DTN: A Learning Rate Scheme with Convergence Rate of $\mathcal{O}(1/t)$ for SGD
Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan, Jayant R. Kalagnanam,, Marten van Dijk

TL;DR
This paper discusses a learning rate scheme for SGD claiming an $(1/t)$ convergence rate, but later acknowledges errors in the proofs and invalidates the main convergence claims due to incorrect use of the test criterion.
Contribution
Proposes a learning rate scheme for SGD with a claimed convergence rate, but the main theoretical results are invalidated due to proof errors.
Findings
Original convergence claims are invalidated.
The paper admits mistakes in the proof of key lemmas.
No validated convergence rate is established due to errors.
Abstract
This paper has some inconsistent results, i.e., we made some failed claims because we did some mistakes for using the test criterion for a series. Precisely, our claims on the convergence rate of of SGD presented in Theorem 1, Corollary 1, Theorem 2 and Corollary 2 are wrongly derived because they are based on Lemma 5. In Lemma 5, we do not correctly use the test criterion for a series. Hence, the result of Lemma 5 is not valid. We would like to thank the community for pointing out this mistake!
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Machine Learning and Algorithms · Advanced Bandit Algorithms Research
