plz show the matlab works!!!

Problem 1: Implement the line search Newton's method with the Hessian modification to minimize the Rosenbrock function f(z) = 100(T2- 2 + (1-X1)2. Choose the step length to satisfy strong Wolfe conditions with c1 10-4 and c2 0.1. Set the initial step length = 1 and the initial point zo (0, 1)T, Terminate the algorithm once llVf(rk) l 10-4. Report the objective function value f, the step length and the norm of gradient f of the last 10 iterations. (The codes for finding the step length to satisfy strong Wolfe conditions with c1 = 10-4 and c2 = 0.1 are the same ones as posted before on Canvas.) 1) The Hessian is modified by adding a matrix with minimum Frobenius norm so that all eigen values of the resulting modified Hessian are at least -10-3 (namely, the non-diagonal modification studied in class) 2) The Hessian is modified by adding a matrix with minimum Euclidean norm so that all eigenval ues of the resulting modified Hessian are at least -10-3 (namely, the diagonal modification studied in class) 3) The Hessian is modified by the modified Cholesky factorization approach with = 10-3 and 4) Compare the results obtained by the above three Hessian modification schemes and conclude which one is best Problem 1: Implement the line search Newton's method with the Hessian modification to minimize the Rosenbrock function f(z) = 100(T2- 2 + (1-X1)2. Choose the step length to satisfy strong Wolfe conditions with c1 10-4 and c2 0.1. Set the initial step length = 1 and the initial point zo (0, 1)T, Terminate the algorithm once llVf(rk) l 10-4. Report the objective function value f, the step length and the norm of gradient f of the last 10 iterations. (The codes for finding the step length to satisfy strong Wolfe conditions with c1 = 10-4 and c2 = 0.1 are the same ones as posted before on Canvas.) 1) The Hessian is modified by adding a matrix with minimum Frobenius norm so that all eigen values of the resulting modified Hessian are at least -10-3 (namely, the non-diagonal modification studied in class) 2) The Hessian is modified by adding a matrix with minimum Euclidean norm so that all eigenval ues of the resulting modified Hessian are at least -10-3 (namely, the diagonal modification studied in class) 3) The Hessian is modified by the modified Cholesky factorization approach with = 10-3 and 4) Compare the results obtained by the above three Hessian modification schemes and conclude which one is best