Basis for Null Space: Finding Basis Vectors | Explained and Solved

In summary, to find a basis for the nullspace of the given matrix, we need to identify all the free variables and then solve for the pivot variables. This will give us two basis vectors, which are (1, 0, 0) and (0, -√2, 1). Alternatively, we can see that the nullspace is the set of all (x, y, z) satisfying the equation y+√2z=0, and therefore, the basis vectors are (1, 0, 0) and (0, -√2, 1).
  • #1
Dgray101
33
0
Hey guys so we need to find the basis for

0 1 [itex]\sqrt{2}[/itex]
0 0 0
0 0 0

I know how you do it. But my prof says that one of the basis vectors is (1 0 0) but I don't know how he arrives at this?
 
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  • #2
Dgray101 said:
Hey guys so we need to find the basis for

0 1 [itex]\sqrt{2}[/itex]
0 0 0
0 0 0

I know how you do it. But my prof says that one of the basis vectors is (1 0 0) but I don't know how he arrives at this?

The way to find a basis for the nullspace is to identify all the free variables (which correspond to the free columns of the matrix): x1 and x3 are the free variables while x2 is a pivot variable.

Since the number of free columns (or number of free variables) equals 2, you will get 2 special solutions for Ax=0, and hence 2 basis vectors. The rest you know how to do: to find 1 of the special solutions, set one of the free variables to 1 and the rest 0, and solve for the pivot variables.
Doing this procedure, would give the following basis vectors for the nullspace: (1,0,0) and (0,-√2,1).

Alternatively, you can still see why the above 2 vectors are a basis. You can easily see that all the solutions of the form
x2= -√2 x3 where x3 is any real number
will solve the system, along with
x1= any real number.

Hence, you see that the full solution to Ax=0 is
x1 (1,0,0) + x3 (0,-√2,1),
and you can easily pick out the basis vectors again.
 
  • #3
Equivalently, the null space of this matrix is the set of all (x, y, z) such that
[tex]\begin{pmatrix}0 & 1 & \sqrt{2} \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{pmatrix}\begin{pmatrix}x \\ y \\ z\end{pmatrix}= \begin{pmatrix}0 \\ 0 \\ 0 \end{pmatrix}[/tex]

[tex]\begin{pmatrix}y+ \sqrt{2}z \\ 0 \\ 0 \end{pmatrix}= \begin{pmatrix} 0 \\ 0 \\ 0 \end{pmatrix}[/tex]

which, as Vahsek said, reduces to the single equation [itex]y+ \sqrt{z}= 0[/itex] or [itex]y= -\sqrt{2}z[/itex] (the other two rows being just 0= 0). There is no condition on x so x can be any thing.

That is, we can write [itex](x, y, z)= (x, -\sqrt{2}z, z)= (x, 0, 0)+ (0, -\sqrt{2}z, z)= x(1, 0, 0)+ z(0, -\sqrt{2}, 1)[/itex]. A vector is in the null space of this matrix if and only if it is a linear combination of [itex](1, 0, 0)[/itex] and [itex](0, -\sqrt{2}, 1)[/itex], exactly as Vahsek said.
 

FAQ: Basis for Null Space: Finding Basis Vectors | Explained and Solved

1. What is the basis for a null space?

The basis for a null space is a set of vectors that, when multiplied by a matrix, result in a vector of all zeros. In other words, the basis for a null space is the set of all possible solutions to the equation Ax = 0, where A is an m x n matrix and x is a n x 1 vector.

2. Why is the basis for a null space important?

The basis for a null space is important because it helps us understand the solutions to a system of linear equations. It also allows us to determine if a system of equations is consistent or inconsistent, and if it has a unique solution or infinitely many solutions.

3. How do you find the basis for a null space?

To find the basis for a null space, we first need to find the reduced row echelon form of the matrix A. Then, we can identify the columns in the matrix that do not have a pivot position. These columns correspond to the free variables in the system of equations, and the basis for the null space is formed by setting each free variable to 1 and all other variables to 0.

4. Can the basis for a null space be empty?

Yes, the basis for a null space can be empty. This occurs when the matrix A is an m x n matrix with m < n, meaning there are more variables than equations. In this case, the null space only contains the zero vector and therefore has no basis.

5. How does the basis for a null space relate to linear independence?

The basis for a null space and linear independence are closely related. If a set of vectors is linearly independent, then the basis for their null space is empty. On the other hand, if a set of vectors is linearly dependent, then the basis for their null space contains non-zero vectors. In fact, the number of non-zero vectors in the basis for a null space is equal to the number of linearly dependent vectors in the original set.

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