The Kernel of a Linear Transformation

The kernel of a linear transformation  
  operating on a set  
  is the set of elements  
\[x \in S\]
  for which  
  so that  
  to the zero element of the codomain.
Example. Differentiation is linear. We can define a linear transformation on the set of polynomials of degree 2.
\[\frac{d}{dx}(1)=0,\frac{d}{dx}(x)=1, \frac{d}{dx}(x^2)=2x\]
We represent  
  by the vectors  
\[\begin{pmatrix}1\\0\\0\end{pmatrix}, \begin{pmatrix}0\\1\\0\end{pmatrix}, \begin{pmatrix}0\\0\\1\end{pmatrix}\]
The columns of the matrix representing  
  can be found by differentiating  
  in turn and representing the results as vectors.
The matrix representing th linear transformation is
\[ \left( \begin{array}{ccc} 0 & 1 & 0 \\ 0 & 0 &2 \end{array} \right) \]
The polynomial  
  is represented by the vector  

\[T(2+3x+5x^2)= \left( \begin{array}{cc} 0 & 1 & 0 \\ 0 & 0 & 2 \end{array} \right) \begin{pmatrix}2\\3\\5\end{pmatrix}=\begin{pmatrix}3\\10\\0\end{pmatrix}\]
  which returns the polynomial  
The kernel of this transformation is the set of constants, since the differential of a constant is zero.
The kernel of a transformation has certain properties. The kernel of a transformation is a subspace of the domain. 1.  

2. If  
\[x,y \in ker(T), T(x)=T(y)=0 \rightarrow T(\alpha x+ \beta y)= \alpha T(x) + \beta T(y) =\alpha \times 0 + \beta \times 0 =0\]
\[\alpha x+ \beta y \in ker(T)\]

The zero element of the domain is always in the kernel. The dimension of the kernel as a subspace is always less than or equal to the dimension of the domain, considered as a subspace.

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