Consider the function f(x, y) = (x − 10)2 + 0.25(y − x 2 ) 2 . Find the minimizer using both gradient descent as well as Newton’s method. In both cases, compute the gradients/hessians analytically (i.e. don’t compute them numerically). Try a few different starting values and compare how the approaches in terms how many iterations it takes them to converge. (In R only)

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