Question about nullity and rank

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In summary, the conversation discusses finding the rank and nullity of a matrix A and determining if it satisfies the Dimension theorem for matrices. It is determined that the rank of A is 1 and the nullity is 2, which does not satisfy the theorem. There is also confusion about how to find the value of x2.
  • #1
georgeh
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So i have the following matrix:
A= [2,0,-1; 4,0,-2;0,0,0]
I do r-r-e
I get
[1,0,-1/2;0,0,0;0,0,0]
So my rank for A is 1, because I only have 1 leading one.
Now for my nullity, i get the following
x_1 - 1/2 X_3 = 0
-->
x_1=1/2 x_3
therefore

[x_1,X_2,X_3] =[1/2;0;1]t
Which would imply that nullity = 1
which then wouldn't satisfy the Dimension theorem for matrices..
which states
rank(a)+nullity(a) = n, where n is the # of columns.
..
so my question is.. what am I doing wrong?
 
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  • #2
How did you come up with x_2 = 0?
 
  • #3
georgeh said:
So i have the following matrix:
A= [2,0,-1; 4,0,-2;0,0,0]
I do r-r-e
I get
[1,0,-1/2;0,0,0;0,0,0]
So my rank for A is 1, because I only have 1 leading one.
Now for my nullity, i get the following
x_1 - 1/2 X_3 = 0
-->
x_1=1/2 x_3
therefore

[x_1,X_2,X_3] =[1/2;0;1]t
Which would imply that nullity = 1
which then wouldn't satisfy the Dimension theorem for matrices..
which states
rank(a)+nullity(a) = n, where n is the # of columns.
..
so my question is.. what am I doing wrong?
The equation x1- (1/2)x3= 0 tells you that x3= 2x1 alright but tells you nothing about x2. A basis for the null space is [1, 0, 2] (equivalent to your [1/2, 0, 1]) and [0, 1, 0]. The nullity is 2.
 

FAQ: Question about nullity and rank

What is nullity and rank?

Nullity and rank are two important concepts in linear algebra. The rank of a matrix is the maximum number of linearly independent rows or columns in the matrix. The nullity of a matrix is the dimension of the null space, which is the set of all vectors that are mapped to zero by the matrix.

How are nullity and rank related?

The relationship between nullity and rank is given by the rank-nullity theorem, which states that the sum of the rank and nullity of a matrix is equal to the number of columns in the matrix.

Why are nullity and rank important?

Nullity and rank are important because they provide information about the properties and structure of a matrix. They can help determine the invertibility of a matrix, as well as the existence and uniqueness of solutions to linear systems of equations.

How are nullity and rank calculated?

The rank of a matrix can be calculated by performing row reductions and counting the number of non-zero rows in the reduced matrix. The nullity can be calculated by finding the dimension of the null space, which can be done using methods such as Gaussian elimination or eigenvalue decomposition.

What are some real-world applications of nullity and rank?

Nullity and rank have many applications in fields such as computer science, engineering, and physics. They are used in data compression, image processing, and solving systems of linear equations in electronic circuits, among others.

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