A subspace-based derivative-free optimization framework is re-introduced with a sketched convergence analysis and preliminary numerical evidence for problems with up to 10^4 variables.
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Scalable Derivative-Free Optimization Algorithms with Low-Dimensional Subspace Techniques
A subspace-based derivative-free optimization framework is re-introduced with a sketched convergence analysis and preliminary numerical evidence for problems with up to 10^4 variables.