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Hello, I am working on an optimization problem with hundreds variables and hundreds constraints.
This model is not possible to provide exact first derivatives and second derivatives.
So my focus is to find a solution with a minimum function evaluation using numerical differentiation.
I wonder if there is a method for the forward (or backward) finite difference scheme to compute derivatives.
I know central fdm gives more exact derivative values but it computes too many function evaluations.
Thanks,
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