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lecture_22_slides
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| lecture_22_slides [2016/04/20 12:18] – [Alternative formula] rupert | lecture_22_slides [2017/04/18 09:33] (current) – rupert | ||
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| ~~REVEAL~~ | ~~REVEAL~~ | ||
| - | ==== Last time ==== | + | ====== |
| + | |||
| + | ===== Distance from $A$ to $\Pi$ ===== | ||
| + | |||
| + | * $\def\dist{\text{dist}}\def\cp# | ||
| + | * $\nn$ is direction of shortest path from $A$ to $\Pi$ | ||
| + | * Let $B$ be any point in the plane $\Pi$.{{ : | ||
| + | |||
| + | * (shortest) distance from $A$ to $\Pi$ is $\text{dist}(A, | ||
| + | * where $\pp=\text{proj}_{\nn}{\vec{AB}}$. | ||
| + | * Do some algebra: we get $\text{dist}(A, | ||
| + | |||
| + | ==== Example ==== | ||
| + | |||
| + | Find the distance from $A=(1, | ||
| + | * choose any point $B$ in $\Pi$, e.g. $B=(2, | ||
| + | * $\nn=\c2{-3}6$ and $\vec{AB}=\c15{-3}$ | ||
| + | * So $\def\dist{\text{dist}}\dist(A, | ||
| - | * Take a plane $\Pi$ with normal vector $\def\dist{\text{dist}}\def\cp# | ||
| - | * Take a point $A$ | ||
| - | * $\dist(A, | ||
| ==== The distance from the origin to a plane ==== | ==== The distance from the origin to a plane ==== | ||
| Line 108: | Line 122: | ||
| * This works in $\bR^n$ for any $n$ | * This works in $\bR^n$ for any $n$ | ||
| - | ==== Example | + | ==== Example, |
| - | + | ||
| - | Let's redo the previous example using this method. | + | |
| - | + | ||
| - | We have $\vec{AB}=\c024$ and $\vv=\c41{-5}$, so | + | |
| - | \[\pp=\proj_{\vv}\vec{AB} | + | |
| - | so | + | |
| - | \[ \nn=\vec{AB}-\pp=\c024-(-\frac37)\c41{-5}=\c024+\frac37\c41{-5}=\frac17\c{12}{17}{13}\] | + | |
| - | so | + | |
| - | \[ \dist(B, | + | |
| - | + | ||
| - | ===== The distance between skew lines in $\mathbb{R}^3$ ===== | + | |
| - | + | ||
| - | Suppose that $L_1$ and $L_2$ are skew lines in $\rt$: lines which are not parallel and do not cross. | + | |
| - | + | ||
| - | Let $\vv_1$ be a direction vector along $L_1$, and let $\vv_2$ be a direction vector along $L_2$. | + | |
| - | + | ||
| - | {{ : | + | |
| - | + | ||
| - | The shortest distance from $L_1$ and $L_2$ is measured along the direction orthogonal to both $\vv_1$ and $\vv_2$, namely the direction of $\nn=\vv_1\times\vv_2$. | + | |
| - | + | ||
| - | Let $\Pi$ be the plane with normal vector $\nn$ which contains $L_1$. | + | |
| - | + | ||
| - | {{ : | + | |
| - | + | ||
| - | For any point $B$ in $L_2$, we have | + | |
| - | \[\dist(L_1, | + | |
| - | where $A$ is any point in $\Pi$; for example, we can take $A$ to be any point in $L_2$. | + | |
| + | Find the $\dist(B, | ||
| - | To summarise: for skew lines $L_1$ and $L_2$ with direction vectors | + | * $\vec{AB}=\c024$ and $\vv=\c41{-5}$ |
| - | \[ \dist(L_1, | + | * $\pp=\proj_{\vv}\vec{AB} |
| - | where $\nn=\vv_1\times\vv_2$ and $A$ and $B$ are points with one in $L_1$ and the other in $L_2$. | + | |
| + | * $\dist(B, | ||
| - | === Example === | ||
| - | Consider the skew lines | ||
| - | \[ L_1:\c xyz=\c 101+t_1\c123, | ||
| - | and | ||
| - | \[ L_2:\c xyz=\c 321+t_2\c1{-1}1, | ||
| - | The direction vectors are $\c123$ and $\c1{-1}1$, so we take $\nn$ to be their cross product: | ||
| - | \[ \nn=\c123\times\c1{-1}1=\begin{vmatrix}\vec\imath& | ||
| - | and if $A=(1,0,1)$ and $B=(3,2,1)$ then $A$ and $B$ are points with one in $L_1$ and the other in $L_2$, and $\vec{AB}=\c 220$. Hence | ||
| - | \[\dist(L_1, | ||
lecture_22_slides.1461154706.txt.gz · Last modified: by rupert
