14.3 Find a basis for NS(A) and dim{NS(A)}

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In summary, the matrix $A$ has five columns and four rows. The first column is 1, the second column is 0, the third column is 0, and the fourth column is 4. The fifth column is 5. The basis for the null space for $A$ is $\left[ \begin{array}{r} -4 \\-3 \\ -3 \\ 1 \\0 \end{array} \right],\left[ \begin{array}{r} -5 \\ -2 \\ -2 \\ 0 \\ 1 \end{array} \right]$.
  • #1
karush
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MHB
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For the matrix
$A=\left[\begin{array}{rrrrr}
1&0&0&4&5\\
0&1&0&3&2\\
0&0&1&3&2\\
0&0&0&0&0\end{array}\right]$
Find a basis for NS(A) and $\dim{NS(A)}$
$\left[\begin{array}{c}
x_1 \\
x_2 \\
x_3 \\
x_4 \\
x_5
\end{array}\right]=
\left[\begin{array}{c}
-4x_4-5x_5\\
-3x_4-2x_5\\
-3x_4-2x_5\\
x_4\\
x_5
\end{array}\right]$

ok I just did this but there is duplication in it
 
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  • #2
"NS(A)" is the null space? If so then we are looking for [tex]\begin{bmatrix}x_1 \\ x_2 \\ x_3 \\ x_4 \\ x_5 \end{bmatrix}[/tex] such that [tex]\begin{bmatrix}1 & 0 & 0 & 4 & 5 \\ 0 & 1 & 0 & 3 & 2 \\ 0 & 0 & 1 & 3 & 2 \\ 0 & 0 & 0 & 0 & 0 \end{bmatrix}\begin{bmatrix}x_1 \\ x_2 \\ x_3 \\x_4 \\ x_5\end{bmatrix}= \begin{bmatrix}x_1+ 4x_4+ 5x_5 \\ x_2+ 3x_4+ 2x_5 \\ x_3+ 3x_4+ 2x_5 \\ 0 \end{bmatrix}= \begin{bmatrix}0 \\ 0 \\ 0 \\ 0 \end{bmatrix}[/tex].

Although I wouldn't have written the equations this way they do give, as you say, [tex]x_1= -4x_4- 5x_5[/tex], [tex]x_2= -3x_4- 2x_5[/tex], [tex]x_3= -3x_4- 2x_5[/tex], and 0= 0. If by "duplication" you mean [tex]x_2= -3x_4- 2x_5[/tex] and [tex]x_3= -3x_4- 2x_5[/tex], that just means that [tex]x_2= x_3[/tex] Since all of [tex]x_1[/tex], [tex]x_2[/tex], and [tex]x_3[/tex] depend upon [tex]x_4[/tex] and [tex]x_5[/tex] take them as parameters (and the null space is two dimensional).

In particular, taking [tex]x_4= 1[/tex] and [tex]x_5= 0[/tex], [tex]x_1= -4[/tex]. [tex]x_2= -3[/tex], and [tex]x_3= -3[/tex]. One vector in the null space is [tex]\begin{bmatrix}-4 \\ -3 \\ -3 \\ 1 \\ 0 \end{bmatrix}[/tex]. Taking [tex]x_4= 0[/tex] and [tex]x_5= 1[/tex], [tex]x_1= -5[/tex], [tex]x_2= -2[/tex], and [tex]x_3= -2[/tex]. Another vector in the null space is [tex]\begin{bmatrix}-5 \\ -2 \\ -2 \\ 0 \\ 1\end{bmatrix}[/tex]. Since the null space is two dimensional and the those vectors are independent, they form a basis for the null space.
 
  • #3
$\left[ \begin{array}{c}
- 5x_4 - 4x_5 \\ - 2x_4 - 3x_5\\ - 2x_4 - 3x_5 \\x_4 \\x_5
\end{array} \right]
=\left[ \begin{array}{r} -4 \\-3 \\ -3 \\ 1 \\0
\end{array} \right]x_4
+\left[ \begin{array}{r} -5 \\ -2 \\ -2 \\ 0 \\ 1
\end{array} \right]x_5$
the basis for the null space is
$\left[ \begin{array}{r} -4 \\-3 \\ -3 \\ 1 \\0
\end{array} \right]
,\left[ \begin{array}{r} -5 \\ -2 \\ -2 \\ 0 \\ 1
\end{array} \right]$

kinda getin it
 
Last edited:

1. What does "NS(A)" mean in this context?

"NS(A)" stands for the null space of matrix A. It is the set of all vectors that, when multiplied by A, result in the zero vector. In other words, it is the set of solutions to the equation Ax = 0.

2. How do you find a basis for NS(A)?

To find a basis for NS(A), you need to solve the equation Ax = 0 and express the solution in terms of free variables. The columns corresponding to the free variables will form the basis for NS(A).

3. What is the significance of finding a basis for NS(A)?

Finding a basis for NS(A) allows us to understand the structure of the solutions to the equation Ax = 0. It also helps us to find the rank of the matrix A, which is an important property in linear algebra.

4. How do you determine the dimension of NS(A)?

The dimension of NS(A) is equal to the number of free variables in the solution to the equation Ax = 0. This can be found by performing row reduction on the augmented matrix [A | 0] and counting the number of leading variables.

5. Can the dimension of NS(A) be greater than the number of columns in matrix A?

No, the dimension of NS(A) cannot be greater than the number of columns in matrix A. This is because the number of columns in A represents the number of variables in the equation Ax = 0, and the dimension of NS(A) represents the number of free variables in the solution to this equation. Since the number of free variables cannot be greater than the total number of variables, the dimension of NS(A) cannot be greater than the number of columns in A.

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