Find the Kernel of $V$

70 Views Asked by At

I've never really had much of an interest in linear algebra because I was never really good at it but recently, as part of my degree I've gotten a better handle on it than before. Currently we're covering linear transformations including the idea of kernels and images. However I'm really confused as to what Kernel actually means and additionally I have the following question where I have to find the Kernel of $V$.

If we have that $V=\left\{{\ \begin {pmatrix} y_1\\ y_2 \\ y_3 \end{pmatrix} | \ V:{R^3} \to {R^3}} \ \right\}$ and goes through a transformation and ends up with $\ \begin {pmatrix} y_2-y_3\\ 0\\ 0\end{pmatrix} $, I need to find $Kern(V)$. To do that I followed the following formula:

$$Kern(V):=\left\{{{\vec v \in {R^3}} \ | L(\vec v)=\vec 0} \ \right\}$$

$$\ \begin {pmatrix} y_2-y_3\\ 0\\ 0\end{pmatrix} =\ \begin {pmatrix} 0\\ 0\\ 0\end{pmatrix}$$ $$y_2=y_3≠0$$

But I thought that you could only find the Kernel if you have $a=b=c=0$? What am I missing here?

2

There are 2 best solutions below

0
On BEST ANSWER

The kernel of an application $F$ is just the set of vectors $x$ that satisfies $F(x)=0$. Here, you have $$ F : Y=\begin{pmatrix} y_1\\ y_2 \\ y_3 \end{pmatrix} \mapsto \begin {pmatrix} y_2-y_3\\ 0 \\ 0 \end{pmatrix} $$ Hence, $Y \in \text{Kern}\left(F\right)$ if and only if $$ F\left(Y\right)=0 \Leftrightarrow y_2=y_3 $$ Hence, the kernel of $F$ is $$ \text{Kern}\left(F\right)=\begin{pmatrix} y_1\\ y_2 \\ y_2 \end{pmatrix} $$ Meaning that $(1,18,18)$ has a null image by $F$ but not $(1,2,3)$.

0
On

If a transformation has a non-trivial kernel, it means that something is getting compressed. The dimension of the image is smaller than the dimension of the domain. The kernel tells you what is getting compressed. Or more specifically, the subspace of the domain that is getting mapped to 0.

To the specific problem.. you have it right that any vector where $y_2 = y_3$ will be in the kernel.

$\begin{bmatrix} 1\\0\\0\end{bmatrix},\begin{bmatrix} 0\\1\\1\end{bmatrix}$ will span the kernel.

Worth noting that the dimension of the domain is 3, the dimension of the image is 1, (it only has one non-zero entry), so the dimension of the kernel must be 2. This is the Rank-Nulity theorem.

As for $\begin{bmatrix} 0\\0\\0\end{bmatrix}$ that is the "trivial" kernel, and is in the kernel of every transformation.