Finding the minimum and maximum distances using Lagrange Multipliers

In summary, the conversation discusses the concept of maximizing distances squared (d^2) and how it relates to the differentiation of distance (d). While d^2 and d do not have the same values, they can be maximized or minimized at the same points. The conversation also touches on the relationship between the squared problem and the original problem, and how it can lead to different solutions depending on the values of the original function.
  • #1
theBEAST
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Homework Statement


attachment.php?attachmentid=47774&stc=1&d=1338346735.png


What I don't understand is why you can maximize the distances squared - d2. Isn't d2 different from d? I don't see how they can get you the same value.
 

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  • #2
theBEAST said:

Homework Statement


attachment.php?attachmentid=47774&stc=1&d=1338346735.png


What I don't understand is why you can maximize the distances squared - d2. Isn't d2 different from d? I don't see how they can get you the same value.

They don't have the same values (unless they happen to be 0 or 1), but they are maximized or minimized at the same points (x,y,z). Think about it: how could it be otherwise?

RGV
 
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  • #3
Letting distance be "D", so we can distinguish it from the differential, "d", [itex]d(D^2)/dt= 2D (dD/dt)= 0[/itex]. If D itself is not 0, [itex]d(D^2)/dt[/itex] will be 0 if and only if [itex]dD/dt[/itex] is 0.
 
  • #4
Ray Vickson said:
They don't have the same values (unless they happen to be 0 or 1), but they are maximized or minimized at the same points (x,y,z). Think about it: how could it be otherwise?

RGV

As HallsofIvy has pointed out, the stationary points are the same in both problems. However, if the original f can take both negative and positive values, a min in the squared problem can be a saddle point in the original problem (eg: f(x) = x^3 has a saddle point at x = 0 but g(x) = f(x)^2 = x^6 has a global minimum at x = 0) and a min in the original problem can be a max in the squared problem, etc. None of these issues arise if the original f is >= 0, as it is in the distance problem you cite.

RGV
 

What is the purpose of using Lagrange Multipliers in finding minimum and maximum distances?

The use of Lagrange Multipliers is a mathematical tool that allows us to find the minimum and maximum values of a function subject to certain constraints. In the context of finding distances, it allows us to find the shortest and longest distances between two objects while taking into account any constraints, such as a limited range of motion or a specific path that must be followed.

How do Lagrange Multipliers work in finding minimum and maximum distances?

Lagrange Multipliers work by converting a constrained optimization problem into an unconstrained one. This is done by introducing a new variable, known as the Lagrange Multiplier, and setting up a system of equations that can be solved using traditional optimization techniques. The solution to this system gives us the minimum and maximum distances that satisfy the given constraints.

What are the advantages of using Lagrange Multipliers in finding minimum and maximum distances?

One major advantage of using Lagrange Multipliers is that it simplifies the optimization process by converting a constrained problem into an unconstrained one. This allows us to use traditional optimization methods, such as setting derivatives equal to zero, to find the solutions. Additionally, Lagrange Multipliers can handle a wide range of constraints, making it a versatile tool for finding minimum and maximum distances.

What are some common applications of using Lagrange Multipliers in finding minimum and maximum distances?

Lagrange Multipliers have many practical applications, including in physics, engineering, economics, and statistics. In physics, they can be used to find the shortest and longest distances between celestial bodies or to optimize the paths of moving objects. In economics, they can be used to maximize profits while taking into account budget constraints. In statistics, they can be used to find the maximum likelihood estimate of a parameter subject to certain constraints. Overall, Lagrange Multipliers are a useful tool for solving optimization problems in a variety of fields.

Are there any limitations or drawbacks to using Lagrange Multipliers in finding minimum and maximum distances?

While Lagrange Multipliers are a powerful tool, they do have some limitations. One limitation is that they may not always give the global minimum or maximum value, but rather a local one. Additionally, the process of setting up and solving the system of equations can be complex and time-consuming. In some cases, there may be alternative methods that are more efficient. However, for problems with multiple constraints, Lagrange Multipliers are often the most effective approach.

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