Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local...

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Extracted text: Besides local minima, saddle points are another reason for gradients to vanish. A saddle point is any location where all gradients of a function vanish but which is neither a global nor a local minimum. Consider the function f(r) = r³. Its first and second derivative vanish for r = 0. Optimization might stall at this point, even though it is not a minimum.