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Section C.1 Sample Exercises with Solutions

Here we model one exercise and solution for each learning objective. Your solutions should not look identical to those shown below, but these solutions can give you an idea of the level of detail required for a complete solution.

Example C.1.1. LE1.

Consider the scalar system of equations
\begin{alignat*}{5} 3x_1 &\,+\,& 2x_2 &\,\,& &\,+\,&x_4 &= 1 \\ -x_1 &\,-\,& 4x_2 &\,+\,&x_3&\,-\,&7x_4 &= 0 \\ &\,\,& x_2 &\,-\,&x_3 &\,\,& &= -2 \end{alignat*}
  1. Rewrite this system as a vector equation.
  2. Write an augmented matrix corresponding to this system.
Solution.
  1. Written as a vector equation, the above linear system corresponds to:
    \begin{equation*} x_1\left[\begin{array}{c} 3 \\ -1 \\ 0 \end{array}{}\right] + x_2 \left[\begin{array}{c}2 \\ -4 \\ 1 \end{array}{}\right]+ x_3 \left[\begin{array}{c} 1 \\ 1 \\ -1 \end{array}{}\right] + x_4 \left[\begin{array}{c} 1 \\ -7 \\ 0 \end{array}{}\right] = \left[\begin{array}{c} 1 \\ 0 \\ -2 \end{array}{}\right] \end{equation*}
  2. Written as an augmented matrix, the above linear system corresponds to:
    \begin{equation*} \left[\begin{array}{cccc|c} 3 & 2 & 0 & 1 & 1 \\ -1 & -4 & 1 & -7 & 0 \\ 0 & 1 & -1 & 0 & -2 \end{array}\right] \end{equation*}

Example C.1.2. LE2.

  1. For each of the following matrices, explain why it is not in reduced row echelon form.
    \begin{equation*} A = \left[\begin{array}{ccc} -4 & 0 & 4 \\ 0 & 1 & -2 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right] \hspace{2em} B = \left[\begin{array}{ccc} 0 & 1 & 2 \\ 1 & 0 & -3 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right] \hspace{2em} C = \left[\begin{array}{ccc} 1 & -4 & 4 \\ 0 & 1 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right] \hspace{2em} \hspace{2em} \end{equation*}
  2. Show step-by-step why
    \begin{equation*} \RREF \left[\begin{array}{cccc} 0 & 3 & 1 & 2 \\ 1 & 2 & -1 & -3 \\ 2 & 4 & -1 & -1 \end{array}\right] = \left[\begin{array}{cccc} 1 & 0 & 0 & 4 \\ 0 & {1} & 0 & -1 \\ 0 & 0 & {1} & 5 \end{array}\right]. \end{equation*}
Solution.
    • \(A=\left[\begin{array}{ccc} -4 & 0 & 4 \\ 0 & 1 & -2 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right]\) is not in reduced row echelon form because the pivots are not all \(1\text{.}\)
    • \(B=\left[\begin{array}{ccc} 0 & 1 & 2 \\ 1 & 0 & -3 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right]\) is not in reduced row echelon form because the pivots are not descending to the right.
    • \(C=\left[\begin{array}{ccc} 1 & -4 & 4 \\ 0 & 1 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right]\) is not in reduced row echelon form because not every entry above and below each pivot is zero.
  1. \begin{alignat*}{4} \left[\begin{array}{cccc} 0 & 3 & 1 & 2 \\ 1 & 2 & -1 & -3 \\ 2 & 4 & -1 & -1 \end{array}\right] &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 2 & -1 & -3 \\ 0 & 3 & 1 & 2 \\ 2 & 4 & -1 & -1 \end{array}\right] &\hspace{0.2in} \text{Swap Rows 1 and 2}& \\ &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 2 & -1 & -3 \\ 0 & 3 & 1 & 2 \\ 0 & 0 & 1 & 5 \end{array}\right] &\hspace{0.2in} \text{Add } -2 \text{ Row 1 to Row 3}& \\ &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 2 & -1 & -3 \\ 0 & \markedPivot{1} & \frac{1}{3} & \frac{2}{3} \\ 0 & 0 & 1 & 5 \end{array}\right] &\hspace{0.2in} \text{Multiply Row 3 by } \frac{1}{3}& \\ &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 0 & -\frac{5}{3} & -\frac{13}{3} \\ 0 & \markedPivot{1} & \frac{1}{3} & \frac{2}{3} \\ 0 & 0 & \markedPivot{1} & 5 \end{array}\right] &\hspace{0.2in} \text{Add } -2 \text{ Row 2 to Row 1}& \\ &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 0 & -\frac{5}{3} & -\frac{13}{3} \\ 0 & \markedPivot{1} & 0 & -1 \\ 0 & 0 & \markedPivot{1} & 5 \end{array}\right] &\hspace{0.2in} \text{Add } -\frac{1}{3} \text{ Row 3 to Row 2}& \\ &\sim& \left[\begin{array}{cccc} \markedPivot{1} & 0 & 0 & 4 \\ 0 & \markedPivot{1} & 0 & -1 \\ 0 & 0 & \markedPivot{1} & 5 \end{array}\right] &\hspace{0.2in} \text{Add } \frac{5}{3} \text{ Row 3 to Row 1}& \end{alignat*}

Example C.1.3. LE3.

Consider each of the following systems of linear equations or vector equations.
  1. \begin{equation*} \begin{matrix} -2 \, x_{1} & + & x_{2} & + & x_{3} & = & -2 \\ -2 \, x_{1} & - & 3 \, x_{2} & - & 3 \, x_{3} & = & 0 \\ 3 \, x_{1} & + & x_{2} & + & x_{3} & = & 3 \\ \end{matrix} \end{equation*}
  2. \begin{equation*} x_{1} \left[\begin{array}{c} -5 \\ 3 \\ -1 \end{array}\right] + x_{2} \left[\begin{array}{c} 3 \\ -2 \\ 2 \end{array}\right] + x_{3} \left[\begin{array}{c} 14 \\ -9 \\ 7 \end{array}\right] = \left[\begin{array}{c} 1 \\ 0 \\ -4 \end{array}\right] \end{equation*}
  3. \begin{equation*} x_{1} \left[\begin{array}{c} 0 \\ -1 \\ -1 \end{array}\right] + x_{2} \left[\begin{array}{c} 1 \\ -4 \\ -4 \end{array}\right] + x_{3} \left[\begin{array}{c} 2 \\ -4 \\ -3 \end{array}\right] = \left[\begin{array}{c} -5 \\ 11 \\ 8 \end{array}\right] \end{equation*}
  • Explain how to find a simpler system or vector equation that has the same solution set for each.
  • Explain whether each solution set has no solutions, one solution, or infinitely-many solutions. If the set is finite, describe it using set notation.
Solution.
  1. \begin{equation*} \mathrm{RREF}\left[\begin{array}{ccc|c} -2 & 1 & 1 & -2 \\ -2 & -3 & -3 & 0 \\ 3 & 1 & 1 & 3 \end{array}\right]=\left[\begin{array}{ccc|c} 1 & 0 & 0 & 0 \\ 0 & 1 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{array}\right] \end{equation*}
    This matrix corresponds to the simpler system
    \begin{equation*} \begin{matrix} x_{1} & & & & & = & 0 \\ & & x_{2} & + & x_{3} & = & 0 \\ & & & & 0 & = & 1 \\ \end{matrix} \end{equation*}
    The third equation \(0=1\) indicates that the system has no solutions. The solution set is \(\emptyset\text{.}\)
  2. \begin{equation*} \mathrm{RREF}\left[\begin{array}{ccc|c} -5 & 3 & 14 & 1 \\ 3 & -2 & -9 & 0 \\ -1 & 2 & 7 & -4 \end{array}\right]=\left[\begin{array}{ccc|c} 1 & 0 & -1 & -2 \\ 0 & 1 & 3 & -3 \\ 0 & 0 & 0 & 0 \end{array}\right] \end{equation*}
    This matrix corresponds to the simpler system
    \begin{equation*} \begin{matrix} x_{1} & & & - & x_3 & = & -2 \\ & & x_{2} & + & 3\,x_{3} & = & -3 \\ & & & & 0 & = & 0 \\ \end{matrix}. \end{equation*}
    Since there are three variables and two nontrivial equations, the solution set has infinitely-many solutions.
  3. \begin{equation*} \mathrm{RREF}\left[\begin{array}{ccc|c} 0 & 1 & 2 & -5 \\ -1 & -4 & -4 & 11 \\ -1 & -4 & -3 & 8 \end{array}\right]=\left[\begin{array}{ccc|c} 1 & 0 & 0 & -3 \\ 0 & 1 & 0 & 1 \\ 0 & 0 & 1 & -3 \end{array}\right] \end{equation*}
    This matrix corresponds to the simpler system
    \begin{equation*} \begin{matrix} x_{1} & & & & & = & -3 \\ & & x_{2} & & & = & 1 \\ & & & & x_{3} & = & -3 \\ \end{matrix}. \end{equation*}
    This system has one solution. The solution set is \(\left\{ \left[\begin{array}{c} -3 \\ 1 \\ -3 \end{array}\right] \right\}\text{.}\)

Example C.1.4. LE4.

