line search in gradient descent applied to a convex function
This isn't a perfect example but hopefully the idea is clear.
If you start at a point just left of 0, small learning rates will never get it out of the local minimum at the left. But large enough learning rates to leave that local minimum will never let it settle at the global minimum at the right.

A decaying learning rate could work for such a point.