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Stochastic Trust-Region Algorithm in Random Subspaces with Convergence and Expected Complexity Analyses
This work proposes a framework for large-scale stochastic derivative-free optimization (DFO) by introducing STARS, a trust-region method based on iterative minimization in random subspaces. This framework is both an algorithmic and theoretical extension of a random subspace derivative-free
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