Consider the following vector equation.
\begin{equation*} x_{1} \left[\begin{array}{c} -3 \\ 0 \\ 4 \end{array}\right] + x_{2} \left[\begin{array}{c} -3 \\ 0 \\ 4 \end{array}\right] + x_{3} \left[\begin{array}{c} 0 \\ 1 \\ 0 \end{array}\right] + x_{4} \left[\begin{array}{c} -4 \\ -5 \\ 5 \end{array}\right] = \left[\begin{array}{c} -11 \\ -9 \\ 14 \end{array}\right] \end{equation*}
  1. Explain how to find a simpler system or vector equation that has the same solution set.
  2. Explain how to describe this solution set using set notation.
Solution.
First, we compute
\begin{equation*} \mathrm{RREF}\left[\begin{array}{cccc|c} -3 & -3 & 0 & -4 & -11 \\ 0 & 0 & 1 & -5 & -9 \\ 4 & 4 & 0 & 5 & 14 \end{array}\right]=\left[\begin{array}{cccc|c} 1 & 1 & 0 & 0 & 1 \\ 0 & 0 & 1 & 0 & 1 \\ 0 & 0 & 0 & 1 & 2 \end{array}\right]. \end{equation*}
This corresponds to the simpler system
\begin{equation*} \begin{matrix} x_{1} & + & x_2 & & & & & = & 1 \\ & & & & x_3 & & & = & 1 \\ & & & & & & x_4 & = & 2 \\ \end{matrix}. \end{equation*}
Since the second column is a non-pivot column, we let \(x_2=a\text{.}\) Making this substitution and then solving for \(x_1\text{,}\) \(x_3\text{,}\) and \(x_4\) produces the system
\begin{equation*} \begin{matrix} x_1 &=& 1-a \\ x_2 &=& a \\ x_3 &=& 1 \\ x_4 &=& 2 \\ \end{matrix} \end{equation*}
Thus, the solution set is \(\left\{ \left[\begin{array}{c} -a + 1 \\ a \\ 1 \\ 2 \end{array}\right] \,\middle|\, a \in\mathbb R \right\} \text{.}\)

Example C.1.5. EV1.

Consider each of these claims about a vector equation.

(a)

\(\left[\begin{array}{c} -13 \\ 3 \\ -15 \end{array}\right]\) is a linear combination of the vectors \(\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] , \text{ and } \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right]\text{.}\)
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim.
Solution.
“The vector equation
\begin{equation*} x_1\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] + x_2\left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] + x_3\left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] + x_4\left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right] = \left[\begin{array}{c} -13 \\ 3 \\ -15 \end{array}\right] \end{equation*}
has at least one solution.”
(ii)
Determine if the statement you wrote is true or false.
Solution.
\(\mathrm{RREF}\, \left[\begin{array}{cccc|c} 1 & 2 & 3 & -5 & -13 \\ 0 & 0 & 0 & 1 & 3 \\ 1 & 2 & 3 & -5 & -15 \end{array}\right] = \left[\begin{array}{cccc|c} 1 & 2 & 3 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 & 1 \end{array}\right]\)
The bottom row requires \(0=1\text{.}\) Therefore the vector equation has no solutions, so \(\left[\begin{array}{c} -13 \\ 3 \\ -15 \end{array}\right]\) is not a linear combination.
(iii)
If your statement was true, explain and demonstrate how to construct a specific linear combination of \(\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] , \text{ and } \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right]\) that equals \(\left[\begin{array}{c} -13 \\ 3 \\ -15 \end{array}\right]\text{.}\)
Solution.
N/A

(b)

\(\left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right]\)is a linear combination of the vectors \(\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] , \text{ and } \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right]\text{.}\)
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim.
Solution.
“The vector equation
\begin{equation*} x_1\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] + x_2\left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] + x_3\left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] + x_4\left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right] = \left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right] \end{equation*}
has at least one solution.”
(ii)
Determine if the statement you wrote is true or false.
Solution.
\(\mathrm{RREF}\, \left[\begin{array}{cccc|c} 1 & 2 & 3 & -5 & -13 \\ 0 & 0 & 0 & 1 & 3 \\ 1 & 2 & 3 & -5 & -13 \end{array}\right] = \left[\begin{array}{cccc|c} 1 & 2 & 3 & 0 & 2 \\ 0 & 0 & 0 & 1 & 3 \\ 0 & 0 & 0 & 0 & 0 \end{array}\right]\)
No row requires \(0=1\text{.}\) Therefore vector equation has at least one solution, so \(\left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right]\) is a linear combination.
(iii)
If your statement was true, explain and demonstrate how to construct a specific linear combination of \(\left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 3 \\ 0 \\ 3 \end{array}\right] , \text{ and } \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right]\) that equals \(\left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right]\text{.}\)
Solution 1.
By setting the free variables \(x_2=0\) and \(x_3=0\text{,}\) we obtain the equations \(x_1=2\) and \(x_4=3\text{.}\) Therefore we may construct
\begin{equation*} 2 \left[\begin{array}{c} 1 \\ 0 \\ 1 \end{array}\right] + 3 \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right] = \left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right]\text{.} \end{equation*}
Solution 2.
By trial and error, we may find that
\begin{equation*} 1 \left[\begin{array}{c} 2 \\ 0 \\ 2 \end{array}\right] + 3 \left[\begin{array}{c} -5 \\ 1 \\ -5 \end{array}\right] = \left[\begin{array}{c} 2-15 \\ 0+3 \\ 2-15 \end{array}\right] = \left[\begin{array}{c} -13 \\ 3 \\ -13 \end{array}\right]\text{.} \end{equation*}

Example C.1.6. EV2.

(a)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} -1 \\ 1 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 0 \\ -1 \\ 1 \\ -3 \end{array}\right] , \left[\begin{array}{c} 2 \\ -2 \\ 1 \\ -2 \end{array}\right] , \left[\begin{array}{c} -3 \\ -1 \\ 4 \\ -5 \end{array}\right] , \left[\begin{array}{c} 7 \\ 8 \\ -15 \\ 28 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors spans \(\mathbb R^4\text{.}\)
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} -1 \\ 1 \\ 0 \\ 2 \end{array}\right] + x_2 \left[\begin{array}{c} 0 \\ -1 \\ 1 \\ -3 \end{array}\right] + x_3 \left[\begin{array}{c} 2 \\ -2 \\ 1 \\ -2 \end{array}\right] + x_4 \left[\begin{array}{c} -3 \\ -1 \\ 4 \\ -5 \end{array}\right] + x_5 \left[\begin{array}{c} 7 \\ 8 \\ -15 \\ 28 \end{array}\right] = \vec w \end{equation*}
has at least one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution.
Note that \(\mathrm{RREF}\, \left[\begin{array}{ccccc} -1 & 0 & 2 & -3 & 7 \\ 1 & -1 & -2 & -1 & 8 \\ 0 & 1 & 1 & 4 & -15 \\ 2 & -3 & -2 & -5 & 28 \end{array}\right] = \left[\begin{array}{ccccc} 1 & 0 & 0 & 0 & 2 \\ 0 & 1 & 0 & 0 & -3 \\ 0 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & -3 \end{array}\right]\) has no row of zeros that would allow a \(0=1\) contradiction.
Therefore the vector equation has solutions for every \(\vec w\text{,}\) and thus the set of vectors does span \(\mathbb{R}^4\text{.}\)

(b)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ 1 \\ -2 \\ 0 \end{array}\right] , \left[\begin{array}{c} 0 \\ 1 \\ -2 \\ 3 \end{array}\right] , \left[\begin{array}{c} -4 \\ -2 \\ 5 \\ 5 \end{array}\right] , \left[\begin{array}{c} 12 \\ 3 \\ -9 \\ -24 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors spans \(\mathbb R^4\text{.}\)
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} 1 \\ 1 \\ -2 \\ 0 \end{array}\right] + x_2 \left[\begin{array}{c} 0 \\ 1 \\ -2 \\ 3 \end{array}\right] + x_3 \left[\begin{array}{c} -4 \\ -2 \\ 5 \\ 5 \end{array}\right] + x_4 \left[\begin{array}{c} 12 \\ 3 \\ -9 \\ -24 \end{array}\right] = \vec w \end{equation*}
has at least one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution.
Note that \(\mathrm{RREF}\, \left[\begin{array}{cccc} 1 & 0 & -4 & 12 \\ 1 & 1 & -2 & 3 \\ -2 & -2 & 5 & -9 \\ 0 & 3 & 5 & -24 \end{array}\right] = \left[\begin{array}{cccc} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & -3 \\ 0 & 0 & 1 & -3 \\ 0 & 0 & 0 & 0 \end{array}\right]\) has a row of zeros that makes a \(0=1\) contradiction possible.
Therefore the vector equation will not have solutions for every \(\vec w\text{,}\) and thus the set of vectors does not span \(\mathbb{R}^4\text{.}\)

(c)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right] , \left[\begin{array}{c} -5 \\ 1 \\ -5 \\ -2 \end{array}\right] , \left[\begin{array}{c} 12 \\ -2 \\ 12 \\ 6 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors spans \(\mathbb R^4\text{.}\)
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ 1 \end{array}\right] + x_2 \left[\begin{array}{c} -5 \\ 1 \\ -5 \\ -2 \end{array}\right] + x_3 \left[\begin{array}{c} 12 \\ -2 \\ 12 \\ 6 \end{array}\right] = \vec w \end{equation*}
has at least one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution 1.
Note that \(\mathrm{RREF}\, \left[\begin{array}{ccc} 1 & -5 & 12 \\ 0 & 1 & -2 \\ 1 & -5 & 12 \\ 1 & -2 & 6 \end{array}\right] = \left[\begin{array}{ccc} 1 & 0 & 2 \\ 0 & 1 & -2 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right]\) has a row of zeros that makes a \(0=1\) contradiction possible.
Therefore the vector equation will not have solutions for every \(\vec w\text{,}\) and thus the set of vectors does not span \(\mathbb{R}^4\text{.}\)
Solution 2.
It takes at least \(4\) vectors to span \(\mathbb R^4\text{,}\) so the equation cannot always have solutions and the set cannot span.

