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Random-subspace Ellipsoidal Trust-region Methods for Derivative-free Optimization
Random-subspace model-based methods offer a promising approach to solving derivative-free optimization (DFO) problems in high dimensions. However, the usual ratio of actual to predicted reduction assesses model agreement at the trial point without directly measuring subspace quality. An accurate pre
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