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Section 6.3 The Kernel and range of \(T\)

Definition 6.3.1. Kernel and Range.

Let \(T\) be a linear transformation from \(V\) to \(W\text{.}\)
  • Kernel of \(\ T:\)
    \begin{equation*} \operatorname{ker}(T)=\{v\in V|T(v)=\mathbf{0}_W\}. \end{equation*}
  • Range of \(\ T:\)
    \begin{align*} \operatorname{range}(T)=\amp\{T(v)|v\in V\} \\ =\amp\{w\in W|\exists v\in V\text{ such that } w=T(v)\} \end{align*}

Example 6.3.2. Finding Kernel and Range.

Find the kernel and range of the linear transformation in Example 6.1.4.
Solution.
Recall from Example 6.1.4 that \(T:\mathbb{R}^{4}\rightarrow \mathbb{R}^{4}\) is defined by
\begin{equation*} T\left(\begin{array}{r} x_1 \\ x_2 \\ x_3\\ x_4 \end{array}\right)=\left(\begin{array}{r} x_1+x_2 \\ x_2+3x_3+x_4 \\ -x_1+x_3-x_4\\ 2x_2+2x_3-2x_4 \end{array}\right). \end{equation*}
Step 1: Find the kernel. By definition, \(\operatorname{ker}(T) = \{\mathbf{v} \in \mathbb{R}^4 \mid T(\mathbf{v}) = \mathbf{0}\}\text{.}\) We need to solve:
\begin{equation*} T\left(\begin{array}{r} x_1 \\ x_2 \\ x_3\\ x_4 \end{array}\right)=\left(\begin{array}{r} x_1+x_2 \\ x_2+3x_3+x_4 \\ -x_1+x_3-x_4\\ 2x_2+2x_3-2x_4 \end{array}\right)=\left(\begin{array}{r} 0 \\ 0 \\ 0\\ 0 \end{array}\right). \end{equation*}
This gives us the system of equations:
\begin{align*} x_1 + x_2 \amp= 0\\ x_2 + 3x_3 + x_4 \amp= 0\\ -x_1 + x_3 - x_4 \amp= 0\\ 2x_2 + 2x_3 - 2x_4 \amp= 0 \end{align*}
We solve this system using the augmented matrix:
\begin{align*} \left[\begin{array}{rrrr|r} 1 \amp 1 \amp 0 \amp 0 \amp 0\\ 0 \amp 1 \amp 3 \amp 1 \amp 0\\ -1 \amp 0 \amp 1 \amp -1 \amp 0\\ 0 \amp 2 \amp 2 \amp -2 \amp 0 \end{array}\right] \xrightarrow{\text{RREF}} \left[\begin{array}{rrrr|r} 1 \amp 0 \amp 0 \amp 2 \amp 0\\ 0 \amp 1 \amp 0 \amp -2 \amp 0\\ 0 \amp 0 \amp 1 \amp 1 \amp 0\\ 0 \amp 0 \amp 0 \amp 0 \amp 0 \end{array}\right]. \tag{1} \end{align*}
From the RREF, we get:
\begin{align*} x_1 + 2x_4 \amp= 0\\ x_2 - 2x_4 \amp= 0\\ x_3 + x_4 \amp= 0 \end{align*}
The only free variable is \(x_4\text{.}\) Let \(x_4 = t\text{,}\) then:
\begin{align*} x_1 \amp= -2t\\ x_2 \amp= 2t\\ x_3 \amp= -t\\ x_4 \amp= t \end{align*}
Therefore,
\begin{equation*} \mathbf{x} = \begin{pmatrix}-2t \\ 2t \\ -t \\ t\end{pmatrix} = t\begin{pmatrix}-2\\2\\-1\\1\end{pmatrix}. \end{equation*}
Answer for kernel:
\begin{equation*} \operatorname{ker}(T) = \operatorname{span}\left\{\begin{pmatrix}-2\\2\\-1\\1\end{pmatrix}\right\}. \end{equation*}
The dimension of the kernel is \(\dim(\operatorname{ker}(T)) = 1\text{.}\)
Step 2: Find the range. By definition, \(\operatorname{range}(T) = \{T(\mathbf{v}) \mid \mathbf{v} \in \mathbb{R}^4\}\text{.}\) For any \(\mathbf{v} = \begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} \in \mathbb{R}^4\text{,}\) we have:
\begin{align*} T(\mathbf{v}) \amp= \begin{pmatrix} x_1+x_2 \\ x_2+3x_3+x_4 \\ -x_1+x_3-x_4\\ 2x_2+2x_3-2x_4 \end{pmatrix}\\ \amp= x_1\begin{pmatrix}1\\0\\-1\\0\end{pmatrix} + x_2\begin{pmatrix}1\\1\\0\\2\end{pmatrix} + x_3\begin{pmatrix}0\\3\\1\\2\end{pmatrix} + x_4\begin{pmatrix}0\\1\\-1\\-2\end{pmatrix}. \end{align*}
Therefore, \(\operatorname{range}(T)\) is spanned by the four vectors:
\begin{equation*} \mathbf{v}_1 = \begin{pmatrix}1\\0\\-1\\0\end{pmatrix}, \quad \mathbf{v}_2 = \begin{pmatrix}1\\1\\0\\2\end{pmatrix}, \quad \mathbf{v}_3 = \begin{pmatrix}0\\3\\1\\2\end{pmatrix}, \quad \mathbf{v}_4 = \begin{pmatrix}0\\1\\-1\\-2\end{pmatrix}. \end{equation*}
However, from the RREF in (1), we see that the first three columns are pivot columns, while the fourth column is not. This means \(\mathbf{v}_4\) is a linear combination of \(\mathbf{v}_1\text{,}\) \(\mathbf{v}_2\text{,}\) and \(\mathbf{v}_3\text{.}\) Indeed, reading from the fourth column of the RREF, we have:
\begin{align*} \mathbf{v}_4 \amp= 2\mathbf{v}_1 - 2\mathbf{v}_2 + \mathbf{v}_3\\ \amp= 2\begin{pmatrix}1\\0\\-1\\0\end{pmatrix} - 2\begin{pmatrix}1\\1\\0\\2\end{pmatrix} + \begin{pmatrix}0\\3\\1\\2\end{pmatrix}\\ \amp= \begin{pmatrix}2\\0\\-2\\0\end{pmatrix} - \begin{pmatrix}2\\2\\0\\4\end{pmatrix} + \begin{pmatrix}0\\3\\1\\2\end{pmatrix}\\ \amp= \begin{pmatrix}0\\1\\-1\\-2\end{pmatrix}. \end{align*}
Since \(\mathbf{v}_4\) is a linear combination of \(\mathbf{v}_1\text{,}\) \(\mathbf{v}_2\text{,}\) and \(\mathbf{v}_3\text{,}\) we have:
\begin{equation*} \operatorname{span}\{\mathbf{v}_1, \mathbf{v}_2, \mathbf{v}_3, \mathbf{v}_4\} = \operatorname{span}\{\mathbf{v}_1, \mathbf{v}_2, \mathbf{v}_3\}. \end{equation*}
Answer for range:
\begin{equation*} \operatorname{range}(T) = \operatorname{span}\left\{\begin{pmatrix}1\\0\\-1\\0\end{pmatrix}, \begin{pmatrix}1\\1\\0\\2\end{pmatrix}, \begin{pmatrix}0\\3\\1\\2\end{pmatrix}\right\}. \end{equation*}
The dimension of the range is \(\dim(\operatorname{range}(T)) = 3\text{.}\)
Verification: Note that \(\dim(\operatorname{ker}(T)) + \dim(\operatorname{range}(T)) = 1 + 3 = 4 = \dim(\mathbb{R}^4)\text{,}\) which confirms the rank-nullity theorem.

