Skip to main content
Contents Index
Dark Mode Prev Up Next
\(\newcommand{\markedPivot}[1]{\boxed{#1}}
\newcommand{\IR}{\mathbb{R}}
\newcommand{\IC}{\mathbb{C}}
\renewcommand{\P}{\mathcal{P}}
\renewcommand{\Im}{\operatorname{Im}}
\newcommand{\RREF}{\operatorname{RREF}}
\newcommand{\vspan}{\operatorname{span}}
\newcommand{\setList}[1]{\left\{#1\right\}}
\newcommand{\setBuilder}[2]{\left\{#1\,\middle|\,#2\right\}}
\newcommand{\unknown}{\,{\color{gray}?}\,}
\newcommand{\drawtruss}[2][1]{
\begin{tikzpicture}[scale=#1, every node/.style={scale=#1}]
\draw (0,0) node[left,magenta]{C} --
(1,1.71) node[left,magenta]{A} --
(2,0) node[above,magenta]{D} -- cycle;
\draw (2,0) --
(3,1.71) node[right,magenta]{B} --
(1,1.71) -- cycle;
\draw (3,1.71) -- (4,0) node[right,magenta]{E} -- (2,0) -- cycle;
\draw[blue] (0,0) -- (0.25,-0.425) -- (-0.25,-0.425) -- cycle;
\draw[blue] (4,0) -- (4.25,-0.425) -- (3.75,-0.425) -- cycle;
\draw[thick,red,->] (2,0) -- (2,-0.75);
#2
\end{tikzpicture}
}
\newcommand{\trussNormalForces}{
\draw [thick, blue,->] (0,0) -- (0.5,0.5);
\draw [thick, blue,->] (4,0) -- (3.5,0.5);
}
\newcommand{\trussCompletion}{
\trussNormalForces
\draw [thick, magenta,<->] (0.4,0.684) -- (0.6,1.026);
\draw [thick, magenta,<->] (3.4,1.026) -- (3.6,0.684);
\draw [thick, magenta,<->] (1.8,1.71) -- (2.2,1.71);
\draw [thick, magenta,->] (1.6,0.684) -- (1.5,0.855);
\draw [thick, magenta,<-] (1.5,0.855) -- (1.4,1.026);
\draw [thick, magenta,->] (2.4,0.684) -- (2.5,0.855);
\draw [thick, magenta,<-] (2.5,0.855) -- (2.6,1.026);
}
\newcommand{\trussCForces}{
\draw [thick, blue,->] (0,0) -- (0.5,0.5);
\draw [thick, magenta,->] (0,0) -- (0.4,0.684);
\draw [thick, magenta,->] (0,0) -- (0.5,0);
}
\newcommand{\trussStrutVariables}{
\node[above] at (2,1.71) {\(x_1\)};
\node[left] at (0.5,0.866) {\(x_2\)};
\node[left] at (1.5,0.866) {\(x_3\)};
\node[right] at (2.5,0.866) {\(x_4\)};
\node[right] at (3.5,0.866) {\(x_5\)};
\node[below] at (1,0) {\(x_6\)};
\node[below] at (3,0) {\(x_7\)};
}
\newcommand{\N}{\mathbb N}
\newcommand{\Z}{\mathbb Z}
\newcommand{\Q}{\mathbb Q}
\newcommand{\R}{\mathbb R}
\DeclareMathOperator{\arcsec}{arcsec}
\DeclareMathOperator{\arccot}{arccot}
\DeclareMathOperator{\arccsc}{arccsc}
\newcommand{\tuple}[1]{\left\langle#1\right\rangle}
\newcommand{\lt}{<}
\newcommand{\gt}{>}
\newcommand{\amp}{&}
\definecolor{fillinmathshade}{gray}{0.9}
\newcommand{\fillinmath}[1]{\mathchoice{\colorbox{fillinmathshade}{$\displaystyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\textstyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\scriptstyle \phantom{\,#1\,}$}}{\colorbox{fillinmathshade}{$\scriptscriptstyle\phantom{\,#1\,}$}}}
\)
Section 2.7 Homogeneous Linear Systems (EV7)
Learning Outcomes
Find a basis for the solution set of a homogeneous system of equations.
Subsection 2.7.1 Warmup
Activity 2.7.2 .
(a)
\begin{equation*}
x_1 \vec{v}_1 + \cdots+x_n \vec{v}_n = \vec{0}
\end{equation*}
is a homogeneous vector equation, then:
The zero vector
\(\vec{0}\) is a solution;
The sum of any two solutions is again a solution;
Multiplying a solution by a scalar produces another solution.
(b)
Based on this recollection, which of the following best describes the solution set to the homogeneous equation?
