For > 0, determine the solution of the regularized LS problem x() = arg min x 1 2 Ax b 2 2 + 2 1 2 x 2 2 . Express the answer in terms of the SVD of an appropriate matrix. Use the fact that B+ = (BB) 1B when BB is invertible to simplify the expression. Hint: Rewrite the cost function so it looks like a usual least-squares problem. (b) What does x() tend to as ? Does this answer make sense? (c) Write (by hand, not code) an iteration based on the gradient descent (GD) method such that the iterates converge to the minimizer x(). (d) Determine a condition on the step size that guarantees convergence of your GD method. Express the condition in terms of the original problem quantities: A, b, and