2025)

The Anytime Convergence of Stochastic Gradient Descent with Momentum: From a Continuous-Time Perspective
par: Feng, Yasong, et autres
Publié: (2023)