Reg-ASTRO achieves almost sure Õ(ε^{-1.5}) iteration complexity for stochastic nonconvex problems with mean-zero subexponential noise by coupling adaptive sampling with an adaptively regularized local model.
SIAM Journal on Optimization 34(2), 2067–2092 (2024) 42
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Adaptive Regularization within Trust Region Methods for Stochastic Nonconvex Optimization
Reg-ASTRO achieves almost sure Õ(ε^{-1.5}) iteration complexity for stochastic nonconvex problems with mean-zero subexponential noise by coupling adaptive sampling with an adaptively regularized local model.