write a code in MATLAB and solve all the questions step by step

these are the examples it is talking about

(a) Modify the MATLAB code given in lecture to create a matlab functions named steepest_descent. Your functions should take the same parameters and return the same items as in problem 2. (b) Modify your code for problem 2 to add a third line to the plot for steepest descent. (c) What did you learn from creating this plot and comparing these three algorithms? function [x, niters ]=cgsolve(A,b) \% cgsolve : Solve Ax=b by conjugate gradients % %[x, niters ]=cgsolve(A,b) % \% Given symmetric positive definite sparse matrix A and vector b, \% this runs conjugate gradient to solve for x in Ax=b. \% It iterates until the residual norm is reduced by 106, % or for at most max(100,sqrt(n)) iterations