How Do Lagrange Multipliers Extend Beyond Two Variables?

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In summary, Lagrange multipliers are a method used to find the extrema of a function with multiple independent variables and one dependent variable. This method involves finding the scalar multiplier (usually denoted as λ) that satisfies the equation grad(x)=λ*grad(c), where grad(x) and grad(c) are perpendicular to the unit vector v and C is held constant. This method is applicable to n-dimensional Euclidean space, but the notation for the directional derivative may vary.
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okkvlt
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How does lagrange multipliers work?
i was able to work out this proof of the idea, but its only true for a function with two independent variables and one dependent variable.

Rn=the space that is the independent variables.

x[Rn]=x
C[Rn]=C=constant.


dx/d[Rn]=grad(x)*v; v is a unit vector
dC/d[Rn]=grad(c)*v

because C is held constant, dC/d[Rn]=0 everywhere.
because cos(pi/2)=0, **grad(C) is perpendicular to v.**

In order for extrema to exist, dx/d[Rn]=0. grad(x)*v is zero meaning **grad(x) is perpendicular to v.**

in the case Rn=R2:
both grad(x) and grad(c) are perpendiular to v, it means grad(x) must be parallel to grad(c).

That is the requirement given by the system
grad(x)=L*grad(c)
C[Rn]=C
where L is the scalar multiplier (upside down y).

but it seems as though this is only true for the R2 case. in 3 dimnensions, if both grad(x) and grad(c) are perpendicular to v, it doesn't necessarily mean grad(x) is parallel to grad(c). It seems like I am missing something.


How do i extend this to more than two independent variables?
 
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  • #2
What do you mean by "dx/d[Rn]" where Rn is n dimensional Euclidean space? I don't believe that is standard notation.
 
  • #3
i know it isnt
[Rn]=x,y,z, etc
basically i meant the directional derivative by dx/d[Rn]"
 

FAQ: How Do Lagrange Multipliers Extend Beyond Two Variables?

What are Lagrangian multipliers?

Lagrangian multipliers are a mathematical tool used to optimize a function subject to a set of constraints. They allow us to find the maximum or minimum value of a function while satisfying the constraints.

How do Lagrangian multipliers work?

Lagrangian multipliers work by introducing a new variable, called a multiplier, to the original function. This creates a new function, called the Lagrangian, which can be solved using techniques from calculus to find the optimal solution.

When are Lagrangian multipliers used?

Lagrangian multipliers are commonly used in optimization problems in various fields such as economics, physics, and engineering. They are also used in machine learning to find the optimal values for model parameters.

What are the benefits of using Lagrangian multipliers?

One of the main benefits of using Lagrangian multipliers is that they allow us to solve constrained optimization problems without having to explicitly include the constraints in the original function. This can make the problem easier to solve and the solution more intuitive.

What are some limitations of Lagrangian multipliers?

One limitation of Lagrangian multipliers is that they may not always provide a unique solution to a constrained optimization problem. In addition, the Lagrangian can become more complex with multiple constraints, making it more difficult to solve. Furthermore, they may not be applicable to all types of constraints, such as inequality constraints with a strict inequality.

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