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lecture_3_slides

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↓ Slide 1

Another look at the last example

  • $\begin{array}{ccccccrrr} x&+&3y&+&z&=&5&\quad&(1)\\ 2x&+&7y&+&4z&=&17&&(2)\end{array}$
  • Find solutions of this system by applying operations
  • Aim to end up with a very simple sort of system where we can see the solutions easily.
  • $\begin{array}{ccccccrrr} x&+&3y&+&z&=&5&\quad&(1)\\ 2x&+&7y&+&4z&=&17&&(2)\end{array}$
  • Replace equation (2) with $(2)-2\times (1)$:
  • $\begin{array}{ccccccrrr} x&+&3y&+&z&=&5&\quad&(1)\\ &&y&+&2z&=&7&&(2)\end{array}$
  • Now replace equation (1) with $(1)-3\times (2)$
  • $\begin{array}{ccccccrrr} x&&&-&5z&=&-16&\quad&(1)\\ &&y&+&2z&=&7&&(2)\end{array}$
  • $\begin{array}{ccccccrrr} x&&&-&5z&=&-16&\quad&(1)\\ &&y&+&2z&=&7&&(2)\end{array}$
  • Can now easily rearrange (1) to find $x$ in terms of $z$, and we rearrange (2) to find $y$ in terms of $z$.
  • Since $z$ can take any value, write $z=t$ where $t$ is a “free parameter”
  • (which means $t$ can be any real number, or $t\in \mathbb{R}$).
  • Solution: \begin{align*} x&=-16+5t\\ y&=7-2t\\ z&=t,\qquad t\in \mathbb{R}\end{align*}
  • Solution: \begin{align*} x&=-16+5t\\ y&=7-2t\\ z&=t,\qquad t\in \mathbb{R}\end{align*}
  • Can also write this in “vector form”:
  • $\begin{bmatrix} x\\y\\z\end{bmatrix}=\begin{bmatrix} -16\\7\\0\end{bmatrix}+t\begin{bmatrix} 5\\-2\\1\end{bmatrix},\qquad t\in \mathbb{R}.$
  • This is the equation of the line where the two planes described by the original equations intersect.
  • $\begin{bmatrix} x\\y\\z\end{bmatrix}=\begin{bmatrix} -16\\7\\0\end{bmatrix}+t\begin{bmatrix} 5\\-2\\1\end{bmatrix},\qquad t\in \mathbb{R}$
  • For each value of $t$, we get a different solution (a different point on the line of intersection).
  • e.g. take $t=0$ to see that $(-16,7,0)$ is a solution
  • take $t=1.5$ to see that $(-16+1.5\times 5,7+1.5\times (-2),1.5) = (-8.5,4,1.5)$ is another solution
  • etc.
  • This works for any value $t\in\mathbb{R}$, and every solution may be written in this way
↓ Slide 2

Observations

  1. The operations we applied to the original linear system don't change the set of solutions. This is because each operation is reversible.
  2. Writing out the variables $x,y,z$ each time is unnecessary. If we erase the variables from the system $$\begin{array}{ccccccrrr} x&+&3y&+&z&=&5&\quad&(1)\\ 2x&+&7y&+&4z&=&17&&(2)\end{array}$$ and write all the numbers in a grid, or a matrix, we get $\begin{bmatrix} 1&3&1&5\\2&7&4&17\end{bmatrix}$
  • System of linear equations: $\begin{array}{ccccccrrr} x&+&3y&+&z&=&5&\quad&(1)\\ 2x&+&7y&+&4z&=&17&&(2)\end{array}$
  • $\begin{bmatrix} 1&3&1&5\\2&7&4&17\end{bmatrix}$ is called the augmented matrix of this linear system
  • Each row corresponds to one equation.
  • The first column corresponds to the $x$ variable, the second to $y$, the third to $z$
  • Numbers in the final column are the right hand sides of the equations.
  • Instead of performing operations on equations, we can perform operations on the rows of this matrix.

