High-Probability Convergence Theory for Distributed Composite Optimization with Sub-Weibull Noises
Fuente:
arXiv
Saved in:
| Main Authors: | Yu, Zhan, Shi, Zhongjie, Yuan, Deming |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Distributed Stochastic Optimization under Heavy-Tailed Noise: A Federated Mirror Descent Approach with High Probability Convergence
by: Yu, Zhan, et al.
Published: (2025)
by: Yu, Zhan, et al.
Published: (2025)
Generic Frameworks for Distributed Functional Optimization and Learning over Time-Varying Networks
by: Yu, Zhan, et al.
Published: (2025)
by: Yu, Zhan, et al.
Published: (2025)
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
by: Madden, Liam, et al.
Published: (2020)
by: Madden, Liam, et al.
Published: (2020)
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2023)
by: Gorbunov, Eduard, et al.
Published: (2023)
High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
by: Yang, Yuchen, et al.
Published: (2025)
by: Yang, Yuchen, et al.
Published: (2025)
Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization
by: Zhang, Wentao, et al.
Published: (2023)
by: Zhang, Wentao, et al.
Published: (2023)
Online Stochastic Gradient Methods Under Sub-Weibull Noise and the Polyak-Łojasiewicz Condition
by: Kim, Seunghyun, et al.
Published: (2021)
by: Kim, Seunghyun, et al.
Published: (2021)
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
by: Bastianello, Nicola, et al.
Published: (2021)
by: Bastianello, Nicola, et al.
Published: (2021)
Feedback-Based Optimization with Sub-Weibull Gradient Errors and Intermittent Updates
by: Ospina, Ana M., et al.
Published: (2021)
by: Ospina, Ana M., et al.
Published: (2021)
Distributed Online Stochastic Convex-Concave Optimization: Dynamic Regret Analyses under Single and Multiple Consensus Steps
by: Zhang, Wentao, et al.
Published: (2025)
by: Zhang, Wentao, et al.
Published: (2025)
Online Distributed Optimization with Clipped Stochastic Gradients: High Probability Bound of Regrets
by: Yang, Yuchen, et al.
Published: (2024)
by: Yang, Yuchen, et al.
Published: (2024)
Distributed Stochastic Optimization under Heavy-Tailed Noises
by: Sun, Chao, et al.
Published: (2023)
by: Sun, Chao, et al.
Published: (2023)
High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking
by: Armacki, Aleksandar, et al.
Published: (2026)
by: Armacki, Aleksandar, et al.
Published: (2026)
High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2021)
by: Gorbunov, Eduard, et al.
Published: (2021)
Enhancing Exploration in Global Optimization by Noise Injection in the Probability Measures Space
by: Serré, Gaëtan, et al.
Published: (2026)
by: Serré, Gaëtan, et al.
Published: (2026)
Distributed Optimization Algorithm with Superlinear Convergence Rate
by: Xu, Yeming, et al.
Published: (2024)
by: Xu, Yeming, et al.
Published: (2024)
Heterogeneous Stochastic Momentum ADMM for Distributed Nonconvex Composite Optimization
by: Zhang, Yangming, et al.
Published: (2026)
by: Zhang, Yangming, et al.
Published: (2026)
Distributed Stochastic Block Coordinate Descent for Time-Varying Multi-Agent Optimization
by: Yu, Zhan, et al.
Published: (2019)
by: Yu, Zhan, et al.
Published: (2019)
Convergence Analysis of EXTRA in Non-convex Distributed Optimization
by: Qin, Lei, et al.
Published: (2025)
by: Qin, Lei, et al.
Published: (2025)
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
by: Karakai, Aron, et al.
Published: (2025)
by: Karakai, Aron, et al.
Published: (2025)
Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak--$Ł$ojasiewicz Condition
by: Xu, Lei, et al.
Published: (2022)
by: Xu, Lei, et al.
Published: (2022)
Distributed Adaptive Time-Varying Optimization with Global Asymptotic Convergence
by: Jiang, Liangze, et al.
