# Nesterov Accelerated Shuffling Gradient Method for Convex Optimization

@inproceedings{Tran2022NesterovAS, title={Nesterov Accelerated Shuffling Gradient Method for Convex Optimization}, author={Trang H. Tran and Lam M. Nguyen and Katya Scheinberg}, booktitle={International Conference on Machine Learning}, year={2022} }

In this paper, we propose Nesterov Accelerated Shuffling Gradient (NASG), a new algorithm for the convex finite-sum minimization problems. Our method integrates the traditional Nesterov’s acceleration momentum with different shuffling sampling schemes. We show that our algorithm has an improved rate of O (1 /T ) using unified shuffling schemes, where T is the number of epochs. This rate is better than that of any other shuffling gradient methods in convex regime. Our convergence analysis does…

## 2 Citations

### On the Convergence to a Global Solution of Shuffling-Type Gradient Algorithms

- Computer ScienceArXiv
- 2022

The shuffling version of SGD which matches the mainstream practical heuristics is focused on and the convergence to a global solution of shuffling SGD for a class of non-convex functions under overparameterized settings is shown.

### SHUFFLING-TYPE GRADIENT ALGORITHMS

- Computer Science
- 2022

The shuffling version of SGD which matches the mainstream practical heuristics is focused on and the convergence to a global solution of shuffling SGD for a class of non-convex functions under overparameterized settings is shown.

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