This exercise uses gradient descent to solve the ridge minimization prob- lem in (1} Specically, let fQB] denote the objective function in (1}, i.e., R m) = 2a as)? + m2. i=1 Next, let VfUS') denote the gradient of f, i.e., VfUS') = df/d. Then, given a positive learning rate o: :> 0 and an initial value ,89, the gradient descent iteratively updates EH1 using: a\" = a aV) |,=,,,. The iteration stops until the successive values of t+1 remain about the same, say, when |t+1 gl a\": E for some small tolerance level E :> 0. Now suppose we have the data 2 1.6 4. 5.7 X: 1 , y: 0.7 0 1.4 5 8 Use gradient descent to nd the ridge estimate with )x : 2. Use the initial value g 2 0, :1 = 0.001 and E = 104. What is the ridge estimate? Plot the values of the intermediate ,83