Fast Convergence of Random Reshuffling Under Over-Parameterization and the Polyak-Łojasiewicz Condition

Author:

Fan Chen,Thrampoulidis Christos,Schmidt Mark

Publisher

Springer Nature Switzerland

Reference39 articles.

1. Ahn, K., Yun, C., Sra, S.: Sgd with shuffling: optimal rates without component convexity and large epoch requirements. Adv. Neural. Inf. Process. Syst. 33, 17526–17535 (2020)

2. Bassily, R., Belkin, M., Ma, S.: On exponential convergence of SGD in non-convex over-parametrized learning. arXiv preprint arXiv:1811.02564 (2018)

3. Bottou, L.: Curiously fast convergence of some stochastic gradient descent algorithms. In: Proceedings of the Symposium on Learning and Data Science, Paris, vol. 8, pp. 2624–2633 (2009)

4. Lecture Notes in Computer Science;L Bottou,2012

5. Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. SIAM Rev. 60(2), 223–311 (2018)

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