Example C.1.7. EV3.

Answer the following questions about Euclidean subspaces.

(a)

Consider the following subsets of Euclidean space \(\mathbb R^4\) defined by
\begin{equation*} U=\left\{ \left[\begin{array}{c} x \\ y \\ z \\ w \end{array}\right] \middle| y^{2} - 7 \, z^{2} = x \right\} \hspace{1em} \text{and} \hspace{1em} W=\left\{ \left[\begin{array}{c} x \\ y \\ z \\ w \end{array}\right] \middle| -5 \, w - 7 \, x - y = -7 \, z \right\} \end{equation*}
Without writing a proof, explain why only one of these subsets is likely to be a subspace.
Solution.
\(W\) appears to be a subspace as its equation is a linear combination of variables and constant scalars, and \(U\) is likely not due to its equation having squared terms.

(b)

Consider the following subset of Euclidean space \(\mathbb R^3\)
\begin{equation*} Q=\left\{ \left[\begin{array}{c} x \\ y \\ z \end{array}\right] \middle| 0 = 5 \, y^{2} - 5 \, x + 3 \, z \right\} \end{equation*}
Prove that \(Q\) is not a subspace.
Solution.
Note that \(\left[\begin{array}{c} 0 \\ 3 \\ -15 \end{array}\right]\) belongs to \(Q\text{,}\) since
\begin{equation*} 5(3)^2-5(0)+3(-15)=45-45=0\text{,} \end{equation*}
but \(2\left[\begin{array}{c} 0 \\ 3 \\ -15 \end{array}\right]= \left[\begin{array}{c} 0 \\ 6 \\ -30 \end{array}\right]\) does not, since
\begin{equation*} 5(6)^2-5(0)+3(-30)=180-90=90\not=0\text{.} \end{equation*}

(c)

Consider the following subset of Euclidean space \(\mathbb R^3\)
\begin{equation*} R=\left\{ \left[\begin{array}{c} x \\ y \\ z \end{array}\right] \middle| 5 \, x - 5 \, y = -4 \, z \right\} \end{equation*}
Prove that \(R\) is a subspace.
Solution.
  • First, note that \(\left[\begin{array}{c} 0 \\ 0 \\ 0 \end{array}\right]\in R\) since \(5(0)-5(0)=0\) and \(-4(0)=0\) as well.
  • Let \(\left[\begin{array}{c} x \\ y \\ z \end{array}\right]\in R\) so that \(5 \, x - 5 \, y = -4 \, z\text{,}\) and let \(\left[\begin{array}{c} a \\ b \\ c \end{array}\right]\in R\) so that \(5 \, a - 5 \, b = -4 \, c\text{.}\) We may then compute
    \begin{align*} 5(x+a)-5(y+b) &= 5x+5a-5y-5b\\ &= (5x-5y)+(5a-5b)\\ &= (-4z)+(-4c)\\ &= -4(z+c) \end{align*}
    So \(5(x+a)-5(y+b) = -4(z+c)\) and therefore \(\left[\begin{array}{c} x \\ y \\ z \end{array}\right]+ \left[\begin{array}{c} a \\ b \\ c \end{array}\right]= \left[\begin{array}{c} x+a \\ y+b \\ z+c \end{array}\right]\in R\text{,}\) showing \(R\) is closed under addition.
  • Let \(\left[\begin{array}{c} x \\ y \\ z \end{array}\right]\in R\) so that \(5 \, x - 5 \, y = -4 \, z\text{,}\) and let \(k\in\mathbb R\) be a scalar. We may then compute
    \begin{align*} 5x - 5y &= -4z\\ \Rightarrow k[5x-5y]&= k[-4z]\\ \Rightarrow 5kx-5ky &= -4kz\\ \Rightarrow 5(kx)-5(ky)&= -4(kz) \end{align*}
    and therefore \(k\left[\begin{array}{c} x \\ y \\ z \end{array}\right] =\left[\begin{array}{c} kx \\ ky \\ kz \end{array}\right]\in R\text{,}\) showing \(R\) is closed under scalar multiplication.

Example C.1.8. EV4.

(a)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} -3 \\ 3 \\ 3 \\ -4 \end{array}\right] , \left[\begin{array}{c} 9 \\ -9 \\ -9 \\ 12 \end{array}\right] , \left[\begin{array}{c} 1 \\ -2 \\ -3 \\ 2 \end{array}\right] , \left[\begin{array}{c} -11 \\ 13 \\ 15 \\ -16 \end{array}\right] \right\}\text{.} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is linearly independent.”
Solution.
“The vector equation
\begin{equation*} x_1\left[\begin{array}{c} -3 \\ 3 \\ 3 \\ -4 \end{array}\right] + x_2\left[\begin{array}{c} 9 \\ -9 \\ -9 \\ 12 \end{array}\right] + x_3\left[\begin{array}{c} 1 \\ -2 \\ -3 \\ 2 \end{array}\right] + x_4\left[\begin{array}{c} -11 \\ 13 \\ 15 \\ -16 \end{array}\right] = \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}\right] \end{equation*}
has exactly one solution: \(\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}\right]\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution.
\(\mathrm{RREF}\, \left[\begin{array}{cccc} -3 & 9 & 1 & -11 \\ 3 & -9 & -2 & 13 \\ 3 & -9 & -3 & 15 \\ -4 & 12 & 2 & -16 \end{array}\right] = \left[\begin{array}{cccc} 1 & -3 & 0 & 3 \\ 0 & 0 & 1 & -2 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{array}\right]\)
Since the RREF matrix has two non-pivot columns (the second and fourth), the solution set has free variables and thus there are more than one solution. This means the set is linearly dependent.

(b)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ -1 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} -3 \\ 4 \\ 3 \\ 1 \end{array}\right] , \left[\begin{array}{c} -5 \\ 5 \\ 1 \\ -2 \end{array}\right] , \left[\begin{array}{c} -15 \\ 17 \\ 8 \\ -1 \end{array}\right] , \left[\begin{array}{c} 2 \\ -5 \\ -7 \\ -4 \end{array}\right] \right\}\text{.} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is linearly independent.”
Solution.
“The vector equation
\begin{equation*} x_1\left[\begin{array}{c} 1 \\ -1 \\ 0 \\ 1 \end{array}\right] + x_2\left[\begin{array}{c} -3 \\ 4 \\ 3 \\ 1 \end{array}\right] + x_3\left[\begin{array}{c} -5 \\ 5 \\ 1 \\ -2 \end{array}\right] + x_4\left[\begin{array}{c} -15 \\ 17 \\ 8 \\ -1 \end{array}\right] + x_5\left[\begin{array}{c} 2 \\ -5 \\ -7 \\ -4 \end{array}\right]= \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}\right] \end{equation*}
has exactly one solution: \(\left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \\ 0 \end{array}\right]\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution 1.
\(\mathrm{RREF}\, \left[\begin{array}{ccccc} 1 & -3 & -5 & -15 & 2 \\ -1 & 4 & 5 & 17 & -5 \\ 0 & 3 & 1 & 8 & -7 \\ 1 & 1 & -2 & -1 & -4 \end{array}\right] = \left[\begin{array}{ccccc} 1 & 0 & 0 & 1 & 3 \\ 0 & 1 & 0 & 2 & -3 \\ 0 & 0 & 1 & 2 & 2 \\ 0 & 0 & 0 & 0 & 0 \end{array}\right]\)
Since the RREF matrix has two non-pivot columns (the fourth and fifth), the solution set has free variables and thus there are more than one solution. This means the set is linearly dependent.
Solution 2.
Since these vectors are from \(\mathbb R^4\) and there are more than \(4\) vectors, the equation must have infinitely-many solutions and the set must be linearly dependent.

(c)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} -3 \\ 2 \\ 1 \\ 0 \end{array}\right] , \left[\begin{array}{c} -2 \\ 1 \\ 0 \\ 0 \end{array}\right] , \left[\begin{array}{c} 5 \\ -5 \\ -4 \\ -2 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is linearly independent.”
Solution.
“The vector equation
\begin{equation*} x_1\left[\begin{array}{c} -3 \\ 2 \\ 1 \\ 0 \end{array}\right] + x_2\left[\begin{array}{c} -2 \\ 1 \\ 0 \\ 0 \end{array}\right] + x_3\left[\begin{array}{c} 5 \\ -5 \\ -4 \\ -2 \end{array}\right] = \left[\begin{array}{c} 0 \\ 0 \\ 0 \\ 0 \end{array}\right] \end{equation*}
has exactly one solution: \(\left[\begin{array}{c} 0 \\ 0 \\ 0 \end{array}\right]\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution.
\(\mathrm{RREF}\, \left[\begin{array}{ccc} -3 & -2 & 5 \\ 2 & 1 & -5 \\ 1 & 0 & -4 \\ 0 & 0 & -2 \end{array}\right] = \left[\begin{array}{ccc} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \end{array}\right]\)
Since the RREF matrix has all pivot columns, the solution set lacks free variables and thus there is exactly one solution. This means the set is linearly independent.

