We have matrix A = ( − 1 0 4 6 3 2 2 5 6 ) \left(\begin{matrix}-1 & 0 & 4\\6 & 3 & 2\\2 & 5 & 6\end{matrix}\right) ⎝ ⎛ − 1 6 2 0 3 5 4 2 6 ⎠ ⎞ ;
Solution:
A) We compute determinant of A by using Laplace expansion (in this case for 3x3 matrix) D e t ( A ) = ∑ j = 1 3 ( − 1 ) 1 + j a 1 j M 1 j , Det(A) = \sum_{j=1}^3 (-1)^{1+j}a_{1j}M_{1j}, De t ( A ) = ∑ j = 1 3 ( − 1 ) 1 + j a 1 j M 1 j , where M 1 j M_{1j} M 1 j is a minor of A.
So,
D e t ( A ) = − 1 ∗ [ 3 2 5 6 ] − 0 ∗ [ 6 2 2 6 ] + 4 ∗ [ 6 3 2 5 ] Det(A) = -1 * \left[\begin{matrix}3 & 2\\5 & 6\end{matrix}\right] - 0 * \left[\begin{matrix}6 & 2\\2 & 6\end{matrix}\right] + 4*\left[\begin{matrix}6 & 3\\2 & 5\end{matrix}\right] De t ( A ) = − 1 ∗ [ 3 5 2 6 ] − 0 ∗ [ 6 2 2 6 ] + 4 ∗ [ 6 2 3 5 ] = − 1 ∗ ( 18 − 10 ) + 4 ∗ ( 30 − 6 ) = 88 = -1*(18 - 10)+4*(30-6) = 88 = − 1 ∗ ( 18 − 10 ) + 4 ∗ ( 30 − 6 ) = 88
As a result
D e t ( A ) = 88 Det(A) = 88 De t ( A ) = 88 .
B) Let's call cofactor matrix of A as B. The B can be computed with next expression b i j = ( − 1 ) i + j a i j M i j . b_{ij} = (-1)^{i+j}a_{ij}M_{ij}. b ij = ( − 1 ) i + j a ij M ij . Following the given formula, we obtain: b 11 = ( − 1 ) 1 + 1 [ 3 2 5 6 ] = 8 ; b_{11} = (-1)^{1+1} \left[\begin{matrix}3 & 2\\5 & 6\end{matrix}\right] = 8; b 11 = ( − 1 ) 1 + 1 [ 3 5 2 6 ] = 8 ; b 12 = ( − 1 ) 1 + 2 [ 6 2 2 6 ] = − 32 ; b_{12} = (-1)^{1+2} \left[\begin{matrix}6 & 2\\2 & 6\end{matrix}\right] =-32; b 12 = ( − 1 ) 1 + 2 [ 6 2 2 6 ] = − 32 ;
b 13 = ( − 1 ) 1 + 3 [ 6 3 2 5 ] = 24 ; b_{13} = (-1)^{1+3} \left[\begin{matrix}6 & 3\\2 & 5\end{matrix}\right] =24; b 13 = ( − 1 ) 1 + 3 [ 6 2 3 5 ] = 24 ; b 21 = ( − 1 ) 2 + 1 [ 0 4 5 6 ] = 20 ; b_{21} = (-1)^{2+1} \left[\begin{matrix}0 & 4\\5 & 6\end{matrix}\right] =20; b 21 = ( − 1 ) 2 + 1 [ 0 5 4 6 ] = 20 ;
b 22 = ( − 1 ) 2 + 2 [ − 1 4 2 6 ] = − 14 ; b_{22} = (-1)^{2+2} \left[\begin{matrix}-1 & 4\\2 & 6\end{matrix}\right] =-14; b 22 = ( − 1 ) 2 + 2 [ − 1 2 4 6 ] = − 14 ; b 23 = ( − 1 ) 2 + 3 [ − 1 0 2 5 ] = 5 ; b_{23} = (-1)^{2+3} \left[\begin{matrix}-1 & 0\\2 & 5\end{matrix}\right] = 5; b 23 = ( − 1 ) 2 + 3 [ − 1 2 0 5 ] = 5 ;
b 31 = ( − 1 ) 3 + 1 [ 0 4 3 2 ] = − 12 ; b_{31} = (-1)^{3+1} \left[\begin{matrix}0 & 4\\3 & 2\end{matrix}\right] =-12; b 31 = ( − 1 ) 3 + 1 [ 0 3 4 2 ] = − 12 ; b 32 = ( − 1 ) 3 + 2 [ − 1 4 6 2 ] = 26 ; b_{32} = (-1)^{3+2} \left[\begin{matrix}-1 & 4\\6 & 2\end{matrix}\right] =26; b 32 = ( − 1 ) 3 + 2 [ − 1 6 4 2 ] = 26 ;
b 33 = ( − 1 ) 3 + 3 [ − 1 0 6 3 ] = − 3 ; b_{33} = (-1)^{3+3} \left[\begin{matrix}-1 & 0\\6 & 3\end{matrix}\right] =-3; b 33 = ( − 1 ) 3 + 3 [ − 1 6 0 3 ] = − 3 ;
Finally, we have
B = ( 8 − 32 24 20 − 14 5 − 12 26 − 3 ) B = \left(\begin{matrix} 8 & -32 & 24\\20 & -14 & 5\\-12 & 26 & -3\end{matrix}\right) B = ⎝ ⎛ 8 20 − 12 − 32 − 14 26 24 5 − 3 ⎠ ⎞ .
C) By definition adjoint (also called "adjugate") is just a transpose matrix of cofactor matrix A
a d j ( A ) = B T . adj(A) = B^T. a d j ( A ) = B T .
In this case
a d j ( A ) = ( 8 20 − 12 − 32 − 14 26 24 5 − 3 ) . adj(A) = \left(\begin{matrix} 8 & 20 & -12\\-32 & -14 & 26\\24 & 5 & -3\end{matrix}\right). a d j ( A ) = ⎝ ⎛ 8 − 32 24 20 − 14 5 − 12 26 − 3 ⎠ ⎞ .
D) Using the expression A − 1 = 1 d e t ( A ) a d j ( A ) A^{-1} = \frac{1}{det(A)}adj(A) A − 1 = d e t ( A ) 1 a d j ( A ) , we obtain
A − 1 = 1 88 ( 8 20 − 12 − 32 − 14 26 24 5 − 3 ) = A^{-1}=\frac{1}{88}\left(\begin{matrix} 8 & 20 & -12\\-32 & -14 & 26\\24 & 5 & -3\end{matrix}\right) = A − 1 = 88 1 ⎝ ⎛ 8 − 32 24 20 − 14 5 − 12 26 − 3 ⎠ ⎞ = [ 1 11 5 22 − 3 22 − 4 11 − 7 44 13 44 3 11 5 88 − 3 88 ] \left[\begin{matrix}\frac{1}{11} & \frac{5}{22} & - \frac{3}{22}\\- \frac{4}{11} & - \frac{7}{44} & \frac{13}{44}\\\frac{3}{11} & \frac{5}{88} & - \frac{3}{88}\end{matrix}\right] ⎣ ⎡ 11 1 − 11 4 11 3 22 5 − 44 7 88 5 − 22 3 44 13 − 88 3 ⎦ ⎤
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