Solution Set of Linear Equation System

2.5: Solution Sets for Systems of Linear Equations

Last updated
Save as PDF

Page ID: 1781

$wally.jpg$
Professor (Mathematics) at University of California, Davis

Algebra problems can have multiple solutions. For example $x(x-1)=0$ has two solutions: $0$ and $1$. By contrast, equations of the form $Ax=b$ with $A$ a linear operator have have the following property.

If $A$ is a linear operator and $b$ is a known then $Ax=b$ has either

[1.] One solution

[2.] No solutions

[3.] Infinitely many solutions

2.5.1: The Geometry of Solution Sets: Hyperplanes

Consider the following algebra problems and their solutions

[1.] $6x=12$, one solution: $2$

[2a.] $0x=12$, no solution

[2b.] $0x=0$, one solution for each number: $x$

In each case the linear operator is a $1\times 1$ matrix. In the first case, the linear operator is invertible. In the other two cases it is not. In the first case, the solution set is a point on the number line, in the third case the solution set is the whole number line.

Lets examine similar situations with larger matrices.

[1.] $\begin{pmatrix}6 &0 \\0 &2 \end{pmatrix}\begin{pmatrix} x \\ y \end{pmatrix} =\begin{pmatrix}12 \\ 6\end{pmatrix}$, one solution: $\begin{pmatrix}2 \\ 3\end{pmatrix}$

[2b.] $\begin{pmatrix}1 &3 \\0 &0 \end{pmatrix} \begin{pmatrix} x \\ y \end{pmatrix} =\begin{pmatrix}4 \\ 1 \end{pmatrix}$, no solutions

[2bi.] $\begin{pmatrix}1 &3 \\0 &0 \end{pmatrix} \begin{pmatrix} x \\ y \end{pmatrix} =\begin{pmatrix}4 \\ 0\end{pmatrix} $, one solution for each number $y$: $\begin{pmatrix}4-3y \\ y\end{pmatrix} $

[2bii.] $\begin{pmatrix}0 &0 \\0 &0 \end{pmatrix} \begin{pmatrix} x \\ y \end{pmatrix} =\begin{pmatrix}0 \\ 0\end{pmatrix} $, one solution for each pair of numbers $x,y$:$\begin{pmatrix}x\\ y\end{pmatrix}$

Again, in the first case the linear operator is invertible while in the other cases it is not. When the operator is not invertible the solution set can be empty, a line in the plane or the plane itself.

For a system of equations with $r$ equations and $k$ variables, one can have a number of different outcomes. For example, consider the case of $r$ equations in three variables. Each of these equations is the equation of a plane in three-dimensional space. To find solutions to the system of equations, we look for the common intersection of the planes (if an intersection exists). Here we have five different possibilities:

[1.] $\textbf{Unique Solution.}$ The planes have a unique point of intersection.

[2a.] $\textbf{No solutions.}$ Some of the equations are contradictory, so no solutions exist.

[2bi.] $\textbf{Line.}$ The planes intersect in a common line; any point on that line then gives a solution to the system of equations.

[2bii.] $\textbf{Plane.}$ Perhaps you only had one equation to begin with, or else all of the equations coincide geometrically. In this case, you have a plane of solutions, with two free parameters.

[2biii.] $\textbf{All of \(\mathbb{R}^3$.}\) If you start with no information, then any point in $\mathbb{R}^3$ is a solution. There are three free parameters.

In general, for systems of equations with $k$ unknowns, there are $k+2$ possible outcomes, corresponding to the possible numbers (i.e. $0,1,2,\dots,k$) of free parameters in the solutions set plus the possibility of no solutions. These types of "solution sets'' are "hyperplanes'', generalizations of planes the behave like planes in $\mathbb{R}^3$ in many ways.

2.5.2: Particular Solution $+$ Homogeneous solutions

In the standard approach, variables corresponding to columns that do not contain a pivot (after going to reduced row echelon form) are $\textit{free}$. We called them non-pivot variables. They index elements of the solutions set by acting as coefficients of vectors.

