Kernel of Linear Map: Show $\ker \phi$ Equation

In summary, we are given a linear map $\phi: V\rightarrow W$ where $V$ and $W$ are vector spaces. We are asked to show that the kernel of $\phi$ is equal to the set of linear combinations of the basis elements of $V$ such that the coefficients form an element of $\mathbf L(\phi(b_1),...,\phi(b_n))$. Through a series of logical equivalences, we can conclude that an element $v$ is in the kernel of $\phi$ if and only if it can be expressed as a linear combination of the basis elements of $V$ with coefficients in $\mathbf L(\phi(b_1),...,\phi(b_n))$.
  • #1
mathmari
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Hey! :eek:

Let $1\leq n,m\in \mathbb{N}$, $V:=\mathbb{R}^n$ and $(b_1, \ldots , b_n)$ a basis of $V$. Let $W:=\mathbb{R}^m$ and let $\phi:V\rightarrow W$ be a linear map.
Show that $$\ker \phi =\left \{\sum_{i=1}^n\lambda_ib_i\mid \begin{pmatrix}\lambda_1\\ \vdots \\ \lambda_n\end{pmatrix}\in \textbf{L}(\phi (b_1), \ldots , \phi (b_n))\right \}$$

I have done the following:

Let $v\in V$. Since $(b_1, \ldots , b_n)$ is a basis of $V$, we have that $\displaystyle{v=\sum_{i=1}^n\lambda_ib_i}$.

Then we have that $$v\in \ker \phi \iff \phi (v)=0_W \iff \phi \left (\sum_{i=1}^n\lambda_ib_i\right )=0_W \iff \sum_{i=1}^n\lambda_i\phi (b_i)=0_W$$

Is this correct so far? (Wondering)
 
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  • #2
mathmari said:
Is this correct so far?

Hey mathmari!

Yep. Correct. (Nod)

Btw, what is $\mathbf L$? (Wondering)
 
  • #3
Klaas van Aarsen said:
Yep. Correct. (Nod)

Btw, what is $\mathbf L$? (Wondering)

The definition is: $$\mathbf L=\left \{(\lambda_1, \ldots , \lambda_k)^T\in \mathbb{R}^k\mid \sum_{i=1}^k\lambda_iv_i=0\right \}$$

So we get $$v\in \ker \phi \iff \phi (v)=0_W \iff \phi \left (\sum_{i=1}^n\lambda_ib_i\right )=0_W \iff \sum_{i=1}^n\lambda_i\phi (b_i)=0_W\iff (\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$$

But to get the desired result it has to be $v=(\lambda_1, \ldots , \lambda_n)^T$, or not? So did we have to take at the beginning this assumption? (Wondering)
 
  • #4
mathmari said:
The definition is: $$\mathbf L=\left \{(\lambda_1, \ldots , \lambda_k)^T\in \mathbb{R}^k\mid \sum_{i=1}^k\lambda_iv_i=0\right \}$$

So we get $$v\in \ker \phi \iff \phi (v)=0_W \iff \phi \left (\sum_{i=1}^n\lambda_ib_i\right )=0_W \iff \sum_{i=1}^n\lambda_i\phi (b_i)=0_W\iff (\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$$

Yep. (Nod)

mathmari said:
But to get the desired result it has to be $v=(\lambda_1, \ldots , \lambda_n)^T$, or not? So did we have to take at the beginning this assumption?

No. $(\lambda_1, \ldots , \lambda_n)^T$ is not an element of $V$, is it? And it shouldn't be. (Shake)
It's not an element of the kernel either.
Don't we already have the desired result? (Wondering)
What do you think is missing?
 
  • #5
Klaas van Aarsen said:
Yep. (Nod)
No. $(\lambda_1, \ldots , \lambda_n)^T$ is not an element of $V$, is it? And it shouldn't be. (Shake)
It's not an element of the kernel either.
Don't we already have the desired result? (Wondering)
What do you think is missing?

Ohh now I think I got it. I thought we have to show that $v\in \ker \phi \iff v\in L$, but $(\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$ is just the condition that $v$ is in $\left \{\sum_{i=1}^n\lambda_ib_i\mid \begin{pmatrix}\lambda_1\\ \vdots \\ \lambda_n\end{pmatrix}\in \textbf{L}(\phi (b_1), \ldots , \phi (b_n))\right \}$, right? (Wondering)

So from $$v\in \ker \phi \iff \phi (v)=0_W \iff \phi \left (\sum_{i=1}^n\lambda_ib_i\right )=0_W \iff \sum_{i=1}^n\lambda_i\phi (b_i)=0_W\iff (\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$$ we have that $v=\sum_{i=1}^n\lambda_ib_i$ is in the kernel iff $(\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$ which means that $v=\sum_{i=1}^n\lambda_ib_i$ is contained in $\left \{\sum_{i=1}^n\lambda_ib_i\mid \begin{pmatrix}\lambda_1\\ \vdots \\ \lambda_n\end{pmatrix}\in \textbf{L}(\phi (b_1), \ldots , \phi (b_n))\right \}$.

Is this correct? (Wondering)
 
  • #6
mathmari said:
Ohh now I think I got it. I thought we have to show that $v\in \ker \phi \iff v\in L$, but $(\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$ is just the condition that $v$ is in $\left \{\sum_{i=1}^n\lambda_ib_i\mid \begin{pmatrix}\lambda_1\\ \vdots \\ \lambda_n\end{pmatrix}\in \textbf{L}(\phi (b_1), \ldots , \phi (b_n))\right \}$, right?

So from $$v\in \ker \phi \iff \phi (v)=0_W \iff \phi \left (\sum_{i=1}^n\lambda_ib_i\right )=0_W \iff \sum_{i=1}^n\lambda_i\phi (b_i)=0_W\iff (\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$$ we have that $v=\sum_{i=1}^n\lambda_ib_i$ is in the kernel iff $(\lambda_1, \ldots , \lambda_n)^T\in \mathbf L$ which means that $v=\sum_{i=1}^n\lambda_ib_i$ is contained in $\left \{\sum_{i=1}^n\lambda_ib_i\mid \begin{pmatrix}\lambda_1\\ \vdots \\ \lambda_n\end{pmatrix}\in \textbf{L}(\phi (b_1), \ldots , \phi (b_n))\right \}$.

Is this correct?

Yep. All correct. (Nod)
 

1. What is the definition of a kernel in linear algebra?

The kernel of a linear map is the set of all input vectors that are mapped to the zero vector. In other words, it is the set of all solutions to the equation Ax=0, where A is the matrix representation of the linear map.

2. How do you show the kernel of a linear map?

To show the kernel of a linear map, you need to find the null space of the matrix representation of the linear map. This can be done by solving the homogeneous equation Ax=0, where A is the matrix representation of the linear map.

3. Can the kernel of a linear map be empty?

Yes, the kernel of a linear map can be empty. This means that there are no input vectors that are mapped to the zero vector. In other words, the only solution to the equation Ax=0 is the trivial solution x=0.

4. How is the dimension of the kernel related to the rank of the linear map?

The dimension of the kernel is related to the rank of the linear map by the rank-nullity theorem. This theorem states that the rank of a linear map plus the dimension of its kernel is equal to the dimension of its domain.

5. Can the dimension of the kernel be greater than the dimension of the domain?

No, the dimension of the kernel cannot be greater than the dimension of the domain. This is because the rank-nullity theorem states that the dimension of the kernel is equal to the dimension of the domain minus the rank of the linear map, and the rank of a linear map cannot be greater than the dimension of its domain.

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