Example C.1.9. EV5.

(a)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ -2 \\ 0 \\ 1 \end{array}\right] , \left[\begin{array}{c} 2 \\ -4 \\ 0 \\ 2 \end{array}\right] , \left[\begin{array}{c} 3 \\ -6 \\ 0 \\ 3 \end{array}\right] , \left[\begin{array}{c} 5 \\ -5 \\ -2 \\ 4 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is a basis for”
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} 1 \\ -2 \\ 0 \\ 1 \end{array}\right] +x_2 \left[\begin{array}{c} 2 \\ -4 \\ 0 \\ 2 \end{array}\right] +x_3 \left[\begin{array}{c} 3 \\ -6 \\ 0 \\ 3 \end{array}\right] +x_4 \left[\begin{array}{c} 5 \\ -5 \\ -2 \\ 4 \end{array}\right] =\vec w \end{equation*}
has exactly one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution 1.
Since \(\mathrm{RREF}\, \left[\begin{array}{cccc} 1 & 2 & 3 & 5 \\ -2 & -4 & -6 & -5 \\ 0 & 0 & 0 & -2 \\ 1 & 2 & 3 & 4 \end{array}\right] = \left[\begin{array}{cccc} 1 & 2 & 3 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{array}\right]\text{,}\) we see from the zero row that there are some vectors \(\vec w\) for which the equation is not true, so the set fails to span and therefore fails to be a basis.
Solution 2.
Since \(\mathrm{RREF}\, \left[\begin{array}{cccc} 1 & 2 & 3 & 5 \\ -2 & -4 & -6 & -5 \\ 0 & 0 & 0 & -2 \\ 1 & 2 & 3 & 4 \end{array}\right] = \left[\begin{array}{cccc} 1 & 2 & 3 & 0 \\ 0 & 0 & 0 & 1 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{array}\right]\text{,}\) we see from the non-pivot column that there are some vectors \(\vec w\) for which the equation has infinitely-many solutions, so the set is linearly dependent and therefore fails to be a basis.

(b)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ 3 \\ 4 \\ -4 \end{array}\right] , \left[\begin{array}{c} -1 \\ -3 \\ -4 \\ 4 \end{array}\right] , \left[\begin{array}{c} 0 \\ 1 \\ 3 \\ -3 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is a basis for”
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} 1 \\ 3 \\ 4 \\ -4 \end{array}\right] +x_2 \left[\begin{array}{c} -1 \\ -3 \\ -4 \\ 4 \end{array}\right] +x_3 \left[\begin{array}{c} 0 \\ 1 \\ 3 \\ -3 \end{array}\right] =\vec w \end{equation*}
has exactly one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution 1.
Since \(\mathrm{RREF}\, \left[\begin{array}{ccc} 1 & -1 & 0 \\ 3 & -3 & 1 \\ 4 & -4 & 3 \\ -4 & 4 & -3 \end{array}\right] = \left[\begin{array}{ccc} 1 & -1 & 0 \\ 0 & 0 & 1 \\ 0 & 0 & 0 \\ 0 & 0 & 0 \end{array}\right]\) we see from the zero row that there are some vectors \(\vec w\) for which the equation is not true, so the set fails to span and therefore fails to be a basis.
Solution 2.
The set has only three vectors, so the set cannot span and there must be vectors for which the equation has no solutions. Therefore the set is not a basis.

(c)

Consider the set of vectors
\begin{equation*} \left\{ \left[\begin{array}{c} 3 \\ 2 \\ -1 \\ 0 \end{array}\right] , \left[\begin{array}{c} -2 \\ -1 \\ 0 \\ -1 \end{array}\right] , \left[\begin{array}{c} -2 \\ -1 \\ 1 \\ 0 \end{array}\right] , \left[\begin{array}{c} -4 \\ -1 \\ 0 \\ -2 \end{array}\right] \right\} \end{equation*}
(i)
Write a statement involving the solutions of a vector equation that’s equivalent to this claim: “The set of vectors is a basis for”
Solution.
“The vector equation
\begin{equation*} x_1 \left[\begin{array}{c} 3 \\ 2 \\ -1 \\ 0 \end{array}\right] +x_2 \left[\begin{array}{c} -2 \\ -1 \\ 0 \\ -1 \end{array}\right] +x_3 \left[\begin{array}{c} -2 \\ -1 \\ 1 \\ 0 \end{array}\right] + x_4\left[\begin{array}{c} -4 \\ -1 \\ 0 \\ -2 \end{array}\right] =\vec w \end{equation*}
has exactly one solution for every \(\vec w\in\mathbb R^4\text{.}\)
(ii)
Explain and demonstrate how to determine whether or not this statement is true.
Solution.
Since \(\mathrm{RREF}\, \left[\begin{array}{cccc} 3 & -2 & -2 & -4 \\ 2 & -1 & -1 & -1 \\ -1 & 0 & 1 & 0 \\ 0 & -1 & 0 & -2 \end{array}\right] = \left[\begin{array}{cccc} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{array}\right]\) we see the equation always has exactly one solution (each row and column has a pivot). Therefore the set is spanning and linearly independent, and therefore the set is a basis.

Example C.1.10. EV6.

Consider the subspace
\begin{equation*} W = \vspan \left\{ \left[\begin{array}{c} 1 \\ -3 \\ -1 \\ 2 \end{array}\right] , \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ -2 \end{array}\right] , \left[\begin{array}{c} 3 \\ -6 \\ -1 \\ 2 \end{array}\right] , \left[\begin{array}{c} 1 \\ 6 \\ 1 \\ -1 \end{array}\right] , \left[\begin{array}{c} 2 \\ 3 \\ 0 \\ 1 \end{array}\right] \right\} . \end{equation*}
  1. Explain how to find a basis of \(W\text{.}\)
  2. Explain how to find the dimension of \(W\text{.}\)
Solution.
  1. Observe that
    \begin{equation*} \RREF \left[\begin{array}{ccccc} 1 & 1 & 3 & 1 & 2 \\ -3 & 0 & -6 & 6 & 3 \\ -1 & 1 & -1 & 1 & 0 \\ 2 & -2 & 2 & -1 & 1 \end{array}\right] = \left[\begin{array}{ccccc} 1 & 0 & 2 & 0 & 1 \\ 0 & 1 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & 1 \\ 0 & 0 & 0 & 0 & 0 \end{array}\right] \end{equation*}
    If we remove the vectors yielding non-pivot columns, the resulting set will span the same vectors while being linearly independent. Therefore
    \begin{equation*} \left\{ \left[\begin{array}{c} 1 \\ -3 \\ -1 \\ 2 \end{array}\right] , \left[\begin{array}{c} 1 \\ 0 \\ 1 \\ -2 \end{array}\right] , \left[\begin{array}{c} 1 \\ 6 \\ 1 \\ -1 \end{array}\right] \right\} \end{equation*}
    is a basis of \(W\text{.}\)
  2. Since this (and thus every other) basis has three vectors in it, the dimension of \(W\) is \(3\text{.}\)

Example C.1.11. EV7.

Consider the homogeneous system of equations
\begin{alignat*}{6} x_1 &\,+\,& x_2 &\,+\,& 3x_3 &\,+\,& x_4 &\,+\,& 2x_5 &=& 0\\ -3x_1 &\,\,& &\,-\,& 6x_3 &\,+\,&6 x_4 &\,+\,& 3x_5 &=& 0\\ -x_1 &\,+\,& x_2 &\,-\,& x_3 &\,+\,& x_4 &\,\,& &=& 0\\ 2x_1 &\,-\,& 2x_2 &\,+\,& 2x_3 &\,-\,& x_4 &\,+\,& x_5 &=& 0 \end{alignat*}
  1. Find the solution space of the system.
  2. Find a basis of the solution space.
Solution.
  1. Observe that
    \begin{equation*} \RREF \left[\begin{array}{ccccc|c} 1 & 1 & 3 & 1 & 2 & 0\\ -3 & 0 & -6 & 6 & 3 & 0\\ -1 & 1 & -1 & 1 & 0 & 0\\ 2 & -2 & 2 & -1 & 1& 0 \end{array}\right] = \left[\begin{array}{ccccc|c} 1 & 0 & 2 & 0 & 1 &0\\ 0 & 1 & 1 & 0 & 0 &0\\ 0 & 0 & 0 & 1 & 1 &0\\ 0 & 0 & 0 & 0 & 0&0 \end{array}\right] \end{equation*}
    Letting \(x_3=a\) and \(x_5=b\) (since those correspond to the non-pivot columns), this is equivalent to the system
    \begin{alignat*}{6} x_1 &\,\,& &\,+\,& 2x_3 &\,\,& &\,+\,& x_5 &=& 0\\ &\,\,& x_2 &\,+\,& x_3 &\,\,& &\,\,& &=& 0\\ &\,\,& &\,\,& x_3 &\,\,& &\,\,& &=& a\\ &\,\,& &\,\,& &\,\,& x_4 &\,+\,& x_5 &=& 0\\ &\,\,& &\,\,& &\,\,& &\,\,& x_5 &=& b \end{alignat*}
    Thus, the solution set is
    \begin{equation*} \setBuilder{\left[\begin{array}{c} -2a-b \\ -a \\ a \\ -b \\ b \end{array}\right]}{a,b \in \IR} . \end{equation*}
  2. Since we can write
    \begin{equation*} \left[\begin{array}{c} -2a-b \\ -a \\ a \\ -b \\ b \end{array}\right] = a \left[\begin{array}{c} -2 \\ -1 \\ 1 \\ 0 \\ 0 \end{array}\right] + b \left[\begin{array}{c} -1 \\ 0 \\ 0 \\ -1 \\ 1 \end{array}\right], \end{equation*}
    a basis for the solution space is
    \begin{equation*} \left \{ \left[\begin{array}{c} -2 \\ -1 \\ 1 \\ 0 \\ 0 \end{array}\right] , \left[\begin{array}{c} -1 \\ 0 \\ 0 \\ -1 \\ 1 \end{array}\right] \right\}. \end{equation*}

Example C.1.12. AT1.

