Determine a basis for kernel/nullspace

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In summary: However, if you want to define a basis for a subspace, you have to specify the order.The basis vectors are the things that completely define this subspace. Every vector in the nullspace is a linear combination of these two vectors, so if ##\vec v \in Null(A)##, then ##\vec v = s<-2, 1, 0> + t<-3, 0, 1>##.
  • #1
Bobman
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Homework Statement


[/B]
A=##\begin{bmatrix}
1 & 2 & 3 \\
4 & 8 & 12
\end{bmatrix}##

Question: Determine a basis for ker(A).

Homework Equations


Ker(A)=##\begin{bmatrix}
1 & 2 & 3& |&0 \\
4 & 8 & 12& |&0
\end{bmatrix}##

(And reduce with gaussian elimination)

The Attempt at a Solution


(Sorry for no attempt on LaTeX here, it's my first time trying to use it at all above.)

Some background information, i am revisiting linear algebra and I were doing gram-schmidt process and got a totally different answer than the textbook, however i am confident i did not make a mistake during the actual gram-schmidt.
Then it occurred to me that i must have chosen a different basis than the textbook, which made me unsure in what i recall and know about the basis for the kernel. I couldn't really find any straight forward information about it in my textbook or with a few google searches, so now I've gotten caught up in my own minor insecurities in basic details.

A easy example below to illustrate my point, are both Basis #1 and Basis #2 correct (i just realized that i wrote base on the paper...)? They would of course lead to two very different orthogonal basis when using gram-schmidt, but both would be orthogonal nonetheless.

My reasoning behind them being equally correct is that there is no (to me) intuitive "basis vector 1 and basis vector 2" as it would be for example unit vectors. Still, the span should be independent of the order in the "set" anyway if i am not wrong? I.e. i would still reach all the points regardless of the order.

b2AyL0a.jpg
 

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  • #2
A basis is an unordered set of vectors. So the two things you wrote relate to the same basis, which is indeed a basis for the null space.

Also, you don't write the s and t in writing the basis. Writing the s and t gives an arbitrary element of the null space, not a basis. In this case a basis is just two purely numeric vectors.
 
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  • #3
andrewkirk said:
A basis is an unordered set of vectors. So the two things you wrote relate to the same basis, which is indeed a basis for the null space.

Also, you don't write the s and t in writing the basis. Writing the s and t gives an arbitrary element of the null space, not a basis. In this case a basis is just two purely numeric vectors.
Great, thanks.

That's very true, i think i tried to make it overly clear with the "order" by keeping the parameters.

Thanks for clarifying!
 
  • #4
Bobman said:
Great, thanks.

That's very true, i think i tried to make it overly clear with the "order" by keeping the parameters.

Thanks for clarifying!
The basis vectors are the things that completely define this subspace. Every vector in the nullspace is a linear combination of these two vectors, so if ##\vec v \in Null(A)##, then ##\vec v = s<-2, 1, 0> + t<-3, 0, 1>##.
That's where the parameters come in, not when you are just listing the vectors that form a basis. Note that any nonzero multiple of either vector would also consitute a basis.
In a set, the order of the set elements doesn't matter.
 

1. What is a basis for kernel/nullspace?

A basis for the kernel/nullspace is a set of vectors that span the space of all possible solutions to a homogeneous system of linear equations. These vectors are linearly independent and any other vector in the nullspace can be expressed as a linear combination of these basis vectors.

2. How do you determine a basis for kernel/nullspace?

To determine a basis for the kernel/nullspace, you can use the Gaussian elimination method to reduce the augmented matrix of the system to row echelon form. The columns corresponding to the free variables in the reduced matrix will form the basis for the nullspace.

3. Why is it important to determine a basis for kernel/nullspace?

Determining a basis for the kernel/nullspace allows us to understand the structure and properties of a given system of linear equations. It also helps in solving the system and finding solutions to it.

4. Can there be more than one basis for kernel/nullspace?

Yes, there can be multiple bases for the kernel/nullspace as long as they satisfy the properties of being linearly independent and spanning the nullspace. However, all bases for the same nullspace will have the same number of vectors.

5. What is the relationship between the dimension of the kernel/nullspace and the number of free variables in the system?

The dimension of the kernel/nullspace is equal to the number of free variables in the system. This means that the more free variables a system has, the larger the nullspace will be.

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