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The performance of optimizers, particularly in deep learning, dependsconsiderably on their chosen hyperparameter configuration. The efficacy ofoptimizers is often studied under near-optimal problem-specifichyperparameters, and finding these settings may be prohibitively costly forpractitioners. In this work, we argue that a fair assessment of optimizers'performance must take the computational cost of hyperparameter tuning intoaccount, i.e., how easy it is to find good hyperparameter configurations usingan automatic hyperparameter search. Evaluating a variety of optimizers on anextensive set of standard datasets and architectures, our results indicate thatAdam is the most practical solution, particularly in low-budget scenarios.

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning