Theoretical analysis of accelerated gradient methods showing almost-sure escape from strict saddles and linear exit times, plus a subclass achieving near-optimal convergence to local minima in convex neighborhoods of nonconvex functions.
arXiv preprint arXiv:1911.07596 (2019)
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Accelerated Gradient Methods for Nonconvex Optimization: Escape Trajectories From Strict Saddle Points and Convergence to Local Minima
Theoretical analysis of accelerated gradient methods showing almost-sure escape from strict saddles and linear exit times, plus a subclass achieving near-optimal convergence to local minima in convex neighborhoods of nonconvex functions.