Minimizing the distance between two points and a line

In summary, the problem is to find the point P on the given line l such that the difference between the distances from P to two given points A and B is maximum. If the given line l and the line through A and B are not parallel, then P is the intersection of l and the line through A and B. However, if l and the line through A and B are parallel, there is no maximum since the distance can be made larger by moving P along the line. It is suggested to solve the problem analytically by maximizing the squared distance instead of the absolute value, using standard methods. It is also mentioned that trigonometric functions may be involved in finding the solution.
  • #1
Mr Davis 97
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Homework Statement


Let ##A## and ##B## be two given points, ##l## a given line. Find the point ##P## on ##l## such that ##|AP-PB|## is a maximum.

Homework Equations

The Attempt at a Solution


Suppose that the line ##l## and the line that goes through ##A## and ##B## (call it ##\ell_{AB}##) are not parallel. Then our desired point ##P## is the the intersection of ##l## and ##\ell_{AB}##. Is this correct? I'm pretty sure it is.

But what about when ##l## and ##\ell_{AB}## are parallel? There doesn't seem to be a maximum, since if you move a point ##P## along any direction of the line we'll just get greater and greater values for ##|AP-PB|##
 
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  • #2
I think your intuition is correct, in which case you'd just note that in the parallel case ##|AP - PB|## is unbounded above.

But you should solve this analytically, maximizing by the usual methods. One hint: When working with distances, it will usually make your life a heck of a lot easier to work with the squared distance. rather than the absolute value. It's differentiable everywhere (no corner points) and the form of the derivatives is a lot simpler.

So set up the expression for ##|AP - PB|^2## and apply the standard methods.

Edit: I misread this problem. My general comments about using distance squared and avoiding square roots still stands, but at first glance I'm not sure if it's applicable to this problem. The general idea is to get a nicer function which is minimized / maximized when your original function is minimized / maximized, but simply squaring isn't going to do it here.

More Edit: My hunch is that the "nicer function" would be something involving trig functions, perhaps invoking the law of cosines in some way. Indeed, it may be the triangles involved that led you to your intuition on the nature of the solution.
 
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1. What is the purpose of minimizing the distance between two points and a line?

The purpose of minimizing the distance between two points and a line is to find the shortest distance between the two points and the line. This can be useful in various fields such as engineering, physics, and computer graphics.

2. How is the distance between two points and a line calculated?

The distance between two points and a line is calculated using the formula d = |ax + by + c| / √(a^2 + b^2), where (x,y) are the coordinates of the point, and a, b, and c are the coefficients of the line's equation (ax + by + c = 0).

3. What does it mean to minimize the distance between two points and a line?

Minimizing the distance between two points and a line means finding the smallest possible distance between the two points and the line. This is achieved by finding the point on the line that is closest to the given points.

4. Can the distance between two points and a line be negative?

No, the distance between two points and a line cannot be negative. It is always a positive value, representing the shortest distance between the two points and the line.

5. Are there any practical applications of minimizing the distance between two points and a line?

Yes, there are many practical applications of minimizing the distance between two points and a line. Some examples include finding the shortest distance between a point and a road or railway track, determining the closest point of approach in navigation, and optimizing the placement of objects in a 3D space.

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