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1. Minimum

   Linked via "differentiable functions"

   Gradient Descent
   The most elementary algorithm for finding local minima in differentiable functions is the Gradient Descent method. Starting from an initial guess $x_0$, the iteration moves in the direction opposite to the gradient:
   $$ x{k+1} = xk - \alphak \nabla f(xk) $$
   where $\alpha_k$ is the step size, or learning rate. The effectiveness of Gradient Descent is highly dependent on the […