Given a matrix A Rdxd and a vector b Rd, our goal is to find a vector x such that Ax = b. From a basic course in linear algebra, we know that if A is invertible, then a unique solution x = A-b exist. The best runtime to solve this is O(237) by applying Gaussian elimination. In this problem, we would like to solve this via GD. Assume A S4 is positive definite. (a) Show that solving Ax = b can cast as solving a convex quadratic optimization problem. (b) For problem you drive in part (a), show that GD finds an e-approximate solution after O(K(A) log()) and show its time complexity, where (A) = (A) is the condition number of a matrix.