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lambda = eig(A)
[V,D] = eig(A)
[V,D,P] = eig(A)
lambda = eig(vpa(A))
[V,D] = eig(vpa(A))
lambda = eig(A) returns a symbolic vector containing the eigenvalues of the square symbolic matrix A.
[V,D] = eig(A) returns matrices V and D. The columns of V present eigenvectors of A. The diagonal matrix D contains eigenvalues. If the resulting V has the same size as A, the matrix A has a full set of linearly independent eigenvectors that satisfy A*V = V*D.
[V,D,P] = eig(A) returns a vector of indices P. The length of P equals to the total number of linearly independent eigenvectors, so that A*V = V*D(P,P).
lambda = eig(vpa(A)) returns numeric eigenvalues using variable precision arithmetic.
[V,D] = eig(vpa(A)) returns numeric eigenvectors using variable precision arithmetic. If A does not have a full set of eigenvectors, the columns of V are not linearly independent.
Compute the eigenvalues for the magic square of order 5:
M = sym(magic(5)); eig(M)
The result is:
ans =
65
(625/2 - (5*3145^(1/2))/2)^(1/2)
((5*3145^(1/2))/2 + 625/2)^(1/2)
-(625/2 - 5/2*3145^(1/2))^(1/2)
-(5/2*3145^(1/2) + 625/2)^(1/2)Compute the eigenvalues for the magic square of order 5 using variable precision arithmetic:
M = sym(magic(5)); eig(vpa(M))
The result is:
ans =
65.0
21.27676547147379553062642669797423
13.12628093070921880252564308594914
-13.126280930709218802525643085949
-21.276765471473795530626426697974Compute the eigenvalues and eigenvectors for one of the MATLAB test matrices:
A = sym(gallery(5)) [v, lambda] = eig(A)
The results are:
A =
[ -9, 11, -21, 63, -252]
[ 70, -69, 141, -421, 1684]
[ -575, 575, -1149, 3451, -13801]
[ 3891, -3891, 7782, -23345, 93365]
[ 1024, -1024, 2048, -6144, 24572]
v =
0
21/256
-71/128
973/256
1
lambda =
[ 0, 0, 0, 0, 0]
[ 0, 0, 0, 0, 0]
[ 0, 0, 0, 0, 0]
[ 0, 0, 0, 0, 0]
[ 0, 0, 0, 0, 0]
![]() | dsolve (sym) | emlBlock (sym) | ![]() |

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