IN PYTHON: import numpy as np import numpy.linalg as la def smallest_eig(A): """ 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] lam = 0 x = np.random.randn(n) pass return (lam, x)
IN PYTHON: import numpy as np import numpy.linalg as la def smallest_eig(A): """ 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] lam = 0 x = np.random.randn(n) pass return (lam, x)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question
IN PYTHON:
import numpy as np
import numpy.linalg as la
def smallest_eig(A):
"""
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]
lam = 0
x = np.random.randn(n)
pass
return (lam, x)
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