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lecture_24
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| lecture_24 [2015/04/23 11:42] – rupert | lecture_24 [2016/01/22 19:06] (current) – rupert | ||
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| + | ~~REVEAL~~ | ||
| ===== The distance from a point to a line ===== | ===== The distance from a point to a line ===== | ||
| Line 39: | Line 40: | ||
| so | so | ||
| \[ \dist(B, | \[ \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 $L_2$. | ||
| + | |||
| + | |||
| + | To summarise: for skew lines $L_1$ and $L_2$ with direction vectors $\vv_1$ and $\vv_2$, we have | ||
| + | \[ \dist(L_1, | ||
| + | where $\nn=\vv_1\times\vv_2$ and $A$ and $B$ are points with one in $L_1$ and the other in $L_2$. | ||
| + | |||
| + | === 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_24.1429789374.txt.gz · Last modified: by rupert
