python - Optimize a function in scipy without explicitly defining the gradient -

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I am currently trying to customize a function using Scripts. I have some obstacles on the variable, and with this link: - It seems that SLSQP is actually what I want. In their example, there is a clearly defined formula for the result in reference to their input, from which they get shields. I have a very disgusting computational intensive task, which calculates how the electromagnetic fields descend on metal walls, so that no meaning can be expressed in closed form (I am using MEEP FDTD Python simulation, If you are interested) Is there an equivalent function that finds the shield of function for you and then optimizes it? Or, equally, there is a function that is made in waste (a basic dragon library will be fine) which will be the shield of a function for me, so that I can go to this optimization program? Any suggestions would be appreciated.

Since you can not easily calculate the gradient, use it to pay off There is a gradient-free optimization algorithm available here in some SciPy:

There is also the basin hopping algorithm, which is similar to fake annealing and mentions on that page Not done:


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