Recent advances in trust region algorithms

By Ya-Xiang Yuan. Mathematical programming, 2015

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Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training (2019, background): “[22, 79]), no ellipsoid fits all objective functions and our contribution thus lies in the identification of adequate matrix-induced constraints for the specific task of deep learning.”
Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites (2019, methodology): “3653 are determined by fitting the model (2) to the measured data on clear days at the targeted site using the trust-region-reflective optimization algorithm [26].”
Complexity and global rates of trust-region methods based on probabilistic models (2018, unknown): “Trust-region methods form a well established and understood class of methods for the minimization of a nonlinear (possibly nonsmooth) function subject or not to constraints on its variables (see the book [7] and the recent survey [28]).”