Answer the following questions about transformations.
  1. Consider the following maps of Euclidean vectors \(P:\mathbb R^3\rightarrow\mathbb R^3\) and \(Q:\mathbb R^3\rightarrow\mathbb R^3\) defined by
    \begin{equation*} P\left( \left[\begin{array}{c} x \\ y \\ z \end{array}\right] \right)= \left[\begin{array}{c} 3 \, x - y + z \\ 2 \, x - 2 \, y + 4 \, z \\ -2 \, x - 2 \, y - 3 \, z \end{array}\right] \hspace{1em} \text{and} \hspace{1em} Q\left( \left[\begin{array}{c} x \\ y \\ z \end{array}\right] \right)= \left[\begin{array}{c} y - 2 \, z \\ -3 \, x - 4 \, y + 12 \, z \\ 5 \, x y + 3 \, z \end{array}\right]. \end{equation*}
    Without writing a proof, explain why only one of these maps is likely to be a linear transformation.
  2. Consider the following map of Euclidean vectors \(S:\mathbb R^2\rightarrow\mathbb R^2\)
    \begin{equation*} S\left( \left[\begin{array}{c} x \\ y \end{array}\right] \right)=\left[\begin{array}{c} x + 2 \, y \\ -3 \, x y \end{array}\right]. \end{equation*}
    Prove that \(S\) is not a linear transformation.
  3. Consider the following map of Euclidean vectors \(T:\mathbb R^2\rightarrow\mathbb R^2\)
    \begin{equation*} T\left( \left[\begin{array}{c} x \\ y \end{array}\right] \right)=\left[\begin{array}{c} -4 \, x - 5 \, y \\ 2 \, x - 4 \, y \end{array}\right]. \end{equation*}
    Prove that \(T\) is a linear transformation.
Solution.
  1. A linear map between Euclidean spaces must consist of linear polynomials in each component. All three components of \(P\) are linear so \(P\) is likely to be linear; however, the third component of \(Q\) contains the nonlinear term \(xy\text{,}\) so \(Q\) is unlikely to be linear.
  2. We need to show either that \(S\) fails to preserve either vector addition or that \(S\) fails to preserve scalar multiplication.
    For example, for a scalar \(c \in \IR\) and a vector \(\left[\begin{array}{c}x \\y \end{array}\right] \in \IR^2\text{,}\) we can compute
    \begin{equation*} S\left(c\left[\begin{array}{c} x \\ y \end{array} \right]\right) = S\left(\left[\begin{array}{c} cx \\ cy \end{array} \right]\right) = \left[\begin{array}{c}cx+2cy \\ -3c^2xy \end{array} \right] \end{equation*}
    whereas
    \begin{equation*} cS\left(\left[\begin{array}{c} x \\ y \end{array} \right]\right) = c\left[\begin{array}{c}x+2y \\ -3xy \end{array} \right] = \left[\begin{array}{c}cx+2cy \\ -3cxy \end{array} \right]. \end{equation*}
    Since \(-3c^2xy \neq -3cxy\text{,}\) we see that \(S\left(c\left[\begin{array}{c} x \\ y \end{array} \right]\right) \neq cS\left(\left[\begin{array}{c} x \\ y \end{array} \right]\right)\text{,}\) so \(S\) fails to preserve scalar multiplication and cannot be a linear transformation.
    Alternatively, we could instead take two vectors \(\left[\begin{array}{c}x_1 \\y_1 \end{array}\right], \left[\begin{array}{c}x_2 \\y_2 \end{array}\right] \in \IR^2\) and compute
    \begin{equation*} S \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right] + \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) = S \left( \left[\begin{array}{c}x_1 +x_2 \\y_1 + y_2 \end{array}\right] \right) = \left[\begin{array}{c} (x_1+x_2)+2(y_1+y_2) \\ -3(x_1+x_2)(y_1+y_2) \end{array} \right] \end{equation*}
    whereas
    \begin{equation*} S \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right]\right) +S\left( \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) = \left[\begin{array}{c} x_1+2y_1 \\ -3x_1y_1 \end{array}\right] + \left[\begin{array}{c} x_2+2y_2 \\ -3x_2y_2 \end{array}\right] = \left[\begin{array}{c} x_1+2y_1+x_2+2y_2 \\ -3x_1y_1-3x_2y_2 \end{array} \right] \end{equation*}
    Since \(-3(x_1+x_2)(y_1+y_2) \neq -3x_1y_1-3x_2y_2 \text{,}\) we see that \(S \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right] + \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) \neq S \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right]\right) +S\left( \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) \text{,}\) so \(S\) fails to preserve addition and cannot be a linear transformation.
  3. We need to show that \(T\) preserves both vector addition and that \(T\) preserves scalar multiplication.
    First, let us take two vectors \(\left[\begin{array}{c}x_1 \\y_1 \end{array}\right], \left[\begin{array}{c}x_2 \\y_2 \end{array}\right] \in \IR^2\) and compute
    \begin{equation*} T \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right] + \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) = T \left( \left[\begin{array}{c}x_1 +x_2 \\y_1 + y_2 \end{array}\right] \right) = \left[\begin{array}{c} -4(x_1+x_2)-5(y_1+y_2) \\ 2(x_1+x_2)-4(y_1+y_2)\end{array} \right] \end{equation*}
    and
    \begin{equation*} T \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right]\right) +T\left( \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) = \left[\begin{array}{c} -4x_1-5y_1 \\ 2x_1-4y_1 \end{array}\right] + \left[\begin{array}{c} -4x_2-5y_2 \\ 2x_2-4y_2 \end{array}\right] = \left[\begin{array}{c} -4x_1-5y_1-4x_2-5y_2 \\ 2x_1-4y_1+2x_2-4y_2\end{array} \right] \end{equation*}
    So we see that \(T \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right] + \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) = T \left( \left[\begin{array}{c}x_1 \\y_1 \end{array}\right]\right) +T\left( \left[\begin{array}{c}x_2 \\y_2 \end{array}\right]\right) \text{,}\) so \(T\) preserves addition.
    Now, take a scalar \(c \in \IR\) and a vector \(\left[\begin{array}{c}x \\y \end{array}\right] \in \IR^2\text{,}\) and compute
    \begin{equation*} T\left(c\left[\begin{array}{c} x \\ y \end{array} \right]\right) = T\left(\left[\begin{array}{c} cx \\ cy \end{array} \right]\right) = \left[\begin{array}{c} -4cx-5cy \\ 2cx-4cy\end{array} \right] \end{equation*}
    and
    \begin{equation*} cT\left(\left[\begin{array}{c} x \\ y \end{array} \right]\right) = c\left[\begin{array}{c}-4x-5y \\ 2x-4y \end{array} \right] = \left[\begin{array}{c}-4cx-5cy \\ 2cx-4cy\end{array} \right]. \end{equation*}
    We see that \(T\left(c\left[\begin{array}{c} x \\ y \end{array} \right]\right) = cT\left(\left[\begin{array}{c} x \\ y \end{array} \right]\right)\text{,}\) so \(T\) preserves scalar multiplication.
    Since \(T\) preserves both addition and scalar multiplication, we have proven that \(T\) is a linear transformation.

Example C.1.13. AT2.

  1. Find the standard matrix for the linear transformation \(T: \IR^3\rightarrow \IR^4\) given by
    \begin{equation*} T\left(\left[\begin{array}{c} x \\ y \\ z \\ \end{array}\right] \right) = \left[\begin{array}{c} -x+y \\ -x+3y-z \\ 7x+y+3z \\ 0 \end{array}\right]. \end{equation*}
  2. Let \(S: \IR^4 \rightarrow \IR^3\) be the linear transformation given by the standard matrix
    \begin{equation*} \left[\begin{array}{cccc} 2 & 3 & 4 & 1 \\ 0 & 1 & -1 & -1 \\ 3 & -2 & -2 & 4 \end{array}\right]. \end{equation*}
    Compute \(S\left( \left[\begin{array}{c} -2 \\ 1 \\ 3 \\ 2\end{array}\right] \right) \text{.}\)
Solution.
  1. Since
    \begin{align*} T\left(\left[\begin{array}{c} 1 \\ 0 \\ 0 \end{array}\right]\right) &= \left[\begin{array}{c} -1 \\ -1 \\ 7 \\0\end{array}\right]\\ T\left(\left[\begin{array}{c} 0 \\ 1 \\ 0 \end{array}\right]\right) &= \left[\begin{array}{c} 1 \\ 3 \\ 1 \\0 \end{array}\right]\\ T\left(\left[\begin{array}{c} 0 \\ 0 \\ 1 \end{array}\right]\right) &= \left[\begin{array}{c} 0 \\ -1 \\ 3 \\ 0 \end{array}\right], \end{align*}
    the standard matrix for \(T\) is \(\left[\begin{array}{ccc} -1 & 1 & 0 \\ -1 & 3 & -1 \\ 7 & 1 & 3 \\ 0 & 0 & 0 \end{array}\right] \text{.}\)
  2. \begin{equation*} S\left(\left[\begin{array}{c} -2 \\ 1 \\ 3 \\ 2 \end{array}\right] \right) = -2S(\vec{e}_1)+S(\vec{e}_2)+3S(\vec{e}_3)+2S(\vec{e}_4) \end{equation*}
    \begin{equation*} = -2 \left[\begin{array}{c} 2 \\ 0 \\ 3 \end{array}\right] + \left[\begin{array}{c} 3 \\ 1 \\ -2 \end{array}\right] + 3 \left[\begin{array}{c} 4 \\ -1 \\ -2 \end{array}\right]+2\left[\begin{array}{c} 1 \\ -1 \\ 4 \end{array}\right] = \left[\begin{array}{c} 13 \\ -4 \\ -6\end{array}\right]. \end{equation*}

Example C.1.14. AT3.

