Imagen de Portada

Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Emmanouilidis, Konstantinos, Vidal, René, Loizou, Nicolas
Formato:	Preprint
Publicado:	2024
Materias:	Optimization and Control Computer Science and Game Theory Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Convergence of Extragradient SVRG for Variational Inequalities: Error Bounds and Increasing Iterate Averaging
por: Nan, Tianlong, et al.
Publicado: (2023)

Expected Variational Inequalities
por: Zhang, Brian Hu, et al.
Publicado: (2025)

A Polynomial-Time Algorithm for Variational Inequalities under the Minty Condition
por: Anagnostides, Ioannis, et al.
Publicado: (2025)

On Separation Between Best-Iterate, Random-Iterate, and Last-Iterate Convergence of Learning in Games
por: Cai, Yang, et al.
Publicado: (2025)

On the Convergence of Min-Max Langevin Dynamics and Algorithm
por: Cai, Yang, et al.
Publicado: (2024)

Convergence of Regret Matching in Potential Games and Constrained Optimization
por: Anagnostides, Ioannis, et al.
Publicado: (2025)

Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms
por: Cai, Yang, et al.
Publicado: (2024)

Last-Iterate Convergence of Adaptive Riemannian Gradient Descent for Equilibrium Computation
por: Cai, Yang, et al.
Publicado: (2023)

An Exponentially Converging Particle Method for the Mixed Nash Equilibrium of Continuous Games
por: Wang, Guillaume, et al.
Publicado: (2022)

Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization
por: Daskalakis, Constantinos, et al.
Publicado: (2018)

From Average-Iterate to Last-Iterate Convergence in Games: A Reduction and Its Applications
por: Cai, Yang, et al.
Publicado: (2025)

Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games
por: Yongacoglu, Bora, et al.
Publicado: (2021)

Stochastic variance reduced extragradient methods for solving hierarchical variational inequalities
por: Dvurechensky, Pavel, et al.
Publicado: (2026)

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures
por: Hu, Ruimeng, et al.
Publicado: (2025)

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

Convergence Analysis of Gradient-Based Learning with Non-Uniform Learning Rates in Non-Cooperative Multi-Agent Settings
por: Chasnov, Benjamin, et al.
Publicado: (2019)

A Variational Inequality Approach to Independent Learning in Static Mean-Field Games
por: Yardim, Batuhan, et al.
Publicado: (2025)

Target Mirror Descent: A Unifying Framework for Solving Monotone Variational Inequalities
por: Chen, Yu-Wen, et al.
Publicado: (2026)

Two-Stage Stochastic Capacity Expansion in Stable Matching under Truthful or Strategic Preference Uncertainty
por: Bazotte, Maria, et al.
Publicado: (2025)

Linear Convergence in Games with Delayed Feedback via Extra Prediction
por: Fujimoto, Yuma, et al.
Publicado: (2026)

A Unified Variational Design of Predictive Mirror Descent in Convex Games under Stochastic Feedback
por: Pan, Yunian, et al.
Publicado: (2026)

Policy Iteration for Two-Player General-Sum Stochastic Stackelberg Games
por: Kudo, Mikoto, et al.
Publicado: (2024)

Stochastic Online Fisher Markets: Static Pricing Limits and Adaptive Enhancements
por: Jalota, Devansh, et al.
Publicado: (2022)

A pragmatic policy learning approach to account for users' fatigue in repeated auctions
por: Heymann, Benjamin, et al.
Publicado: (2024)

The Oracle Complexity of Simplex-based Matrix Games
por: Kornowski, Guy, et al.
Publicado: (2024)

Reinforcement Learning for Finite Space Mean-Field Type Games
por: Shao, Kai, et al.
Publicado: (2024)

Mistake, Manipulation and Margin Guarantees in Online Strategic Classification
por: Shen, Lingqing, et al.
Publicado: (2024)

Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations
por: Huang, Han, et al.
Publicado: (2024)

A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents
por: Laurière, Mathieu, et al.
Publicado: (2024)

Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
por: Yardim, Batuhan, et al.
Publicado: (2024)

Finite-Sample Guarantees for Learning Dynamics in Zero-Sum Polymatrix Games
por: Faizal, Fathima Zarin, et al.
Publicado: (2024)

MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
por: Bhusal, Gokul, et al.
Publicado: (2024)

Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games
por: Cai, Yang, et al.
Publicado: (2024)

Tracking solutions of time-varying variational inequalities
por: Hadiji, Hédi, et al.
Publicado: (2024)

Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
por: Qiu, Shuang, et al.
Publicado: (2022)

Blind Network Revenue Management and Bandits with Knapsacks under Limited Switches
por: Simchi-Levi, David, et al.
Publicado: (2019)

Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
por: Ba, Wenjia, et al.
Publicado: (2021)

Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment
por: Graf, Lukas, et al.
Publicado: (2021)

Faster Rates for No-Regret Learning in General Games via Cautious Optimism
por: Soleymani, Ashkan, et al.
Publicado: (2025)

Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games
por: Soleymani, Ashkan, et al.
Publicado: (2025)