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Gradient Estimation with Simultaneous Perturbation and Compressive Sensing

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Figure

Figure 1: Percentage error of ∥∇f − ∇�f∥ in our method, with varying number of iterationsk for SP
Figure 6: Performance of the proposed algorithm with variation k for different sparsitylevels.
Figure 7: Performance of the proposed algorithm with variation in sparsity.
Figure 8: Percentage error in ∥⟨G(X)⟩ − ⟨ G�(X)⟩∥ with number of samples r varying from1 to 25
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