Non-pivot columns determine terms of the solutions

\[\begin{pmatrix}
1 & 0 & 1 & -1 \\
0 & 1 & -1& 1 \\
0 &0 & 0 & 0 \\
\end{pmatrix}
\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix}
=
\begin{pmatrix}1\\1\\0\end{pmatrix}
\Leftrightarrow
\left\{
\begin{array}{lcr}
1x_1 +0x_2+ 1x_3 - 1x_4 & = 1 \\
0x_1 +1x_2 - 1x_3 + 1x_4 & = 1 \\
0x_1 +0x_2 + 0x_3 + 0x_4 & = 0
\end{array}
\right.
$$
Following the standard approach, express the pivot variables in terms of the non-pivot variables and add "freebee equations". Here $x_3$ and $x_4$ are non-pivot variables.
$$\begin{eqnarray*}
\left.
\begin{array}{rcl}
x_1 & = &1 -x_3+x_4 \\
x_2 & = &1 +x_3-x_4 \\
x_3 & = &\phantom{1+~\,}x_3\\
x_4 & =&\phantom{1+x_3+~}x_4
\end{array}
\right\}
\Leftrightarrow
\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix}
= \begin{pmatrix}1\\1\\0\\0\end{pmatrix} + x_3\begin{pmatrix}-1\\1\\1\\0\end{pmatrix} + x_4\begin{pmatrix}1\\-1\\0\\1\end{pmatrix}
\end{eqnarray*}$$
The preferred way to write a solution set is with set notation. $$S = \left\{\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} = \begin{pmatrix}1\\1\\ 0\\0\end{pmatrix} + \mu_1 \begin{pmatrix}-1\\1\\1\\0\end{pmatrix} + \mu_2 \begin{pmatrix}1\\-1\\ 0 \\1\end{pmatrix} : \mu_1,\mu_2\in {\mathbb R} \right\}$$
Notice that the first two components of the second two terms come from the non-pivot columns
Another way to write the solution set is
\[S= \left\{ X_0 + \mu_1 Y_1 + \mu_2 Y_2 : \mu_1,\mu_2 \in {\mathbb R} \right\} \]
where
\[X_0= \begin{pmatrix}1\\1\\0 \\0\end{pmatrix}, Y_1=\begin{pmatrix}-1\\1\\1\\0\end{pmatrix} , Y_2= \begin{pmatrix}1\\-1\\0 \\1\end{pmatrix}
\]

Here $X_0$ is called a particular solution while $Y_1$ and $Y_2$ are called homogeneous solutions.

2.5.3: Linearity and these parts

With the previous example in mind, lets say that the matrix equation $MX=V$ has solution set $\{ X_0 + \mu_1 Y_1 + \mu_2 Y_2):\mu_1,\mu_2 \in {\mathbb R} \}$. Recall that matrices are linear operators. Thus
$$M( X_0 + \mu_1 Y_1 + \mu_2 Y_2) = MX_0 + \mu_1MY_1 + \mu_2MY_2 =V$$
for $\textit{any}$ $\mu_1, \mu_2 \in \mathbb{R}$. Choosing $\mu_1=\mu_2=0$, we obtain
$$MX_0=V\, .$$
This is why $X_0$ is an example of a $\textit{particular solution}$.

Setting $\mu_1=1, \mu_2=0$, and using the particular solution $MX_0=V$, we obtain
$$MY_1=0\, .$$
Likewise, setting $\mu_1=0, \mu_2=1$, we obtain $$MY_2=0\, .$$
Here $Y_1$ and $Y_2$ are examples of what are called $\textit{homogeneous}$ solutions to the system. They $\textit {do not}$ solve the original equation $MX=V$, but instead its associated $\textit {homogeneous equation}$ $M Y =0$.

One of the fundamental lessons of linear algebra: the solution set to $Ax=b$ with $A$ a linear operator consists of a particular solution plus homogeneous solutions.

\[general ~solution = particular~ solution + homogeneous~ solutions.\]

Example 32

Consider the matrix equation of the previous example. It has solution set
\[S = \left\{\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} = \begin{pmatrix}1\\1\\0 \\0\end{pmatrix} + \mu_1 \begin{pmatrix}-1\\1\\1\\0\end{pmatrix} + \mu_2 \begin{pmatrix}1\\-1\\ 0\\1\end{pmatrix} \right\} \]
Then $MX_0=V$ says that $\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} =
\begin{pmatrix}1\\1\\0 \\ 0\end{pmatrix}$ solves the original matrix equation, which is certainly true, but this is not the only solution.

$MY_1=0$ says that $\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} = \begin{pmatrix}-1\\1\\1\\ 0\end{pmatrix}$ solves the homogeneous equation.

$MY_2=0$ says that $\begin{pmatrix}x_1\\x_2\\x_3\\x_4\end{pmatrix} =
\begin{pmatrix}1\\-1\\0 \\1\end{pmatrix}$ solves the homogeneous equation.

Notice how adding any multiple of a homogeneous solution to the particular solution yields another particular solution

Solution Set of Linear Equation System

Source: https://math.libretexts.org/Bookshelves/Linear_Algebra/Map%3A_Linear_Algebra_(Waldron_Cherney_and_Denton)/02%3A_Systems_of_Linear_Equations/2.05%3A_Solution_Sets_for_Systems_of_Linear_Equations

Woolcock Yousagannot

Solution Set of Linear Equation System

2.5: Solution Sets for Systems of Linear Equations

2.5.1: The Geometry of Solution Sets: Hyperplanes

2.5.2: Particular Solution \(+\) Homogeneous solutions

2.5.3: Linearity and these parts

Menu Halaman Statis