A = [ 1 0 1 2 1 2 3 1 3 1 1 1 ] A=\begin{bmatrix}
1 & 0 & 1 \\
2 & 1 & 2 \\
3 & 1 & 3 \\
1 & 1 & 1 \\
\end{bmatrix} A = ⎣ ⎡ 1 2 3 1 0 1 1 1 1 2 3 1 ⎦ ⎤ R 2 = R 2 − 2 R 1 R_2=R_2-2R_1 R 2 = R 2 − 2 R 1
[ 1 0 1 0 1 0 3 1 3 1 1 1 ] \begin{bmatrix}
1 & 0 & 1 \\
0 & 1 & 0 \\
3 & 1 & 3 \\
1 & 1 & 1 \\
\end{bmatrix} ⎣ ⎡ 1 0 3 1 0 1 1 1 1 0 3 1 ⎦ ⎤ R 3 = R 3 − 3 R 1 R_3=R_3-3R_1 R 3 = R 3 − 3 R 1
[ 1 0 1 0 1 0 0 1 0 1 1 1 ] \begin{bmatrix}
1 & 0 & 1 \\
0 & 1 & 0 \\
0 & 1 & 0 \\
1 & 1 & 1 \\
\end{bmatrix} ⎣ ⎡ 1 0 0 1 0 1 1 1 1 0 0 1 ⎦ ⎤ R 4 = R 4 − R 1 R_4=R_4-R_1 R 4 = R 4 − R 1
[ 1 0 1 0 1 0 0 1 0 0 1 0 ] \begin{bmatrix}
1 & 0 & 1 \\
0 & 1 & 0 \\
0 & 1 & 0 \\
0 & 1 & 0 \\
\end{bmatrix} ⎣ ⎡ 1 0 0 0 0 1 1 1 1 0 0 0 ⎦ ⎤ R 3 = R 3 − R 2 R_3=R_3-R_2 R 3 = R 3 − R 2
[ 1 0 1 0 1 0 0 0 0 0 1 0 ] \begin{bmatrix}
1 & 0 & 1 \\
0 & 1 & 0 \\
0 & 0 & 0 \\
0 & 1 & 0 \\
\end{bmatrix} ⎣ ⎡ 1 0 0 0 0 1 0 1 1 0 0 0 ⎦ ⎤ R 4 = R 4 − R 2 R_4=R_4-R_2 R 4 = R 4 − R 2
[ 1 0 1 0 1 0 0 0 0 0 0 0 ] \begin{bmatrix}
1 & 0 & 1 \\
0 & 1 & 0 \\
0 & 0 & 0 \\
0 & 0 & 0 \\
\end{bmatrix} ⎣ ⎡ 1 0 0 0 0 1 0 0 1 0 0 0 ⎦ ⎤ The rank of a matrix is the number of nonzero rows in the reduced matrix, so the rank is 2. 2. 2.
In the case of an m × n m\times n m × n matrix, the dimension of the domain is n , n, n , the number of columns in the matrix.
By the Rank-Nullity Theorem
Rank ( A ) + Nullity ( A ) = n . {\displaystyle \operatorname {Rank} (A)+\operatorname {Nullity} (A)=n.} Rank ( A ) + Nullity ( A ) = n .
N u l l i t y ( A ) = 3 − 2 = 1 {Nullity} (A)=3-2=1 N u ll i t y ( A ) = 3 − 2 = 1 The nullity of the given matrix A A A is 1. 1. 1.
The rank of the given matrix A A A is 2. 2. 2.
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