Graduated Optimisation of Black-Box Functions - implementation -

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Graduated Optimisation of Black-Box Functions by Weijia Shao, Christian Geißler and Fikret Sivrikaya

Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our approach for tuning high dimensional hyperparameters in machine learning. 

 The attendant implementation is here: https://github.com/christiangeissler/gradoptbenchmark

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