Convergence of Random Reshuffling Under The Kurdyka-{\L}ojasiewicz Inequality. (arXiv:2110.04926v2 [math.OC] UPDATED)

We study the random reshuffling (RR) method for smooth nonconvex optimization
problems with a finite-sum structure. Though this method is widely utilized in
practice such as the training of neural networks, its convergence behavior is
only understood in several limited settings. In this paper, under the
well-known Kurdyka-Lojasiewicz (KL) inequality, we establish strong limit-point
convergence results for RR with appropriate diminishing step sizes, namely, the
whole sequence of iterates generated by RR is convergent and converges to a
single stationary …

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