A Lyapunov framework yields O(1/log T) and O(log T/T) gradient-norm rates for Schedule-Free on smooth nonconvex objectives, with the faster rate depending on an unproven assumption.
New Stochastic Approximation Type Procedures
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Analysis of Schedule-Free Nonconvex Optimization
A Lyapunov framework yields O(1/log T) and O(log T/T) gradient-norm rates for Schedule-Free on smooth nonconvex objectives, with the faster rate depending on an unproven assumption.