Let \(T: \IR^4 \rightarrow \IR^3\) be the linear transformation given by
\begin{equation*} T\left(\left[\begin{array}{c}x\\y\\z\\w\end{array}\right] \right) = \left[\begin{array}{c} x+3y+2z-3w \\ 2x+4y+6z-10w \\ x+6y-z+3w \end{array}\right] \end{equation*}
  1. Explain how to find the image of \(T\) and the kernel of \(T\text{.}\)
  2. Explain how to find a basis of the image of \(T\) and a basis of the kernel of \(T\text{.}\)
  3. Explain how to find the rank and nullity of T, and why the rank-nullity theorem holds for T.
Solution.
  1. To find the image we compute
    \begin{equation*} \Im(T) = T\left(\vspan\left\{\vec{e}_1,\vec{e}_2,\vec{e}_3,\vec{e}_4\right\}\right) \end{equation*}
    \begin{equation*} = \vspan\left\{T(\vec{e}_1),T(\vec{e}_2),T(\vec{e}_3),T(\vec{e}_4)\right\} \end{equation*}
    \begin{equation*} = \vspan\left\{\left[\begin{array}{c}1 \\ 2 \\ 1 \end{array}\right], \left[\begin{array}{c} 3 \\ 4 \\ 6 \end{array}\right], \left[\begin{array}{c} 2 \\ 6 \\ -1 \end{array}\right], \left[\begin{array}{c} -3 \\ -10 \\ 3 \end{array}\right]\right\}. \end{equation*}
  2. The kernel is the solution set of the corresponding homogeneous system of equations, i.e.
    \begin{alignat*}{5} x &+& 3y &+& 2z &-& 3w &=& 0\\ 2x &+& 4y &+& 6z &-& 10w &=& 0\\ x &+& 6y &-& z &+& 3w &=& 0 . \end{alignat*}
    So we compute
    \begin{equation*} \RREF\left[\begin{array}{cccc|c} 1 & 3 & 2 & -3 & 0 \\ 2 & 4 & 6 & -10 &0 \\ 1 & 6 & -1 & 3 & 0 \end{array}\right] = \left[\begin{array}{cccc|c} 1 & 0 & 5 & -9 & 0 \\ 0 & 1 & -1 & 2 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{array}\right]. \end{equation*}
    Then, letting \(z=a\) and \(w=b\) we have
    \begin{equation*} \ker T = \setBuilder{\left[\begin{array}{c}-5a+9b \\ a-2b \\ a \\ b \end{array}\right]}{a,b \in \IR}. \end{equation*}
  3. Since \(\Im(T) = \vspan\left\{\left[\begin{array}{c}1 \\ 2 \\ 1 \end{array}\right], \left[\begin{array}{c} 3 \\ 4 \\ 6 \end{array}\right], \left[\begin{array}{c} 2 \\ 6 \\ -1 \end{array}\right], \left[\begin{array}{c} -3 \\ -10 \\ 3 \end{array}\right]\right\}\text{,}\) we simply need to find a linearly independent subset of these four spanning vectors. So we compute
    \begin{equation*} \RREF \left[\begin{array}{cccc}1 & 3 & 2 & -3 \\ 2 & 4 & 6 & -10 \\ 1 & 6 & -1 & 3 \end{array}\right] = \left[\begin{array}{cccc} 1 & 0 & 5 & -9 \\ 0 & 1 & -1 & 2 \\ 0 & 0 & 0 & 0\end{array}\right]. \end{equation*}
    Since the first two columns are pivot columns, they form a linearly independent spanning set, so a basis for \(\Im T\) is \(\setList{\left[\begin{array}{c}1\\2\\1 \end{array}\right], \left[\begin{array}{c}3\\4\\6 \end{array}\right]}.\)
    To find a basis for the kernel, note that
    \begin{equation*} \ker T = \setBuilder{\left[\begin{array}{c}-5a+9b \\ a-2b \\ a \\ b \end{array}\right]}{a,b \in \IR} \end{equation*}
    \begin{equation*} = \setBuilder{a \left[\begin{array}{c}-5 \\ 1 \\ 1 \\ 0 \end{array}\right]+b \left[\begin{array}{c} 9 \\ -2 \\ 0 \\ 1 \end{array}\right]}{a,b \in \IR} \end{equation*}
    \begin{equation*} = \vspan\left\{ \left[\begin{array}{c} -5 \\ 1 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c} 9 \\ -2 \\ 0 \\ 1 \end{array}\right]\right\}. \end{equation*}
    so a basis for the kernel is
    \begin{equation*} \setList{\left[\begin{array}{c}-5 \\ 1 \\ 1 \\ 0 \end{array}\right], \left[\begin{array}{c}9 \\ -2 \\ 0 \\ 1 \end{array}\right]}. \end{equation*}
  4. The dimension of the image (the rank) is \(2\text{,}\) the dimension of the kernel (the nullity) is \(2\text{,}\) and the dimension of the domain of \(T\) is \(4\text{,}\) so we see \(2+2=4\text{,}\) which verifies that the sum of the rank and nullity of \(T\) is the dimension of the domain of \(T\text{.}\)

Example C.1.15. AT4.

Let \(T: \IR^4 \rightarrow \IR^3\) be the linear transformation given by the standard matrix \(\left[\begin{array}{cccc} 1 & 3 & 2 & -3 \\ 2 & 4 & 6 & -10 \\ 1 & 6 & -1 & 3 \end{array}\right]\text{.}\)
  1. Explain why \(T\) is or is not injective.
  2. Explain why \(T\) is or is not surjective.
Solution.
Compute
\begin{equation*} \RREF\left[\begin{array}{cccc}1 & 3 & 2 & -3 \\ 2 & 4 & 6 & -10 \\ 1 & 6 & -1 & 3 \end{array}\right] = \left[\begin{array}{cccc} 1 & 0 & 5 & -9 \\ 0 & 1 & -1 & 2 \\ 0 & 0 & 0 & 0\end{array}\right]. \end{equation*}
  1. Note that the third and fourth columns are non-pivot columns, which means \(\ker T\) contains infinitely many vectors, so \(T\) is not injective.
  2. Since there are only two pivots, the image (i.e. the span of the columns) is a 2-dimensional subspace (and thus does not equal \(\IR^3\)), so \(T\) is not surjective.

Example C.1.16. AT5.

Let \(V\) be the set of all pairs of numbers \((x,y)\) of real numbers together with the following operations:
\begin{align*} (x_1,y_1) \oplus (x_2,y_2) &= (2x_1+2x_2,2y_1+2y_2)\\ c\odot (x,y) &= (cx,c^2y) \end{align*}
  1. Show that scalar multiplication distributes over vector addition:
    \begin{equation*} c\odot \left((x_1,y_1) \oplus (x_2,y_2) \right) = c \odot (x_1,y_1) \oplus c \odot (x_2,y_2) \end{equation*}
  2. Explain why \(V\) nonetheless is not a vector space.
Solution.
  1. We compute both sides:
    \begin{align*} c \odot \left((x_1,y_1) \oplus (x_2,y_2) \right) &= c \odot (2x_1+2x_2,2y_1+2y_2)\\ &= (c(2x_1+2x_2),c^2(2y_1+2y_2))\\ &= (2cx_1+2cx_2,2c^2y_1+2c^2y_2) \end{align*}
    and
    \begin{align*} c\odot (x_1,y_1) \oplus c \odot (x_2,y_2) &= (cx_1,c^2y_1) \oplus (cx_2,c^2y_2)\\ &= (2cx_1+2cx_2,2c^2y_1+2c^2y_2) \end{align*}
    Since these are the same, we have shown that the property holds.
  2. To show \(V\) is not a vector space, we must show that it fails one of the 8 defining properties of vector spaces. We will show that scalar multiplication does not distribute over scalar addition, i.e., there are values such that
    \begin{equation*} (c+d)\odot(x,y) \neq c \odot(x,y) \oplus d\odot(x,y) \end{equation*}
    • (Solution method 1) First, we compute
      \begin{align*} (c+d)\odot(x,y) &= ((c+d)x,(c+d)^2y)\\ &= ( (c+d)x, (c^2+2cd+d^2)y). \end{align*}
      Then we compute
      \begin{align*} c\odot (x,y) \oplus d\odot(x,y) &= (cx,c^2y) \oplus (dx,d^2y)\\ &= ( 2cx+2dx, 2c^2y+2d^2y). \end{align*}
      Since \((c+d)x\not=2cx+2dy\) when \(c,d,x,y=1\text{,}\) the property fails to hold.
    • (Solution method 2) When we let \(c,d,x,y=1\text{,}\) we may simplify both sides as follows.
      \begin{align*} (c+d)\odot(x,y) &= 2\odot(1,1)\\ &= (2\cdot1,2^2\cdot1)\\ &=(2,4) \end{align*}
      \begin{align*} c\odot (x,y) \oplus d\odot(x,y) &= 1\odot(1,1)\oplus 1\odot(1,1)\\ &= (1\cdot1,1^2\cdot1)\oplus(1\cdot1,1^2\cdot1)\\ &= (1,1)\oplus(1,1)\\ &= (2\cdot1+2\cdot1,2\cdot1+2\cdot1)\\ &= (4,4) \end{align*}
      Since these ordered pairs are different, the property fails to hold.

