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Poster
Acceleration via Fractal Learning Rate Schedules
Naman Agarwal · Surbhi Goel · Cyril Zhang

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 1 »

In practical applications of iterative first-order optimization, the learning rate schedule remains notoriously difficult to understand and expensive to tune. We demonstrate the presence of these subtleties even in the innocuous case when the objective is a convex quadratic. We reinterpret an iterative algorithm from the numerical analysis literature as what we call the Chebyshev learning rate schedule for accelerating vanilla gradient descent, and show that the problem of mitigating instability leads to a fractal ordering of step sizes. We provide some experiments to challenge conventional beliefs about stable learning rates in deep learning: the fractal schedule enables training to converge with locally unstable updates which make negative progress on the objective.

Keywords: Optimization

Author Information

Naman Agarwal (Google Research)
Surbhi Goel (Microsoft Research)
Cyril Zhang (Microsoft Research)

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