How does learning rate scheduling modify cost function optimization trajectory?

Learning rate schedules control step size evolution, shaping convergence dynamics.

Schedules like cosine decay, step decay, and warm restarts adjust learning rates over time. High initial rates encourage exploration of the cost landscape, while lower rates later refine convergence into minima. This balance helps avoid sharp minima and improves generalization.