\begin{align*} \begin{bmatrix} 1&3&1&5\\2&7&4&17\end{bmatrix} &\xrightarrow{R2\to R2-2\times R1} \begin{bmatrix} 1&3&1&5\\0&1&2&7\end{bmatrix} \\[6pt]&\xrightarrow{R1\to R1-3\times R1} \begin{bmatrix} 1&0&-5&-16\\0&1&2&7\end{bmatrix} \end{align*}

  • Translate back into equations and solve:
  • $\begin{array}{ccccccrrr} x&&&-&5z&=&-16&\quad&(1)\\ &&y&+&2z&=&7&&(2)\end{array}$
  • $\begin{bmatrix} x\\y\\z\end{bmatrix}=\begin{bmatrix} -16\\7\\0\end{bmatrix}+t\begin{bmatrix} 5\\-2\\1\end{bmatrix},\qquad t\in \mathbb{R}.$

This method always works:

  • take any system of linear equations
  • write down a corresponding matrix (the augmented matrix)
  • perform reversible operations on the rows of this matrix to get a “nicer” matrix
  • write down a new system of linear equations with the same solutions as the original system.
  • Hopefully the new system will be easy to solve…
  • and the solutions haven't changed, so we'll have solved the original system!
→ Slide 3

The augmented matrix and elementary operations

↓ Slide 4

Definition

Given a system of linear equations: \begin{align*} a_{11}x_1+a_{12}x_2+\dots+a_{1m}x_m&=b_1\\ a_{21}x_1+a_{22}x_2+\dots+a_{2m}x_m&=b_2\\ \hphantom{a_{11}}\vdots \hphantom{x_1+a_{22}}\vdots\hphantom{x_2+\dots+{}a_{nn}} \vdots\ & \hphantom{{}={}\!} \vdots\\ a_{n1}x_1+a_{n2}x_2+\dots+a_{nm}x_m&=b_n \end{align*} its augmented matrix is \[ \begin{bmatrix} a_{11}&a_{12}&\dots &a_{1m}&b_1\\ a_{21}&a_{22}&\dots &a_{2m}&b_2\\ \vdots&\vdots& &\vdots&\vdots\\ a_{n1}&a_{n2}&\dots &a_{nm}&b_n \end{bmatrix}.\]

The numbers in this matrix are called its entries.

↓ Slide 5

Example

  • Find the augmented matrix of the linear system\begin{align*}3x+4y+7z&=2\\x+3z&=0\\y-2z&=5\end{align*}
  • We can rewrite it as \begin{align*}3x+4y+7z&=2\\{\color{red}1}x+{\color{red}0y}+3z&=0\\{\color{red}0x}+{\color{red}1}y-2z&=5\end{align*}
  • So the augmented matrix is\[ \begin{bmatrix} 3&4&7&2\\1&0&3&0\\0&1&-2&5\end{bmatrix}.\]
↓ Slide 6

Elementary operations on a system of linear equations

If we perform one of the following operations on a system of linear equations:

  1. list the equations in a different order; or
  2. multiply one of the equations by a non-zero real number; or
  3. replace equation $j$ by “equation $j$ ${}+{}$ $c\times {}$ (equation $i$)”, where $c$ is a non-zero real number and $i\ne j$,

then the new system will have exactly the same solutions as the original system. These are called elementary operations on the linear system.

↓ Slide 7

Why do elementary operations leave the solutions of systems unchanged?

  • We do the same thing to the left hand side and the right hand side of each equation…
    • so any solution to the original system will also be a solution to the new system.
  • These operations are all reversible (using operations of the same type)…
    • so any solution to the new system will also be a solution to the original system.
↓ Slide 8

Elementary row operations on a matrix

Translate elementary operations on the linear system into operations on the rows of the augmented matrix:

  1. change the order of the rows of the matrix;
  2. multiply one of the rows of the matrix by a non-zero real number;
  3. replace row $j$ by “row $j$ ${}+{}$ $c\times {}$ (row $i$)”, where $c$ is a non-zero real number and $i\ne j$.
  • These operations are called elementary row operations or EROs on the matrix.
  • The systems of linear equations corresponding to these matrices have exactly the same solutions.
lecture_3_slides.1454347182.txt.gz · Last modified: by rupert

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