Understanding Eigenvalues and Eigenvectors in Mathematics
Explore the concept of eigenvalues and eigenvectors in mathematics with detailed explanations, examples, and geometric interpretations. Learn how to find eigenvalues and understand their significance in matrix transformations.
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Engineering Mathematics-I Eigenvalues and Eigenvectors Prepared By- Prof. Mandar Vijay Datar I2IT, Hinjawadi, Pune - 411057 Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Definition Definition 1: A nonzero vector x is an eigenvector (or characteristic vector) of a square matrix A if there exists a scalar such that Ax = x. Then is an eigenvalue (or characteristic value) of A. Note: The zero vector can not be an eigenvector but zero can be an eigenvalue. 1 1 1 2 = = x A Example: Claim that is an eigen-vector for matrix 1 4 1 2 1 3 = = Ax Solution:- Observe that 4 For = 3 1 1 3 1 3 = 3 x = 1 Thus, = 3 is an eigenvalue of A and x is the corresponding eigen vector. 3 Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Geometric interpretation Let A be an n n matrix, if X is an n 1 vector then Y=AXis another n 1 vector.Thus A can be considered as a transformation matrix that transforms vector X to vector Y In general, a matrix multiplied to a vector changes both its magnitude and direction. However, a matrix may operate on certain vectors by changing only their magnitude, and leaving their direction unchanged. Such vectors are the Eigenvectors of the matrix. If a matrix multiplied to an eigenvector changes its magnitude by a factor, which is positive if its direction is unchanged and negative if its direction is reversed. This factor is nothing but the eigenvalue associated with that eigenvector. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Eigenvalues Let x be an eigenvector of the matrix A. Then there must exist an eigenvalue such that Ax = x or, equivalently, Ax - x = 0 or (A I)x = 0 If we define a new matrix B = A I, then Bx = 0 If B has an inverse then x = B-10 = 0. But an eigenvector cannot be zero. Thus, it follows that x will be an eigenvector of A if and only if B does not have an inverse, or equivalently det(B)=0, or det(A I) = 0 This is called the characteristic equation of A. Its roots are the eigenvalues of A. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Eigenvalues: examples 1 4 Example 1: Find the eigenvalues of = = A 2 3 1 4 = A I 1 ( )( 3 ) 8 2 3 2 = = ) 1 + 4 5 ( 5 )( Thus, two eigenvalues: 5, 1 Note: The roots of the characteristic equation can be repeated. That is, 1 = 2 = = k. If that happens, the eigenvalue is said to be of multiplicity k. Example 2: Find the eigen values of 2 2 3 = A 1 1 1 1 3 1 Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Example continued.. 2 2 3 = A I 1 1 1 1 3 1 3 2 = + = A I 2 5 = 6 0 + ( 2 )( 1 )( 3 ) 0 Therefore, = -2, 1, 3 are eigenvalues of A. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Eigenvectors To each distinct eigenvalue of a matrix A there will be at least one corresponding eigenvector which can be obtained by solving the appropriate set of homogenous equations. If i is an eigenvalue then the corresponding eigenvector xi is the solution of system (A iI)xi= 0 Example 1 (cont.): Let, =5. Consider the following system 4 4 X ) I 5 A ( 1 1 = X X 2 2 0 0 Solving the above system, we get eigenvector corresponding to eigen value Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Example continued.. = = = x x 0 x , t x t 1 2 1 2 x 1 1 = = x t , t 0 x 1 2 2 4 1 2 = = 1 : A ( I ) 1 2 4 0 0 x 2 1 = = x s , s 0 2 x 1 2 Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Example continued.. 2 2 3 Example 2 (cont.): Find the eigenvectors of = A 1 1 1 1 3 1 Recall that = -2 is an eigenvalue of A Solve the homogeneous linear system represented by = 1 4 2 3 x 0 1 = x ( A ( ) I ) 2 1 3 1 x 0 x2= t Let 2 3 1 x 0 3 x 11 t 11 1 The eigenvectors of = -2 are of the form = = = x x t t 1 2 x 14 t 14 3 Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Properties of Eigenvalues and Eigenvectors Definition: The trace of a matrix A, denoted by tr(A), is the sum of the elements present on the main diagonal of matrix A. Property 1: The sum of the eigenvalues of a matrix equals the trace of the matrix. Property 2: A matrix is singular if and only if it has a zero eigenvalue. Property 3: The eigenvalues of an upper (or lower) triangular matrix are the elements on the main diagonal. Property 4: If is an eigenvalue of A and A is invertible, then 1/ is an eigenvalue of matrix A-1. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Properties of Eigenvalues and Eigenvectors Property 5: If is an eigenvalue of A then k is an eigenvalue of kA where k is any arbitrary scalar. Property 6: If is an eigenvalue of A then k is an eigenvalue of Ak for any positive integer k. Property 8: If is an eigenvalue of A then is an eigenvalue of AT. Property 9: The product of the eigenvalues (counting multiplicity) of a matrix equals the determinant of the matrix. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
Properties of Eigenvalues and Eigenvectors Important Results- Theorem: Eigenvectors corresponding to distinct eigenvalues are linearly independent. Theorem: If is an eigenvalue of multiplicity k of an n n matrix A then the number of linearly independent eigenvectors of A associated with is given by m = n - rank(A- I). Furthermore, 1 m k. Hope Foundation s International Institute of Information Technology, I IT, P-14 Rajiv Gandhi Infotech Park, Hinjawadi, Pune - 411 057 Tel - +91 20 22933441 / 2 / 3 | Website - www.isquareit.edu.in ; Email - info@isquareit.edu.in
THANK YOU For details, please contact For details, please contact Prof. Prof. Mandar Mandar Datar Datar Asst Prof mandard@isquareit.edu.in mandard@isquareit.edu.in Department of Applied Sciences & Engineering Asst Prof Hope Foundation s International Institute of Information Technology, I IT P-14,Rajiv Gandhi Infotech Park MIDC Phase 1, Hinjawadi, Pune 411057 Tel - +91 20 22933441/2/3 www.isquareit.edu.in | info@isquareit.edu.in