A basis for
\(\IR^n\text{.}\)
A subspace of
\(\IR^n\text{.}\)
Subsection 2.7.2 Class Activities
Activity 2.7.3 .
Consider the homogeneous system of equations
\begin{alignat*}{5}
x_1&\,+\,&2x_2&\,\,& &\,+\,& x_4 &=& 0\\
2x_1&\,+\,&4x_2&\,-\,&x_3 &\,-\,&2 x_4 &=& 0\\
3x_1&\,+\,&6x_2&\,-\,&x_3 &\,-\,& x_4 &=& 0
\end{alignat*}
(a)
Find its solution set (a subspace of
\(\IR^4\) ).
(b)
Rewrite this solution space in the following forms
\begin{equation*}
\setBuilder{ a \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\end{array}\right] + b \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown \end{array}\right] }{a,b \in \IR}
\end{equation*}
\begin{equation*}
=
\vspan \left\{\left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\end{array}\right], \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown \end{array}\right]\right\}\text{.}
\end{equation*}
(c)
So, how can we use this to find a basis for the solution space?
Take RREF of an appropriate matrix and remove the vectors that caused a non-pivot row.
Take RREF of an appropriate matrix and remove the vectors that caused a non-pivot column.
Take RREF of an appropriate matrix and remove the vectors that caused a non-pivot row or column.
This set cannot be a basis for the solution space because it will always have in a zero row in the RREF.
Answer .
This is exactly the technique used in
SectionΒ 2.6 to find a basis from a set of spanning vectors.
Activity 2.7.5 .
Consider the homogeneous system of equations
\begin{alignat*}{8}
2x_1&\,+\,&4x_2&\,+\,&2x_3 &\,-\,&3 x_4 &\,+\,&31x_5&\,+\,&2x_6&\,-\,&16x_7&=& 0\\
-1x_1&\,-\,&2x_2&\,+\,&4x_3 &\,-\,&x_4 &\,+\,&2x_5&\,+\,&9x_6&\,+\,&3x_7&=& 0\\
x_1&\,+\,&2x_2&\,+\,&x_3 &\,+\,& x_4 &\,+\,&3x_5&\,+\,&6x_6&\,+\,&7x_7&=& 0
\end{alignat*}
(a)
Find its solution set (a subspace of
\(\IR^7\) ).
(b)
Rewrite this solution space in the following forms:
\begin{equation*}
\setBuilder{ a \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right] + b \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right]+c \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right]+d \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right] }{a,b,c,d \in \IR}.
\end{equation*}
\begin{equation*}
=\vspan\left\{\left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right], \left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right],\left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right],\left[\begin{array}{c} \unknown \\ \unknown \\ \unknown \\ \unknown\\ \unknown\\ \unknown \\ \unknown\end{array}\right]\right\}\text{.}
\end{equation*}
(c)
Find a basis for this solution space.
Fact 2.7.6 .
The coefficients of the free variables in the solution space of a linear system always yield linearly independent vectors that span the solution space.
Thus if
\begin{equation*}
\setBuilder{
a \left[\begin{array}{c} -2 \\ 1 \\ 0 \\ 0\\0\\0\\0\end{array}\right] +
b \left[\begin{array}{c} -7 \\ 0 \\ -1 \\ 5\\1\\0\\0 \end{array}\right]+
c \left[\begin{array}{c} -1 \\ 0 \\ -3 \\ -2\\0\\1\\0 \end{array}\right]+
d \left[\begin{array}{c} 1 \\ 0 \\ -2 \\ -6\\0\\0\\1 \end{array}\right]
}{
a,b,c,d \in \IR
} = \vspan\left\{ \left[\begin{array}{c} -2 \\ 1 \\ 0 \\ 0\\0\\0\\0\end{array}\right],
\left[\begin{array}{c} -7 \\ 0 \\ -1 \\ 5\\1\\0\\0 \end{array}\right],
\left[\begin{array}{c} -1 \\ 0 \\ -3 \\ -2\\0\\1\\0 \end{array}\right],
\left[\begin{array}{c} 1 \\ 0 \\ -2 \\ -6\\0\\0\\1 \end{array}\right] \right\}
\end{equation*}
is the solution space for a homogeneous system, then
\begin{equation*}
\setList{
\left[\begin{array}{c} -2 \\ \textcolor{blue}{1} \\ 0 \\ 0\\\textcolor{blue}{0}\\\textcolor{blue}{0}\\\textcolor{blue}{0}\end{array}\right],
\left[\begin{array}{c} -7 \\ \textcolor{blue}{0} \\ -1 \\ 5\\\textcolor{blue}{1}\\\textcolor{blue}{0}\\\textcolor{blue}{0} \end{array}\right],
\left[\begin{array}{c} -1 \\ \textcolor{blue}{0} \\ -3 \\ -2\\\textcolor{blue}{0}\\\textcolor{blue}{1}\\\textcolor{blue}{0} \end{array}\right],
\left[\begin{array}{c} 1 \\ \textcolor{blue}{0} \\ -2 \\ -6\\\textcolor{blue}{0}\\\textcolor{blue}{0}\\\textcolor{blue}{1} \end{array}\right]
}
\end{equation*}
is a basis for the solution space.
