Please don't copy chegg answer. Its not correct. Need it in the python code format or there will be an unlike.

As you saw in your midterm exam, in the risk parity portfolio (RPP), you have to solve the following convex optimization problem minx0g(x)21xxblog(x) where log(x)=(log(x1),log(x2),,log(xN)),b is the risk budget vector and is the covariance matrix of the log-returns. In this problem we assume N=3 and =1.00.020.040.021.00.020.040.021.0b=31 (a) Use the Jacobi algorithm to solve problem () (derive the update formula for each element xi of x, assuming the other elements to be constant). Note: In Jacobi algorithm, the update xk+1 in k+1-th iteration is obtained after computing all xik+1 updates in parallel. (b) (Bonus) Find such that I0 (positive semi-definite). Then prove that u(x,y) defined below, is a surrogate majorization function for g(x). u(x,y)=21xx+21(xy)(I)(xy)blog(x) (c) Use the Majorization Minimization algorithm (MM) to solve the problem via the following iterations (choose =1.2 ) xk+1=x0argminu(x,xk) Hint: Rewrite u(x,xk) as follows u(x,xk)=2xxk+1xk2blog(x)+h(xk) (d) Plot the convergence figures of the above methods versus time. The vertical axis should be g(xk)g(x) and the horizontal axis should be tk (the time taken to compute xk in seconds, starting from zero at k=0 ). You may obtain x using cvxpy