4 x 2 − 4 x y + y 2 ( 4 − 2 − 2 1 ) T h e e i g e n v a l u e s o f A a r e t h e r o o t s o f t h e c h a r a c t e r i s t i c e q u a t i o n det ( A − λ I ) = 0 λ = 5 , λ = 0 4x^2-4xy+y^2\\
\begin{pmatrix}
4 & -2 \\
-2 & 1
\end{pmatrix}\\
\mathrm{The\:eigenvalues\:of}\:A\:\mathrm{are\:the\:roots\:of\:the\:characteristic\:equation}\:\det \left(A-λ\:I\right)=0\\
λ=5,\:λ=0\\ 4 x 2 − 4 x y + y 2 ( 4 − 2 − 2 1 ) The eigenvalues of A are the roots of the characteristic equation det ( A − λ I ) = 0 λ = 5 , λ = 0
Therefore the orthogonal canonical reduction of the quadratic form is
𝑄 = ( 𝑥 ′ 𝑦 ′ ) ( 0 0 0 5 ) ( x y ) = 5 y ′ 2 𝑄 = (𝑥′ 𝑦′) \begin{pmatrix}
0 & 0 \\
0 & 5
\end{pmatrix}\begin{pmatrix}
x \\
y
\end{pmatrix} = 5y'^2 Q = ( x ′ y ′ ) ( 0 0 0 5 ) ( x y ) = 5 y ′2
Principal axes at λ = 0 λ=0\\ λ = 0
The reduced row echelon form of the matrix is [ 1 2 0 0 ] \begin{bmatrix}
1 & 2\\
0 & 0
\end{bmatrix} [ 1 0 2 0 ]
To find the null space, solve the matrix equation[ 1 2 0 0 ] [ x 1 x 2 ] = [ 0 0 ] \begin{bmatrix}
1 & 2\\
0 & 0
\end{bmatrix} \begin{bmatrix}
x_1 \\
x_2
\end{bmatrix}= \begin{bmatrix}
0\\
0
\end{bmatrix} [ 1 0 2 0 ] [ x 1 x 2 ] = [ 0 0 ]
If we take x 2 = t x 2 = t , x_
2
=t
x_2=t, x 2 = t x 2 = t , then x 1 = − 2 t x 1 = − 2 t x_1=−2tx_1=−2t x 1 = − 2 t x 1 = − 2 t .
T h u s , x ⃗ = [ − 2 t t ] = [ − 2 1 ] t Thus, x⃗ =\begin{bmatrix}
-2t \\
t
\end{bmatrix}= \begin{bmatrix}
-2 \\
1
\end{bmatrix} t T h u s , x ⃗ = [ − 2 t t ] = [ − 2 1 ] t
∣ x ∣ = ( − 2 ) 2 + 1 2 = 5 |x|= \sqrt{(-2)^2+1^2}=\sqrt5 ∣ x ∣ = ( − 2 ) 2 + 1 2 = 5
1 5 ( − 2 1 ) \frac{1}{\sqrt5}\begin{pmatrix}
-2 \\
1
\end{pmatrix} 5 1 ( − 2 1 )
Principal axes at λ = 5 λ=5 λ = 5
The reduced row echelon form of the matrix is [ 1 − 0.5 0 0 ] \begin{bmatrix}
1 & -0.5\\
0 & 0
\end{bmatrix} [ 1 0 − 0.5 0 ]
To find the null space, solve the matrix equation[ 1 − 0.5 0 0 ] [ x 1 x 2 ] = [ 0 0 ] \begin{bmatrix}
1 & -0.5\\
0 & 0
\end{bmatrix} \begin{bmatrix}
x_1 \\
x_2
\end{bmatrix}= \begin{bmatrix}
0\\
0
\end{bmatrix} [ 1 0 − 0.5 0 ] [ x 1 x 2 ] = [ 0 0 ]
If we take x 2 = t x 2 = t , x_
2
=t
x_2=t, x 2 = t x 2 = t , then x 1 = − 2 t x 1 = − 2 t x_1=−2tx_1=−2t x 1 = − 2 t x 1 = − 2 t .
T h u s , x ⃗ = [ − 0.5 t t ] = [ − 0.5 1 ] t Thus, x⃗ =\begin{bmatrix}
-0.5t \\
t
\end{bmatrix}= \begin{bmatrix}
-0.5 \\
1
\end{bmatrix} t T h u s , x ⃗ = [ − 0.5 t t ] = [ − 0.5 1 ] t
∣ x ∣ = ( − 0.5 ) 2 + 1 2 = 5 2 |x|= \sqrt{(-0.5)^2+1^2}=\frac{\sqrt5}{2} ∣ x ∣ = ( − 0.5 ) 2 + 1 2 = 2 5
2 5 ( 0.5 1 ) \frac{2}{\sqrt5}\begin{pmatrix}
0.5 \\
1
\end{pmatrix} 5 2 ( 0.5 1 )
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