Geometric description of the nullspace

In summary, the general form of solutions to Ax=b is found by finding a basis for the nullspace of A, which in this case is {-3,0,1} and {5,1,0}. The geometric description of the nullspace of A is a two-dimensional space, as it takes two linearly independent vectors to span a two-dimensional space. The vectors in the nullspace cannot be identical or multiples of each other.
  • #1
Tomblue
7
0

Homework Statement


general form of solutions to Ax=b
Consider matrix A=
{[ 2 -10 6 ]
[ 4 -20 12 ]
[ 1 -5 3 ]}
Find a basis for the nullspace of A. Give a geometric description of the nullspace of A.


The Attempt at a Solution


I found the basis for the nullspace of A to be
{[-3 5]
[0 1 ]
[1 0 ]}
The thing i don't understand is how to give a geometric description of the nullspace of A. If someone could help to explain how i would start to go about doing this that would be awesome because I'm not quite sure i understand the question.
 
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  • #2
Are you saying that your null space is spanned by {-3, 0, 1} and {5, 1, 0}? Your notation is a bit confusion to me; but I believe that they are asking you to describe what kind of "Space" this is. What do two vectors span?
 
  • #3
I was trying to say that the vectors {-3,0,1} and {5,1,0} form the basis for the nullspace of A and that I'm not seeing how to give a geometric description of the nullspace of A.
 
  • #4
How many linearly independent vectors does it take to span a line? A plane? A three-dimensional space?
 
  • #5
one linearly independent vector to span a line, two linearly independent vectors to span a plane, and 3 linearly independent vectors to span a 3-dimensional space, and so forth any n vectors that span an n-dimensional space are going to be linearly independent. So i see I'm going to have 2 linearly independent vectors and therefore the dim(W) is going to be 2 dimensional like i thought. Question: Can the vectors ever be identical say that v1=v2
 
  • #6
Linearly independent vectors can't be identical or even multiples of one another.
 

FAQ: Geometric description of the nullspace

What is the nullspace?

The nullspace, also known as the kernel, is the set of all vectors in a vector space that get mapped to the zero vector by a given linear transformation.

Why is the nullspace important?

The nullspace provides crucial information about the linear transformation, such as its rank and dimension, and can be used to solve systems of linear equations.

How is the nullspace described geometrically?

The nullspace can be described geometrically as the set of all vectors that lie on a plane or hyperplane that is perpendicular to the subspace spanned by the columns of the matrix representing the linear transformation.

How can the nullspace be calculated?

The nullspace can be calculated by finding the basis of the nullspace, which is the set of linearly independent vectors that span the nullspace. This can be done by solving the homogenous system of linear equations represented by the matrix.

Can the nullspace be empty?

Yes, it is possible for the nullspace to be empty if the linear transformation is one-to-one, meaning it maps each input vector to a unique output vector. In this case, the only vector that gets mapped to the zero vector is the zero vector itself, resulting in an empty nullspace.

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