Solve using python

In [26]: import numpy as np import numpy . linalg as la def smallest_eiga) : Given a positive definite symmetric matrix A, calculates the smallest eigenvalue of A and the corresponding eigevector by using the inverse iteration. You are not allowed to use numpy . linalg functions for calculating the eigenvalues. However, you can use numpy linalg functions for solving linear equations and for calculating norms. Output: a tuple (lam, x), where lam is the smallest eigenvalue and x is the corresponding eigenvector n = A. shape[0] 1am = 0 x = np. random. randn(n) pass return (lam, x) Tests for Problem 4. In [27]: import numpy as np import numpy . linalg as la m = 200 n = 200 A = np . random. randn (m, n) A = A. TO A LAM, Q = la.eiga) lame = np. min(LAM) (lam, x) = smallest_eiga) if abs (lam - lame)