How can I handle errors in SciPy optimization?

I'm trying to optimize a function using SciPy, but I'm encountering errors. How can I handle them?

Handling errors in SciPy optimization typically involves using try-except blocks to catch exceptions. What specific errors are you facing?

I'm getting a 'ValueError' when I pass my initial guess. What should I do?

A 'ValueError' often indicates that the input parameters are not valid. Ensure your initial guess is within the bounds of the function you're optimizing.

What if my function is returning NaN values?

If your function returns NaN, check for divisions by zero or invalid operations. You can also use debugging techniques to trace the issue.

Are there any built-in options in SciPy to handle these errors automatically?

Yes, SciPy provides options like disp in the optimization functions to display messages. You can also use options={'maxiter': 100} to limit iterations and avoid infinite loops.