A stochastic global optimizer samples random k-dimensional subspaces, solves the restricted problem on each, and moves to the improved point, with rate controlled by a gap parameter on the distribution of restricted minima.
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Stochastic global optimization of continuous functions via random walks on Grassmannians
A stochastic global optimizer samples random k-dimensional subspaces, solves the restricted problem on each, and moves to the improved point, with rate controlled by a gap parameter on the distribution of restricted minima.