Cover Image

Shuffling Momentum Gradient Algorithm for Convex Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tran, Trang H., Tran-Dinh, Quoc, Nguyen, Lam M.
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Shuffling Gradient-Based Methods for Nonconvex-Concave Minimax Optimization
by: Tran-Dinh, Quoc, et al.
Published: (2024)

Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
by: Nguyen, Duc Toan, et al.
Published: (2025)

Randomized Block-Coordinate Optimistic Gradient Algorithms for Root-Finding Problems
by: Tran-Dinh, Quoc, 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)

Stochastic ISTA/FISTA Adaptive Step Search Algorithms for Convex Composite Optimization
by: Nguyen, Lam M., et al.
Published: (2024)

Learning to Shuffle: Block Reshuffling and Reversal Schemes for Stochastic Optimization
by: Nguyen, Lam M., et al.
Published: (2026)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization
by: Tran, Trinh, et al.
Published: (2026)

A Class of Accelerated Fixed-Point-Based Methods with Delayed Inexact Oracles and Its Applications
by: Nguyen-Trung, Nghia, et al.
Published: (2025)

Unbiased and Biased Variance-Reduced Forward-Reflected-Backward Splitting Methods for Stochastic Composite Inclusions
by: Tran-Dinh, Quoc, et al.
Published: (2026)

Variance-Reduced Fast Krasnoselkii-Mann Methods for Finite-Sum Root-Finding Problems
by: Tran-Dinh, Quoc
Published: (2024)

Variance-Reduced Fast Operator Splitting Methods for Generalized Equations
by: Tran-Dinh, Quoc
Published: (2025)

Stochastic Difference-of-Convex Optimization with Momentum
by: Chayti, El Mahdi, et al.
Published: (2025)

Revisiting Extragradient-Type Methods -- Part 1: Generalizations and Sublinear Convergence Rates
by: Tran-Dinh, Quoc, et al.
Published: (2024)

Accelerated Extragradient-Type Methods -- Part 2: Generalization and Sublinear Convergence Rates under Co-Hypomonotonicity
by: Tran-Dinh, Quoc, et al.
Published: (2025)

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
by: Zhang, Liang, et al.
Published: (2023)

From Halpern's Fixed-Point Iterations to Nesterov's Accelerated Interpretations for Root-Finding Problems
by: Tran-Dinh, Quoc
Published: (2022)

Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
by: Pan, Xiaokang, et al.
Published: (2024)

On the Last-Iterate Convergence of Shuffling Gradient Methods
by: Liu, Zijian, et al.
Published: (2024)

On Unbalanced Optimal Transport: Gradient Methods, Sparsity and Approximation Error
by: Nguyen, Quang Minh, et al.
Published: (2022)

Negative Momentum for Convex-Concave Optimization
by: Shugart, Henry, et al.
Published: (2026)

Adaptive Optimization via Momentum on Variance-Normalized Gradients
by: Patitucci, Francisco, et al.
Published: (2026)

Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
by: Liu, Wei, et al.
Published: (2025)

Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization
by: Tran-The, Hung, et al.
Published: (2022)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Small Gradient Norm Regret for Online Convex Optimization
by: Gao, Wenzhi, et al.
Published: (2026)

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)

A Hyper-Transformer model for Controllable Pareto Front Learning with Split Feasibility Constraints
by: Tuan, Tran Anh, et al.
Published: (2024)

An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2024)

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
by: Dang, Thanh, et al.
Published: (2025)

Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024)

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
by: Lekeufack, Jordan, et al.
Published: (2024)

Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
by: Sinha, Abhishek, et al.
Published: (2023)

Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization
by: Borodich, Ekaterina, et al.
Published: (2025)

You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization
by: Veviurko, Grigorii, et al.
Published: (2023)

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
by: Xie, Xingyu, et al.
Published: (2022)

Bilevel Optimization with Lower-Level Uniform Convexity: Theory and Algorithm
by: Wu, Yuman, et al.
Published: (2026)

Rapid Overfitting of Multi-Pass Stochastic Gradient Descent in Stochastic Convex Optimization
by: Vansover-Hager, Shira, et al.
Published: (2025)

Are Convex Optimization Curves Convex?
by: Barzilai, Guy, et al.
Published: (2025)

Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
by: Medyakov, Daniil, et al.
Published: (2025)