Solve Lagrange Multipliers for x,y,z in Min Distance Problem

In summary, the points (1,0,0), (-1,0,0) and (0,0,0) are all solutions to the equation x^2-z^2 = 1 when λ=-1.
  • #1
kliker
104
0

Homework Statement



find the points on the surface x^2-z^2 = 1 which are in minimum distance from (0,0)

i should find the points using d = x^2+y^2+z^2

first of all

gradf = λ gradg

where f = d and g = x^2-z^2

so we have (2x,2y,2z) = λ (2x,0,2z)

now
2x = λ2x
2y = 0 => y = 0
2z = λ2z

so λ=1

but now how can i find x and z?

im totally confused
 
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  • #2
i would check your grad g

also i think you mean closest to (0,0,0)?
 
  • #3
kliker said:

Homework Statement



find the points on the surface x^2-z^2 = 1 which are in minimum distance from (0,0)

i should find the points using d = x^2+y^2+z^2

first of all

gradf = λ gradg

where f = d and g = x^2-z^2

so we have (2x,2y,2z) = λ (2x,0,2z)
This s wrong. You have dropped a sign.

now
2x = λ2x
2y = 0 => y = 0
2z = λ2z

so λ=1

but now how can i find x and z?

im totally confused
 
  • #4
oh you're right so now i have

2x = λ2x
2y = 0 => y = 0
2z = -λ2z => λ*2z+2z = 0 => 2z(1+λ)=0

hence we have λ = -1 and z = 0

but if z is 0 then x = +1 or x = -1

so for λ = -1 we have two points (1,0,0) and (-1,0,0)

is this correct? also, i would like to ask something, if these points are correct then why for λ = -1 in the first equation i get -4x = 0 hence x = 0?

but when x = 0 the point will be (0,0,0), can we check for this point too?

thanks for your help
 
  • #5
(0,0,0) is not in your surface
 
  • #6
its also worth drawing the surface to help understand what is going on

try drawing the curve given by y=0, x^2-z^2 = 1 in the xz plane...
 
  • #7
Just curious, shouldn't d = sqrt(x^2+y^2+z^2)
 
  • #8
yeah, but minimising x^2 will minimise |x| so its ok - but i suppose you should show it
 
Last edited:
  • #9
the problem states by itself that it shouldn't be sqrt(x^2+y^2+z^2) I think i understand the solution now,

thanks for your help :)
 

1. What is the Lagrange multiplier method?

The Lagrange multiplier method is a mathematical optimization technique used to find the maximum or minimum value of a function subject to constraints. It involves finding the critical points of the function using a system of equations known as the Lagrange equations.

2. How does the Lagrange multiplier method work?

The Lagrange multiplier method works by finding the critical points of the objective function, which is the function that needs to be optimized. These critical points are found by solving a system of equations known as the Lagrange equations, which include the objective function, the constraint function, and the Lagrange multiplier. The Lagrange multiplier is used to ensure that the constraint is satisfied at the critical point.

3. What is the Min Distance problem?

The Min Distance problem is a type of optimization problem where the objective is to find the minimum distance between a fixed point (such as the origin) and a moving point, subject to certain constraints. This problem can be solved using the Lagrange multiplier method by setting the constraint function to be the distance formula and the objective function to be the distance between the two points.

4. How do you solve Lagrange Multipliers for x, y, z in the Min Distance problem?

To solve Lagrange Multipliers for x, y, z in the Min Distance problem, you need to first define the objective function and the constraint function in terms of x, y, z. Then, you can use the Lagrange multiplier method to find the critical points of the objective function. These critical points will give you the values of x, y, z that minimize the distance between the two points.

5. What are some real-life applications of the Lagrange multiplier method?

The Lagrange multiplier method has many real-life applications in fields such as economics, engineering, and physics. It can be used to optimize production processes, minimize costs, and find the optimal design of structures. It is also used in physics to find the path of least resistance or minimum energy, and in game theory to find the optimal strategies for players. Additionally, the Lagrange multiplier method is commonly used in machine learning algorithms to optimize model parameters.

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