Checkpoint 6.3.3.

The matrix
\begin{equation*} A = \left[\begin{array}{cccc} 1 \amp -2 \amp 1 \amp -2 \\ 2 \amp 2 \amp -8 \amp 12 \\ 5 \amp 1 \amp -5 \amp 11 \end{array}\right] \end{equation*}
is the standard matrix of a linear transformation \(T: \mathbb{R}^k \rightarrow \mathbb{R}^n\text{.}\)
The reduced row echelon form of \(A\) is:
\begin{equation*} \operatorname{rref}(A) = \left[\begin{array}{cccc} 1 \amp 0 \amp 0 \amp 1 \\ 0 \amp 1 \amp 0 \amp 1 \\ 0 \amp 0 \amp 1 \amp -1 \end{array}\right]. \end{equation*}
  1. Find \(k\) and \(n\text{.}\)
  2. Find the dimension of the kernel and the dimension of the range.
  3. Is \(T\) onto?
  4. Is \(T\) one-to-one?
Hint.
  • The size of the matrix determines \(k\) and \(n\text{.}\)
  • Count the pivot columns in the RREF to find the rank. Use the rank-nullity theorem.
  • \(T\) is onto if and only if \(\operatorname{range}(T) = \mathbb{R}^n\text{.}\)
  • \(T\) is one-to-one if and only if \(\operatorname{ker}(T) = \{\mathbf{0}\}\text{.}\)

Proof.

