[D] What is generally accepted as the best way to tune learning rates?

Given that a static learning rate usually doesn't work well for static optimizers (like SGD), and there are many adaptive optimizers (like Adam and its variants), what is generally the best way to tune the learning rate for a neural net? I've come across many methods ranging from testing a (one-cycle) scheduler, reduce LR on plateau, cosine decay (with restarts), (noisy) linear cosine decay, weight decay and super convergence (?) etc Seems like it's mostly arbitrary (or chosen based on theoretical performance) and tested over and Iver again.