Example C.1.17. AT6.

  1. Given the set
    \begin{equation*} \left\{ x^{3} - 2 \, x^{2} + x + 2 , 2 \, x^{2} - 1 , -x^{3} + 3 \, x^{2} + 3 \, x - 2 , x^{3} - 6 \, x^{2} + 9 \, x + 5 \right\} \end{equation*}
    write a statement involving the solutions to a polynomial equation that’s equivalent to each claim below.
    • The set of polynomials is linearly independent.
    • The set of polynomials is linearly dependent.
  2. Explain how to determine which of these statements is true.
Solution.
The set of polynomials
\begin{equation*} \left\{ x^{3} - 2 \, x^{2} + x + 2 , 2 \, x^{2} - 1 , -x^{3} + 3 \, x^{2} + 3 \, x - 2 , x^{3} - 6 \, x^{2} + 9 \, x + 5 \right\} \end{equation*}
is linearly independent exactly when the polynomial equation
\begin{equation*} y_1\left( x^{3} - 2 \, x^{2} + x + 2 \right)+y_2\left( 2 \, x^{2} - 1 \right)+y_3\left( -x^{3} + 3 \, x^{2} + 3 \, x - 2 \right)+y_4\left( x^{3} - 6 \, x^{2} + 9 \, x + 5\right)=0 \end{equation*}
has no nontrivial (i.e. nonzero) solutions. The set is linearly dependent when this equation has a nontrivial (i.e. nonzero) solution.
To solve this equation, we distribute and then collect coefficients to obtain
\begin{equation*} \left(y_1-y_3+y_4\right)x^3+\left(-2y_1+2y_2+3y_3-6y_4\right)x^2+\left(y_1+3y_3+9y_4\right)x+\left(2y_1-y_2-2y_3+5y_4\right)=0\text{.} \end{equation*}
These polynomials are equal precisely when their coefficients are equal, leading to the system
\begin{equation*} \begin{matrix} y_1 & & &-&y_3 & +&y_4 & = & 0 \\ -2 y_1 & + & 2y_2 &+&3y_3 & -&6y_4 & = & 0 \\ y_1 & + & &+&3y_3 & +&9y_4 & = & 0 \\ 2 y_1 & - & y_2 &-&2y_3 & +&5y_4 & = & 0 \end{matrix}\text{.} \end{equation*}
To solve this, we compute
\begin{equation*} \mathrm{RREF}\, \left[\begin{array}{cccc|c} 1 & 0 & -1 & 1 & 0\\ -2 & 2 & 3 & -6 & 0\\ 1 & 0 & 3 & 9 & 0\\ 2 & -1 & -2 & 5 & 0 \end{array}\right] = \left[\begin{array}{cccc|c} 1 & 0 & 0 & 3 & 0\\ 0 & 1 & 0 & -3 & 0\\ 0 & 0 & 1 & 2 & 0\\ 0 & 0 & 0 & 0 & 0 \end{array}\right] \end{equation*}
The system has (infintely many) nontrivial solutions, so we that the set of polynomials is linearly dependent.

Example C.1.18. MX1.

Of the following three matrices, only two may be multiplied.
\begin{align*} A &= \left[\begin{array}{cc} 1 & -3 \\ 0 & 1 \end{array}\right] & B&= \left[\begin{array}{ccc} 4 & 1 & 2 \end{array}\right] & C&= \left[\begin{array}{ccc} 0 & 1 & 3 \\ 1 & -2 & 5 \end{array}\right] \end{align*}
Explain which two may be multiplied and why. Then show how to find their product.
Solution.
\(AC\) is the only one that can be computed, since \(C\) corresponds to a linear transformation \(\mathbb{R}^3 \rightarrow \mathbb{R}^2\) and \(A\) corresponds to a linear transfromation \(\mathbb{R}^2 \rightarrow \mathbb{R}^2\text{.}\) Thus the composition \(AC\) corresponds to a linear transformation \(\mathbb{R}^3 \rightarrow \mathbb{R}^2\) with a \(2\times 3\) standard matrix. We compute
\begin{align*} AC\left( \vec{e}_1 \right) &= A \left( \left[\begin{array}{c} 0 \\ 1 \end{array}\right] \right) = 0 \left[\begin{array}{c} 1 \\ 0 \end{array}\right] + 1\left[\begin{array}{c} -3 \\ 1 \end{array}\right] = \left[\begin{array}{c} -3 \\ 1 \end{array}\right] \\\\ AC\left( \vec{e}_2 \right) &= A \left( \left[\begin{array}{c} 1 \\ -2 \end{array}\right] \right) = 1 \left[\begin{array}{c} 1 \\ 0 \end{array}\right] -2\left[\begin{array}{c} -3 \\ 1 \end{array}\right] = \left[\begin{array}{c} 7 \\ -2 \end{array}\right] \\\\ AC\left( \vec{e}_3 \right) &= A \left( \left[\begin{array}{c} 3 \\ 5 \end{array}\right] \right) = 3 \left[\begin{array}{c} 1 \\ 0 \end{array}\right] + 5\left[\begin{array}{c} -3 \\ 1 \end{array}\right] = \left[\begin{array}{c} -12 \\ 5 \end{array}\right] \\\text{.} \end{align*}
Thus
\begin{equation*} AC = \left[\begin{array}{ccc} -3 & 7 & -12 \\ 1 & -2 & 5 \end{array}\right]. \end{equation*}

Example C.1.19. MX2.

Explain why each of the following matrices is or is not invertible by disussing its corresponding linear transformation. If the matrix is invertible, explain how to find its inverse.
\begin{equation*} \hspace{2em} D = \left[\begin{array}{cccc} -1 & 1 & 0 & 2 \\ -2 & 5 & 5 & -4 \\ 2 & -3 & -2 & 0 \\ 4 & -4 & -3 & 5 \end{array}\right] \hspace{2em} N = \left[\begin{array}{cccc} -3 & 9 & 1 & -11 \\ 3 & -9 & -2 & 13 \\ 3 & -9 & -3 & 15 \\ -4 & 12 & 2 & -16 \end{array}\right] \hspace{2em} \end{equation*}
Solution.
We compute
\begin{equation*} \mathrm{RREF}\left(D\right)=\left[\begin{array}{cccc} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{array}\right]\text{.} \end{equation*}
We see \(D\) is bijective, and therefore invertible. To compute the inverse, we solve \(D\vec{x}=\vec{e}_1\) by computing
\begin{equation*} \mathrm{RREF}\,\left[\begin{array}{cccc|c} -1 & 1 & 0 & 2 & 1\\ -2 & 5 & 5 & -4 & 0 \\ 2 & -3 & -2 & 0 & 0\\ 4 & -4 & -3 & 5 & 0 \end{array}\right]=\left[\begin{array}{cccc|c} 1 & 0 & 0 & 0 & 21 \\ 0 & 1 & 0 & 0 & 38\\ 0 & 0 & 1 & 0 & -36\\ 0 & 0 & 0 & 1 & -8 \end{array}\right]\text{.} \end{equation*}
Similarly, we solve \(D\vec{x}=\vec{e}_2\) by computing
\begin{equation*} \mathrm{RREF}\,\left[\begin{array}{cccc|c} -1 & 1 & 0 & 2 & 0\\ -2 & 5 & 5 & -4 & 1 \\ 2 & -3 & -2 & 0 & 0\\ 4 & -4 & -3 & 5 & 0 \end{array}\right]=\left[\begin{array}{cccc|c} 1 & 0 & 0 & 0 & 8 \\ 0 & 1 & 0 & 0 & 14\\ 0 & 0 & 1 & 0 & -13\\ 0 & 0 & 0 & 1 & -3 \end{array}\right]\text{.} \end{equation*}
Similarly, we solve \(D\vec{x}=\vec{e}_3\) by computing
\begin{equation*} \mathrm{RREF}\,\left[\begin{array}{cccc|c} -1 & 1 & 0 & 2 & 0\\ -2 & 5 & 5 & -4 & 0 \\ 2 & -3 & -2 & 0 & 1\\ 4 & -4 & -3 & 5 & 0 \end{array}\right]=\left[\begin{array}{cccc|c} 1 & 0 & 0 & 0 & 23 \\ 0 & 1 & 0 & 0 & 41\\ 0 & 0 & 1 & 0 & -39\\ 0 & 0 & 0 & 1 & -9 \end{array}\right]\text{.} \end{equation*}
Similarly, we solve \(D\vec{x}=\vec{e}_4\) by computing
\begin{equation*} \mathrm{RREF}\,\left[\begin{array}{cccc|c} -1 & 1 & 0 & 2 & 0\\ -2 & 5 & 5 & -4 & 0 \\ 2 & -3 & -2 & 0 & 0\\ 4 & -4 & -3 & 5 & 1 \end{array}\right]=\left[\begin{array}{cccc|c} 1 & 0 & 0 & 0 & -2 \\ 0 & 1 & 0 & 0 & -4\\ 0 & 0 & 1 & 0 & 4\\ 0 & 0 & 0 & 1 & 1 \end{array}\right]\text{.} \end{equation*}
Combining these, we obtain
\begin{equation*} D^{-1}=\left[\begin{array}{cccc} 21 & 8 & 23 & -2 \\ 38 & 14 & 41 & -4 \\ -36 & -13 & -39 & 4 \\ -8 & -3 & -9 & 1 \end{array}\right]\text{.} \end{equation*}
We compute
\begin{equation*} \mathrm{RREF}\left(N\right)=\left[\begin{array}{cccc} 1 & -3 & 0 & 3 \\ 0 & 0 & 1 & -2 \\ 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 \end{array}\right]\text{.} \end{equation*}
We see \(N\) is not bijective and thus is not invertible.