  1. We need to verify the three conditions for \(\operatorname{ker}(T)\) to be a subspace:
    • (i) Non-empty: By Theorem 6.1.6, \(T(\mathbf{0}_V) = \mathbf{0}_W\text{,}\) so \(\mathbf{0}_V \in \operatorname{ker}(T)\text{.}\)
    • (ii) Closed under addition: Let \(\mathbf{u}, \mathbf{v} \in \operatorname{ker}(T)\text{.}\) Then \(T(\mathbf{u}) = \mathbf{0}_W\) and \(T(\mathbf{v}) = \mathbf{0}_W\text{.}\) By linearity:
      \begin{equation*} T(\mathbf{u} + \mathbf{v}) = T(\mathbf{u}) + T(\mathbf{v}) = \mathbf{0}_W + \mathbf{0}_W = \mathbf{0}_W. \end{equation*}
      Therefore, \(\mathbf{u} + \mathbf{v} \in \operatorname{ker}(T)\text{.}\)
    • (iii) Closed under scalar multiplication: Let \(\mathbf{v} \in \operatorname{ker}(T)\) and \(c \in \mathbb{R}\text{.}\) Then \(T(\mathbf{v}) = \mathbf{0}_W\text{.}\) By linearity:
      \begin{equation*} T(c\mathbf{v}) = cT(\mathbf{v}) = c\mathbf{0}_W = \mathbf{0}_W. \end{equation*}
      Therefore, \(c\mathbf{v} \in \operatorname{ker}(T)\text{.}\)
  2. We verify the three conditions for \(\operatorname{range}(T)\) to be a subspace:
    • (i) Non-empty: Since \(T(\mathbf{0}_V) = \mathbf{0}_W\text{,}\) we have \(\mathbf{0}_W \in \operatorname{range}(T)\text{.}\)
    • (ii) Closed under addition: Let \(\mathbf{w}_1, \mathbf{w}_2 \in \operatorname{range}(T)\text{.}\) Then there exist \(\mathbf{v}_1, \mathbf{v}_2 \in V\) such that \(T(\mathbf{v}_1) = \mathbf{w}_1\) and \(T(\mathbf{v}_2) = \mathbf{w}_2\text{.}\) By linearity:
      \begin{equation*} \mathbf{w}_1 + \mathbf{w}_2 = T(\mathbf{v}_1) + T(\mathbf{v}_2) = T(\mathbf{v}_1 + \mathbf{v}_2). \end{equation*}
      Therefore, \(\mathbf{w}_1 + \mathbf{w}_2 \in \operatorname{range}(T)\text{.}\)
    • (iii) Closed under scalar multiplication: Let \(\mathbf{w} \in \operatorname{range}(T)\) and \(c \in \mathbb{R}\text{.}\) Then there exists \(\mathbf{v} \in V\) such that \(T(\mathbf{v}) = \mathbf{w}\text{.}\) By linearity:
      \begin{equation*} c\mathbf{w} = cT(\mathbf{v}) = T(c\mathbf{v}). \end{equation*}
      Therefore, \(c\mathbf{w} \in \operatorname{range}(T)\text{.}\)
For the rest of this section, we assume that \(T:\mathbb{R}^{n}\rightarrow \mathbb{R}^{m}.\) Recall that \(\forall v\in V\text{,}\)
\begin{equation*} T(v)=Av\text{,} \end{equation*}
where \(A=\Big[T(\mathbf{e}_1)\, T(\mathbf{e}_2)\,\ldots\, T(\mathbf{e}_n)\Big]\text{.}\)

Proof.

  1. \(\forall v\in \operatorname{ker}(T), T(v)=\mathbf{0}.\) It follows from \(T(v)=Av\) that \(Av=\mathbf{0}.\) Thus \(v\in \operatorname{Nullspace}(A).\) Therefore \(\operatorname{ker}(T)\subseteq \operatorname{Nullspace}(A).\)
    \(\forall v\in \operatorname{Nullspace}(A), \) \(Av=\mathbf{0}\) it follows from \(T(v)=Av\) that \(T(v)=\mathbf{0}.\) Thus \(v\in \operatorname{Ker}(T).\) Therefore \(\operatorname{Nullspace}(A)\subseteq \operatorname{ker}(T).\)
    Therefore, \(\operatorname{ker}(T)=\operatorname{Nullspace}(A).\)
  2. Exercise.

Key Insight.

Theorem 6.3.5 reveals a fundamental connection: the kernel and range of a linear transformation \(T\) are completely determined by its matrix \(A\text{.}\) Example 6.3.2 illustrates this connection.
This means that to study the kernel and range of \(T\text{,}\) we simply compute the null space and column space of \(A\) — techniques we already mastered in earlier chapters. Once again, questions about transformations reduce to questions about matrices. See Example 6.2.3 for more details.

Remark 6.3.7. Significance of the Rank-Nullity Theorem.

The Rank-Nullity Theorem is one of the most fundamental results in linear algebra. It reveals a profound relationship between the kernel (the part that gets "collapsed") and the range (the part that gets "covered") of a linear transformation:
  • If \(T\) is one-to-one, then \(\dim(\operatorname{ker}(T)) = 0\text{,}\) so \(\dim(\operatorname{range}(T)) = \dim(V)\text{.}\)
  • If \(V\) and \(W\) have the same dimension and \(T\) is onto, then \(\dim(\operatorname{range}(T)) = \dim(W) = \dim(V)\text{,}\) so \(\dim(\operatorname{ker}(T)) = 0\text{,}\) which means \(T\) is also one-to-one.
  • For a matrix \(A\text{,}\) this formula becomes: \(\dim(\operatorname{Nullspace}(A)) + \operatorname{rank}(A) = n\) (the number of columns).