Published: (2024)
by: Jiang, Liangze, et al.
Published: (2024)
General Distribution Steering: A Sub-Optimal Solution by Convex Optimization
by: Wu, Guangyu, et al.
Published: (2023)
by: Wu, Guangyu, et al.
Published: (2023)
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
by: Kar, Avik, et al.
Published: (2026)
by: Kar, Avik, et al.
Published: (2026)
High-Probability Convergence Guarantees of Decentralized SGD
by: Armacki, Aleksandar, et al.
Published: (2025)
by: Armacki, Aleksandar, et al.
Published: (2025)
Distributed Nonconvex Optimization with Double Privacy Protection and Exact Convergence
by: Ou, Zichong, et al.
Published: (2025)
by: Ou, Zichong, et al.
Published: (2025)
Nonsmooth Nonconvex-Concave Minimax Optimization: Convergence Criteria and Algorithms
by: Shi, Jinyang, et al.
Published: (2026)
by: Shi, Jinyang, et al.
Published: (2026)
An Axiomatic Analysis of Distributionally Robust Optimization with $q$-Norm Ambiguity Sets for Probability Smoothing
by: Izunaga, Yoichi, et al.
Published: (2025)
by: Izunaga, Yoichi, et al.
Published: (2025)
Perturbed Proximal Gradient ADMM for Nonconvex Composite Optimization
by: Zhou, Yuan, et al.
Published: (2025)
by: Zhou, Yuan, et al.
Published: (2025)
A Convex Optimization Approach to Model-Free Inverse Optimal Control with Provable Convergence
by: Yu, Meiling, et al.
Published: (2025)
by: Yu, Meiling, et al.
Published: (2025)
Differentially Private Clipped-SGD: High-Probability Convergence with Arbitrary Clipping Level
by: Khah, Saleh Vatan, et al.
Published: (2025)
by: Khah, Saleh Vatan, et al.
Published: (2025)
Negative Curvature Methods with High-Probability Complexity Guarantees for Stochastic Nonconvex Optimization
by: Berahas, Albert S., et al.
Published: (2026)
by: Berahas, Albert S., et al.
Published: (2026)
Distributed Nonconvex Optimization with Exponential Convergence Rate via Hybrid Systems Methods
by: Hendrickson, Katherine R., et al.
Published: (2025)
by: Hendrickson, Katherine R., et al.
Published: (2025)
ADMM for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2024)
by: Yuan, Ganzhao
Published: (2024)
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
Convergence of Distributed Adaptive Optimization with Local Updates
by: Cheng, Ziheng, et al.
Published: (2024)
by: Cheng, Ziheng, et al.
Published: (2024)
Block Coordinate Descent Methods for Structured Nonconvex Optimization with Nonseparable Constraints: Optimality Conditions and Global Convergence
by: Yuan, Zhijie, et al.
Published: (2024)
by: Yuan, Zhijie, et al.
Published: (2024)
Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence
by: Yu, Shuhua, et al.
Published: (2025)
by: Yu, Shuhua, et al.
Published: (2025)
On Linear Convergence of Distributed Stochastic Bilevel Optimization over Undirected Networks via Gradient Aggregation
by: Tak, Ajay, et al.
Published: (2025)
by: Tak, Ajay, et al.
Published: (2025)
From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)
by: Xie, Shengping, et al.
Published: (2025)
Similar Items
-
Distributed Stochastic Optimization under Heavy-Tailed Noise: A Federated Mirror Descent Approach with High Probability Convergence
by: Yu, Zhan, et al.
Published: (2025) -
Generic Frameworks for Distributed Functional Optimization and Learning over Time-Varying Networks
by: Yu, Zhan, et al.
Published: (2025) -
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise
by: Madden, Liam, et al.
Published: (2020) -
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
by: Gorbunov, Eduard, et al.
Published: (2023) -
High Probability Convergence of Distributed Clipped Stochastic Gradient Descent with Heavy-tailed Noise
by: Yang, Yuchen, et al.
Published: (2025)