MAINT: clarify dual_annealing-minimizer_kwargs docstring. Closes #20614 #20676
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| rng = np.random.default_rng(94253637693657847462) | ||
| def f(x, power): | ||
| return np.sum(np.exp(x ** power)) | ||
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| def jac(x, power): | ||
| return np.exp(x ** power) * power * x ** (power - 1) | ||
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| def hess(x, power): | ||
| # calculated using WolframAlpha as d^2/dx^2 e^(x^p) |
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I did it by hand first :-). Do you use WolframAlpha at all?
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All the time to get reference values of special/stats functions. Sometimes need to use Mathematics proper if it's not provided with Alpha.
Co-authored-by: Matt Haberland <mhaberla@calpoly.edu>
mdhaber
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dschmitz89
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|
Yeah, I saw that |
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May 9, 2024
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Closes #20614
This PR explains more clearly how keyword arguments are passed to a local minimiser as part of
dual_annealing.As part of understanding how the minimizer_kwargs is passed to
minimizeI realised that ifargsare provided to the originaldual_annealingcall, then they were automatically provided tofuncand possiblyjacusing a wrapping approach, and therefore don't need to be specified inminimizer_kwargs. The PR therefore states not to includeargsin that dict. I then realised that the same wrapping mechanism should be implemented forhessandhessp, and did that as well.