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Let define a n-by-n matrix A
+
 
  and a non-zero <math>vector \vec{x}\in\mathbb{R}^{n}</math>
+
. If there exists a scalar value \lambda
+
  which satisfies the vector equationA\vec{x}=\lambda\vec{x},
+
we define \lambda
+
  as an eigenvalue of the matrix A
+
, and the corresponding non-zero vector \vec{x}
+
  is called an eigenvector of the matrix A
+
. To determine eigenvalues and eigenvectors a characteristic equation D(\lambda)=det\left(A-\lambda I\right)
+
  is used. Here is an example of determining eigenvectors and eigenvalues where the matrix A
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  is given by A=\left[\begin{matrix}-5 & 2\\
+
2 & -2
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\end{matrix}\right].
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Then the characteristic equation D(\lambda)=\left(-5-\lambda\right)\left(-2-\lambda\right)-4=\lambda^{2}+7\lambda+6=0.
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  By solving the quadratic equation for \lambda
+
, we will have two eigenvalues \lambda_{1}=-1
+
  and \lambda_{2}=-6
+
. By substituting \lambda's
+
  into Eq [eq:1]
+

Latest revision as of 12:46, 13 May 2014

Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang