The paper proposes truncated, model-gradient-generated subspaces for large-scale optimization and gives conditional decrease and convergence theorems, but the stated guarantees are not fully proven.
Expected decrease for derivative-free algorithms using random subspaces.Math
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Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
The paper proposes truncated, model-gradient-generated subspaces for large-scale optimization and gives conditional decrease and convergence theorems, but the stated guarantees are not fully proven.