[D] Simulated Annealing and Gradient Descent

First of all I would like to apologise if my question is irrelevant.

I am a beginner in the field of data science and I’ve been using gradient descent to find the minimum of a function. ( I’m using python)

Obviously, I came across the local minimum problem and tried to find ways to avoid it and found simulated annealing.

Is there a way to combine both to get to the global minimum?

Thanks in advance

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