Example C.1.20. MX3.

Use a matrix inverse to solve the following matrix-vector equation.
\begin{equation*} \left[\begin{array}{ccc} 1& 2& 1\\ 0& 0& 2\\ 1& 1& 1\\ \end{array}\right] \vec{v} = \left[\begin{array}{c}4\\ -2 \\ 2 \end{array}\right] \end{equation*}
Solution.
Using the techniques from section Section 4.3, and letting \(M = \left[\begin{array}{ccc} 1& 2& 1\\ 0& 0& 2\\ 1& 1& 1\\ \end{array}\right]\text{,}\) we find \(M^{-1} = \left[\begin{array}{ccc} -1& -1/2& 2\\ 1& 0& -1\\ 0& 1/2& 0\\ \end{array}\right]\text{.}\) Our equation can be written as \(M\vec{v} = \left[\begin{array}{c}4\\ -2 \\ 2 \end{array}\right]\text{,}\) and may therefore be solved via
\begin{equation*} \vec{v} = I\vec{v} = M^{-1}M\vec{v} = M^{-1}\left[\begin{array}{c}4\\ -2 \\ 2 \end{array}\right] = \left[\begin{array}{c}1\\ 2 \\ -1 \end{array}\right] \end{equation*}

Example C.1.21. MX4.

Let \(A\) be a \(4\times4\) matrix.
  1. Give a \(4\times 4\) matrix \(P\) that may be used to perform the row operation \({R_3} \to R_3+4 \, {R_1} \text{.}\)
  2. Give a \(4\times 4\) matrix \(Q\) that may be used to perform the row operation \({R_1} \to -4 \, {R_1}\text{.}\)
  3. Use matrix multiplication to describe the matrix obtained by applying \({R_3} \to 4 \, {R_1} + {R_3}\) and then \({R_1} \to -4 \, {R_1}\) to \(A\) (note the order).
Solution.
  1. \(\displaystyle P=\left[\begin{array}{cccc} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 \\ 4 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{array}\right]\)
  2. \(\displaystyle Q=\left[\begin{array}{cccc} -4 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1\end{array}\right]\)
  3. \(\displaystyle QPA\)

Example C.1.22. GT1.

Let \(A\) be a \(4 \times 4\) matrix with determinant \(-7\text{.}\)
  1. Let \(B\) be the matrix obtained from \(A\) by applying the row operation \(R_3+3R_4 \to R_3\text{.}\) What is \(\det(B)\text{?}\)
  2. Let \(C\) be the matrix obtained from \(A\) by applying the row operation \(-3R_2 \to R_2\text{.}\) What is \(\det(C)\text{?}\)
  3. Let \(D\) be the matrix obtained from \(A\) by applying the row operation \(R_3 \leftrightarrow R_4\text{.}\) What is \(\det(D)\text{?}\)
Solution.
  1. Adding a multiple of one row to another row does not change the determinant, so \(\det(B)=\det(A)=-7\text{.}\)
  2. Scaling a row scales the determinant by the same factor, so so \(\det(B)=-3\det(A)=-3(-7)=21\text{.}\)
  3. Swaping rows changes the sign of the determinant, so \(\det(B)=-\det(A)=7\text{.}\)

Example C.1.23. GT2.

Show how to compute the determinant of the matrix
\begin{equation*} A = \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 1 & 1 & 2 & 4 \\ 1 & 1 & 1 & 3 \\ -3 & 1 & 2 & -5 \end{array}\right] \end{equation*}
Solution.
Here is one possible solution, first applying a single row operation, and then performing Laplace/cofactor expansions to reduce the determinant to a linear combination of \(2\times 2\) determinants:
\begin{align*} \det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 1 & 1 & 2 & 4 \\ 1 & 1 & 1 & 3 \\ -3 & 1 & 2 & -5 \end{array}\right] &= \det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 0 & 0 & 1 & 1 \\ 1 & 1 & 1 & 3 \\ -3 & 1 & 2 & -5 \end{array}\right] = (-1) \det \left[\begin{array}{ccc} 1 & 3 & -1 \\ 1 & 1 & 3 \\ -3 & 1 & -5 \end{array}\right] + (1) \det \left[\begin{array}{ccc} 1 & 3 & 0 \\ 1 & 1 & 1 \\ -3 & 1 & 2 \end{array}\right]\\ &= (-1) \left( (1) \det \left[\begin{array}{cc} 1 & 3 \\ 1 & -5 \end{array}\right] - (1) \det \left[\begin{array}{cc} 3 & -1 \\ 1 & -5 \end{array}\right] + (-3) \det \left[\begin{array}{cc} 3 & -1 \\ 1 & 3 \end{array}\right] \right) +\\ &\phantom{==} (1) \left( (1) \det \left[\begin{array}{cc} 1 & 1 \\ 1 & 2 \end{array}\right] - (3) \det \left[\begin{array}{cc} 1 & 1 \\ -3 & 2 \end{array}\right] \right)\\ % &= (-1)\left( (1)(-8)-(1)(-14)+(-3)(10) \right) + (1) \left( (1)(1)-(3)(5) \right)\\ &= (-1) \left( -8+14-30 \right) + (1) \left(1-15 \right)\\ &=10 \end{align*}
Here is another possible solution, using row and column operations to first reduce the determinant to a \(3\times 3\) matrix and then applying a formula:
\begin{align*} \det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 1 & 1 & 2 & 4 \\ 1 & 1 & 1 & 3 \\ -3 & 1 & 2 & -5 \end{array}\right] &= \det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 0 & 0 & 1 & 1 \\ 1 & 1 & 1 & 3 \\ -3 & 1 & 2 & -5 \end{array}\right] = \det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 0 & 0 & 1 & 0 \\ 1 & 1 & 1 & 2 \\ -3 & 1 & 2 & -7 \end{array}\right]\\ &=-\det \left[\begin{array}{cccc} 1 & 3 & 0 & -1 \\ 1 & 1 & 1 & 2 \\ 0 & 0 & 1 & 0 \\ -3 & 1 & 2 & -7 \end{array}\right] = -\det \left[\begin{array}{ccc} 1 & 3 & -1 \\ 1 & 1 & 2 \\ -3 & 1 & -7 \end{array}\right]\\ &=-((-7-18-1)-(3+2-21))\\ &=10 \end{align*}

Example C.1.24. GT3.

Explain how to find the eigenvalues of the matrix \(\left[\begin{array}{cc} -2 & -2 \\ 10 & 7 \end{array}\right] \text{.}\)
Solution.
Compute the characteristic polynomial:
\begin{equation*} \det(A-\lambda I) = \det \left[\begin{array}{cc} -2 - \lambda & -2 \\ 10 & 7-\lambda \end{array}\right] \end{equation*}
\begin{equation*} = (-2-\lambda)(7-\lambda)+20 = \lambda ^2 -5\lambda +6 = (\lambda -2)(\lambda -3) \end{equation*}
The eigenvalues are the roots of the characteristic polynomial, namely \(2\) and \(3\text{.}\)

Example C.1.25. GT4.

Explain how to find a basis for the eigenspace associated to the eigenvalue \(3\) in the matrix
\begin{equation*} \left[\begin{array}{ccc} -7 & -8 & 2 \\ 8 & 9 & -1 \\ \frac{13}{2} & 5 & 2 \end{array}\right]. \end{equation*}
Solution.
The eigenspace associated to \(3\) is the kernel of \(A-3I\text{,}\) so we compute
\begin{equation*} \RREF(A-3I) = \RREF \left[\begin{array}{ccc} -7-3 & -8 & 2 \\ 8 & 9-3 & -1 \\ \frac{13}{2} & 5 & 2-3 \end{array}\right] = \end{equation*}
\begin{equation*} \RREF \left[\begin{array}{ccc} -10 & -8 & 2 \\ 8 & 6 & -1 \\ \frac{13}{2} & 5 & -1 \end{array}\right] = \left[\begin{array}{ccc} 1 & 0 & 1 \\ 0 & 1 & -\frac{3}{2} \\ 0 & 0 & 0 \end{array}\right]. \end{equation*}
Thus we see the kernel is
\begin{equation*} \setBuilder{\left[\begin{array}{c} -a \\ \frac{3}{2} a \\ a \end{array}\right]}{a \in \IR} \end{equation*}
which has a basis of \(\left\{ \left[\begin{array}{c} -1 \\ \frac{3}{2} \\ 1 \end{array}\right] \right\}\text{.}\)