Minimizing Distance to the Origin

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In summary, the conversation discusses finding the point(s) on a surface z^2 - 10xy = 10 that is closest to the origin by minimizing distance. The person uses Lagrange multipliers to solve for the variables x, y, and z and tests different values for λ. The correct solutions are derived to be (±1, ±1, 0) and (0, 0, ±√10), with the former giving a distance of √2 and the latter giving a distance of √10.
  • #1
TranscendArcu
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Homework Statement


Find the point(s) on the surface z2 - 10xy = 10 nearest to the origin.

The Attempt at a Solution


I will minimize distance by minimizing distance2. So,

D = x2 + y2 + z2, where z2 - 10xy = 10. By Lagrange multipliers I have,

2x = -10yλ
2y = -10xλ
2z = 2zλ.

By the last equation, clearly either z = 0 or λ = 1 or λ = 0.

I will test λ = 1 first with the condition y ≠ 0 and x ≠ 0. By dividing the first equation by the second equation I have,

x/y = y/x, which implies x = y. But clearly 2x ≠ -10x when x ≠ 0. So I reject λ = 1 with the condition y ≠ 0 and x ≠ 0. I will now test λ = 1 first with the condition y = 0. I have,

2(0) = 10 * x * 1, so clearly x = 0. (Similarly, I could have tested with the condition x = 0 and found y = 0.) By the constraint equation I have z2 - 10(0)(0) = 10, so clearly z = ±√10.

Suppose z = 0. Then x ≠ 0 and y ≠ 0 because, in our constraint equation, -10xy = 10. So clearly xy = -1. I will now divide 2x = -10yλ by 2y = -10xλ and again find x = y. This implies,

x2 = -1 or y2 = -1.

x = i or y = i. I reject these solutions as crazy.

Suppose λ = 0. Then it must follow that y = 0 and x = 0. Again, plugging into our constraint equation gives z = ±√10. Therefore, Lagrange multipliers give the points (0,0,±√10), both of which give distance √10.

However, by inspection, one can see that the points (1,-1,0) and (-1,1,0) also satisfy the constraint and give distance √2.

Ideas where I went wrong?
 
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  • #2
First, λ = 0 is not a solution from your 3rd equation. (Check it.)

Second, your conclusion x=y is not quite right.
What you have is x^2=y^2, which has 2 solutions: x=y or x=-y.
 
  • #3
Okay. You're right about λ = 0. My bad.

Let's see if I can actually derive the correct (I presume, unless there is another point closer to the origin that satisfies the restraint) solutions.

So, from x2 = y2 I determine that x = ± y.

02 -10(±y)(y) = 10.
(-/+)y2 = 1
(-/+)y = ± 1
y = (-/+) 1

x = -y, so when y = -1, x = 1. When y = 1, x = -1. Therefore, I also have solutions (±1,(-/+)1,0), both of which give distance √2. Look about right?

Why, however, do (±1,(-/+)1,0) fail to satisfy 2x = -10y*λ and 2y = -10x*λ, when λ = 1?
 
  • #4
When λ=1, you do not have x,y=±1.

What you do have is x=±y.
And from 2x = -10yλ, it follows (with λ=1) that x=y=0.Btw, your solutions (±1,(-/+)1,0), both of which give distance √2, look right.
 

Related to Minimizing Distance to the Origin

1. What is "Minimizing Distance to the Origin"?

"Minimizing Distance to the Origin" is a mathematical concept that involves finding the shortest distance between a given point and the origin (0,0) on a coordinate plane.

2. Why is minimizing distance to the origin important?

Minimizing distance to the origin is important in various fields such as physics, engineering, and statistics. It can help in finding the most efficient route or path for a given object, optimizing the placement of objects, and determining the correlation between two variables.

3. How is distance to the origin calculated?

Distance to the origin is calculated using the Pythagorean theorem, where the distance is equal to the square root of the sum of the squares of the coordinates (x and y) of a point. This can also be represented as d = √(x^2 + y^2).

4. What are some methods for minimizing distance to the origin?

One method is to use the gradient descent algorithm, which involves iteratively moving towards the direction of steepest descent until the minimum distance is reached. Another method is to use calculus and optimization techniques to find the minimum distance analytically.

5. Are there any real-life applications of minimizing distance to the origin?

Yes, there are many real-life applications such as finding the shortest route for a delivery truck, optimizing the placement of cell phone towers for better coverage, and determining the correlation between two variables in a scientific study.

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