Activity 2.7.7 .
Consider the homogeneous system of equations
\begin{alignat*}{5}
x_1&\,-\,&3x_2&\,+\,& 2x_3 &=& 0\\
2x_1&\,+\,&6x_2&\,+\,&4x_3 &=& 0\\
x_1&\,+\,&6x_2&\,-\,&4x_3 &=& 0
\end{alignat*}
(a)
(b)
Which of these is the best choice of basis for this solution space?
\(\displaystyle \{\}\)
\(\displaystyle \{\vec 0\}\)
The basis does not exist
Answer .
In
SectionΒ 2.4 we established that sets that contain the zero vector cannot be independent.
Activity 2.7.8 .
To create a computer-animated film, an animator first models a scene as a subset of
\(\mathbb R^3\text{.}\) Then to transform this three-dimensional visual data for display on a two-dimensional movie screen or television set, the computer could apply a linear transformation that maps visual information at the vector
\(\left[\begin{array}{c}x\\y\\z\end{array}\right]\in\mathbb R^3\) onto the pixel located at
\(\left[\begin{array}{c}x+y\\y-z\end{array}\right]\in\mathbb R^2\text{.}\)
(a)
What homogeneous linear system describes the positions
\(\left[\begin{array}{c}x\\y\\z\end{array}\right]\) within the original scene that would be aligned with the pixel
\(\left[\begin{array}{c}0\\0\end{array}\right]\) on the screen?
Answer .
\begin{equation*}
x+y=0
\end{equation*}
\begin{equation*}
y-z=0
\end{equation*}
(b)
Find a basis for the solution set of this system. What best describes the shape of the data that gets projected onto this single point of the screen?
Answer .
The basis
\(\left\{\left[\begin{array}{c}-1\\1\\1\end{array}\right]\right\}\) describes a one-dimensional line.
Subsection 2.7.3 Individual Practice
Activity 2.7.9 .
Let \(S=\setList{
\left[\begin{array}{c} -2 \\ 1 \\ 0 \\ 0\end{array}\right],
\left[\begin{array}{c} -1 \\ 0 \\ -4 \\ 1 \end{array}\right],
\left[\begin{array}{c} 1 \\ 0 \\ -2 \\ 3 \end{array}\right]
}\) and \(A=\left[\begin{array}{ccc}
-2 & -1 &1\\
1 & 0 &0\\
0 & -4 &-2\\
0 & 1 &3
\end{array}\right];\) note that
\begin{equation*}
\RREF(A)=\left[\begin{array}{ccc}
1 & 0 &0\\
0 & 1 &0\\
0 & 0 &1\\
0 & 0 &0
\end{array}\right].
\end{equation*}
The following statements are all invalid for at least one reason. Determine what makes them invalid and, suggest alternative valid statements that the author may have meant instead.
(a)
The matrix
\(A\) is linearly independent because
\(\RREF(A)\) has a pivot in each column.
(b)
The matrix
\(A\) does not span
\(\IR^4\) because
\(\RREF(A)\) has a row of zeroes.
(c)
The set of vectors
\(S\) spans.
(d)
The set of vectors
\(S\) is a basis.
Subsection 2.7.4 Videos
Figure 23. Video: Polynomial and matrix calculations
Subsection 2.7.5 Exercises
Subsection 2.7.6 Mathematical Writing Explorations
Exploration 2.7.10 .
An \(n \times n\) matrix \(M\) is non-singular if the associated homogeneous system with coefficient matrix \(M\) is consistent with one solution. Assume the matrices in the writing explorations in this section are all non-singular.
Prove that the reduced row echelon form of \(M\) is the identity matrix.
Prove that, for any column vector \(\vec{b} = \left[\begin{array}{c}b_1\\b_2\\ \vdots \\b_n \end{array}\right]\text{,}\) the system of equations given by \(\left[\begin{array}{c|c}M & \vec{b}\end{array}
\right]\) has a unique solution.
Prove that the columns of \(M\) form a basis for \(\mathbb{R}^n\text{.}\)
Prove that the reduced row echelon form of \(M\) has \(n\) pivots.
Subsection 2.7.7 Sample Problem and Solution