Image de couverture de livre

A Single-Loop Algorithm for Decentralized Bilevel Optimization

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Dong, Youran, Ma, Shiqian, Yang, Junfeng, Yin, Chao
Format:	Preprint
Publié:	2023
Sujets:	Optimization and Control Distributed, Parallel, and Cluster Computing Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

S$^3$LDBO: A Snapshot Single-Loop Algorithm for Decentralized Bilevel Optimization
par: Yin, Chao, et autres
Publié: (2026)

A Penalty-Based Method for Communication-Efficient Decentralized Bilevel Programming
par: Nazari, Parvin, et autres
Publié: (2022)

Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization
par: Li, Jiaxiang, et autres
Publié: (2024)

A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization
par: Wen, Min, et autres
Publié: (2024)

Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
par: Li, Junyi, et autres
Publié: (2023)

A Bias-Correction Decentralized Stochastic Gradient Algorithm with Momentum Acceleration
par: Hu, Yuchen, et autres
Publié: (2025)

A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks
par: Yau, Chung-Yiu, et autres
Publié: (2024)

A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints
par: Wang, Lei, et autres
Publié: (2024)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
par: Wang, He, et autres
Publié: (2023)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
par: He, Yutong, et autres
Publié: (2023)

Decentralized Directed Collaboration for Personalized Federated Learning
par: Liu, Yingqi, et autres
Publié: (2024)

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
par: Qin, Zhen, et autres
Publié: (2023)

Decentralized Personalized Federated Learning for Min-Max Problems
par: Borodich, Ekaterina, et autres
Publié: (2021)

Energy-efficient Decentralized Learning via Graph Sparsification
par: Zhang, Xusheng, et autres
Publié: (2024)

Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
par: Zhou, Yuan, et autres
Publié: (2025)

Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering
par: Zehtabi, Shahryar, et autres
Publié: (2022)

Time-varying Mixing Matrix Design for Energy-efficient Decentralized Federated Learning
par: Zhang, Xusheng, et autres
Publié: (2025)

Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps
par: Yang, Junchi, et autres
Publié: (2024)

Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms
par: Beznosikov, Aleksandr, et autres
Publié: (2020)

Efficient Adaptive Federated Optimization
par: Lee, Su Hyeong, et autres
Publié: (2024)

High-Performance Hybrid Algorithm for Minimum Sum-of-Squares Clustering of Infinitely Tall Data
par: Mussabayev, Ravil, et autres
Publié: (2023)

Decentralized Distributed Optimization for Saddle Point Problems
par: Rogozin, Alexander, et autres
Publié: (2021)

Decentralized Nonsmooth Nonconvex Optimization with Client Sampling
par: Chen, Xinyan, et autres
Publié: (2026)

CONGO: Compressive Online Gradient Optimization
par: Carleton, Jeremy, et autres
Publié: (2024)

A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
par: Wang, Jing, et autres
Publié: (2025)

On Principled Local Optimization Methods for Federated Learning
par: Yuan, Honglin
Publié: (2024)

Optimizing Stochastic Gradient Push under Broadcast Communications
par: Nguyen, Tuan, et autres
Publié: (2026)

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
par: Shi, Xinli, et autres
Publié: (2025)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
par: He, Yutong, et autres
Publié: (2023)

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
par: Huang, Yan, et autres
Publié: (2024)

AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
par: Yue, Yun, et autres
Publié: (2023)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
par: Bylinkin, Dmitry, et autres
Publié: (2024)

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
par: Mahran, Ammar, et autres
Publié: (2026)

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
par: Tyurin, Alexander
Publié: (2025)

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
par: Tovmasyan, Zhirayr, et autres
Publié: (2026)

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

Decentralized Nonconvex Optimization under Heavy-Tailed Noise: Normalization and Optimal Convergence
par: Yu, Shuhua, et autres
Publié: (2025)

The Privacy Power of Correlated Noise in Decentralized Learning
par: Allouah, Youssef, et autres
Publié: (2024)

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

Smoothed Gradient Clipping and Error Feedback for Decentralized Optimization under Symmetric Heavy-Tailed Noise
par: Yu, Shuhua, et autres
Publié: (2023)