Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities
Fuente:
arXiv
Saved in:
| Main Authors: | Jeong, Yungi, Otsuka, Takumi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
by: Alacaoglu, Ahmet, et al.
Published: (2026)
by: Alacaoglu, Ahmet, et al.
Published: (2026)
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
by: Chayti, El Mahdi, et al.
Published: (2023)
by: Chayti, El Mahdi, et al.
Published: (2023)
TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction
by: Fang, Yuchen, et al.
Published: (2026)
by: Fang, Yuchen, et al.
Published: (2026)
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024)
by: Liang, Ling, et al.
Published: (2024)
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
by: Jiang, Wei, et al.
Published: (2024)
by: Jiang, Wei, et al.
Published: (2024)
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
by: Dereziński, Michał
Published: (2022)
by: Dereziński, Michał
Published: (2022)
Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
by: Upadhyay, Antesh, et al.
Published: (2026)
by: Upadhyay, Antesh, et al.
Published: (2026)
Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
by: Wei, Yi, et al.
Published: (2026)
by: Wei, Yi, et al.
Published: (2026)
First-order methods for Stochastic Variational Inequality problems with Function Constraints
by: Boob, Digvijay, et al.
Published: (2023)
by: Boob, Digvijay, et al.
Published: (2023)
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
by: Wang, Lei, et al.
Published: (2022)
by: Wang, Lei, et al.
Published: (2022)
Primal Methods for Variational Inequality Problems with Functional Constraints
by: Zhang, Liang, et al.
Published: (2024)
by: Zhang, Liang, et al.
Published: (2024)
Extragradient Type Methods for Riemannian Variational Inequality Problems
by: Hu, Zihao, et al.
Published: (2023)
by: Hu, Zihao, et al.
Published: (2023)
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)
by: Beznosikov, Aleksandr, et al.
Published: (2023)
Perseus: A Simple and Optimal High-Order Method for Variational Inequalities
by: Lin, Tianyi, et al.
Published: (2022)
by: Lin, Tianyi, et al.
Published: (2022)
Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
by: Liang, Jiaming
Published: (2024)
by: Liang, Jiaming
Published: (2024)
Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional Optimization
by: Jiang, Wei, et al.
Published: (2024)
by: Jiang, Wei, et al.
Published: (2024)
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)
VFOG: Variance-Reduced Fast Optimistic Gradient Methods for a Class of Nonmonotone Generalized Equations
by: Tran-Dinh, Quoc, et al.
Published: (2025)
by: Tran-Dinh, Quoc, et al.
Published: (2025)
On the Hypomonotone Class of Variational Inequalities
by: Alomar, Khaled, et al.
Published: (2024)
by: Alomar, Khaled, et al.
Published: (2024)
Stochastic Gradient Langevin Dynamics with Variance Reduction
by: Huang, Zhishen, et al.
Published: (2021)
by: Huang, Zhishen, et al.
Published: (2021)
Towards Weaker Variance Assumptions for Stochastic Optimization
by: Alacaoglu, Ahmet, et al.
Published: (2025)
by: Alacaoglu, Ahmet, et al.
Published: (2025)
Decentralized Non-convex Stochastic Optimization with Heterogeneous Variance
by: Chen, Hongxu, et al.
Published: (2026)
by: Chen, Hongxu, et al.
Published: (2026)
Gradient Estimation and Variance Reduction in Stochastic and Deterministic Models
by: Keane, Ronan
Published: (2024)
by: Keane, Ronan
Published: (2024)
Divergence Results and Convergence of a Variance Reduced Version of ADAM
by: Wang, Ruiqi, et al.
Published: (2022)
by: Wang, Ruiqi, et al.
Published: (2022)
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity
by: Boveiri, Mohammad, et al.
Published: (2024)
by: Boveiri, Mohammad, et al.
Published: (2024)
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
by: Di, Qiwei, et al.
Published: (2023)
by: Di, Qiwei, et al.
Published: (2023)
A Block Coordinate and Variance-Reduced Method for Generalized Variational Inequalities of Minty Type
by: Diakonikolas, Jelena
Published: (2024)
by: Diakonikolas, Jelena
Published: (2024)
Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
by: Zhang, Siqi, et al.
Published: (2023)
by: Zhang, Siqi, et al.
Published: (2023)
Variance-Reduced $(\varepsilon,δ)-$Unlearning using Forget Set Gradients
by: Van Waerebeke, Martin, et al.
Published: (2026)
by: Van Waerebeke, Martin, et al.
Published: (2026)
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
by: Bassily, Raef, et al.
Published: (2024)
by: Bassily, Raef, et al.
Published: (2024)
Adaptive Optimization via Momentum on Variance-Normalized Gradients
by: Patitucci, Francisco, et al.
Published: (2026)
by: Patitucci, Francisco, et al.
Published: (2026)
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
by: D'Orazio, Ryan, et al.
Published: (2024)
by: D'Orazio, Ryan, et al.
Published: (2024)
Decoupling Learning and Decision-Making: Breaking the $\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with First-Order Methods
by: Gao, Wenzhi, et al.
Published: (2024)
by: Gao, Wenzhi, et al.
Published: (2024)
An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
by: Orvieto, Antonio, et al.
Published: (2024)
by: Orvieto, Antonio, et al.
Published: (2024)
SGD for Variational Inference: Tackling Unbounded Variance via Preconditioning and Dynamic Batching
by: Labarrière, Hippolyte, et al.
Published: (2026)
by: Labarrière, Hippolyte, et al.
Published: (2026)
Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
by: Tran-Dinh, Quoc, et al.
Published: (2026)
by: Tran-Dinh, Quoc, et al.
Published: (2026)
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints
by: Ablin, Pierre, et al.
Published: (2023)
by: Ablin, Pierre, et al.
Published: (2023)
Momentum Does Not Reduce Stochastic Noise in Stochastic Gradient Descent
by: Sato, Naoki, et al.
Published: (2024)
by: Sato, Naoki, et al.
Published: (2024)
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints
by: Alkousa, Mohammad S., et al.
Published: (2026)
by: Alkousa, Mohammad S., et al.
Published: (2026)
Similar Items
-
Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
by: Alacaoglu, Ahmet, et al.
Published: (2026) -
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
by: Chayti, El Mahdi, et al.
Published: (2023) -
TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reduction
by: Fang, Yuchen, et al.
Published: (2026) -
On the Stochastic (Variance-Reduced) Proximal Gradient Method for Regularized Expected Reward Optimization
by: Liang, Ling, et al.
Published: (2024) -
Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
by: Jiang, Wei, et al.
Published: (2024)