Stochastic trust-region methods achieve O(ε^{-2} log(1/ε)) complexity for unconstrained problems and O(ε^{-4} log(1/ε)) for equality-constrained problems under the strong growth condition, with experiments showing stable performance comparable to tuned baselines without learning-rate scheduling.
Bertsekas,Nonlinear programming, Journal of the Operational Research Society 48 (1997), pp
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Stochastic Trust-Region Methods for Over-parameterized Models
Stochastic trust-region methods achieve O(ε^{-2} log(1/ε)) complexity for unconstrained problems and O(ε^{-4} log(1/ε)) for equality-constrained problems under the strong growth condition, with experiments showing stable performance comparable to tuned baselines without learning-rate scheduling.