Cyclical Log Annealing as a Learning Rate Scheduler

Cyclical log annealing make harsh restarts by creating learning rate spikes to escape a local minimum on weight bias fields while retaining the capability to hang onto a low learning rate unlike cosine annealing, which opens up a discussion of if more greedy stochastic optimization schemes could be viable without as high of a risk of overfitting in late stage training. Though log annealing works similarly to cosine annealing, it's properties are different, as it's base learning rate is much more prevalent and much higher, though this can be adjusted by tweaking minimum decay and restart interval multipliers.