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.
IMA Journal of Numerical Analysis 45(2), 971–1008 (2025) 41
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.