Accelerating the Nelder - Mead Method with Predictive Parallel Evaluation by Yoshihiko Ozaki, Shuhei Watanabe and Masaki Onishi
The Nelder–Mead (NM) method has been recently proposed for application in hyperparameter optimization (HPO) of deep neural networks. However, the NM method is not suitable for parallelization, which is a serious drawback for its practical application in HPO. In this study, we propose a novel approach to accelerate the NM method with respect to the parallel computing resources. The numerical results indicate that the proposed method is significantly faster and more efficient when compared with the previous naive approaches with respect to the HPO tabular benchmarks.The attendant implementaiton is here.
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