Solving a system in five unknowns for lagrange multiplier

In summary, the conversation discusses finding the extrema of a given function with two constraints. The attempt at a solution involves creating a new function and taking partial derivatives, but there are no local extrema found. The possibility of extrema on the boundary is also considered, but it is determined that there are no extrema there either.
  • #1
jonroberts74
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0

Homework Statement



I have to find the extrema of a given function with two constraints

[tex]f(x,y,z) = x+y+z;x^2-y^2=1;2x+z=1[/tex]





The Attempt at a Solution


If I create a new function F

then I have

[tex]F(x,y,z,\lambda,\mu)=x+y+z-(x^2\lambda - y^2\lambda -\lambda) -(2x\mu + z\mu - \mu) [/tex]

and taking the partials

[tex]\left\{\begin{array}{cc} F_{x} = 1-2x\lambda - 2\mu =0\\ F_{y} = 1+2y\lambda = 0 \\F_{z} = 1 - \mu = 0 \\ F_{\lambda} = -x^2 + y^2 + 1 = 0 \\ F_{\mu} = -2x - z +1 = 0 \end{array}\right. [/tex]

so now,

[tex]\mu =1[/tex]
solving for lambda

[tex]\lambda = -\frac{1}{2y} = \frac{1-2}{2x}[/tex]

now solving for x [or y] [tex]x=y[/tex]

but this causes an issue with [tex]-x^2+y^2+1=0[/tex] because 1 does not equal zero
 
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  • #2
There are no local extrema then.

ehild
 
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  • #3
okay, that's what I had figured. Making sure Idid not miss something
 
  • #4
But can be extrema on the boundary.

ehild
 
  • #5
ehild said:
But can be extrema on the boundary.

ehild

I can't find the zeros for the constraints though so what would I test in the function to see if there is.
 
  • #6
ehild said:
But can be extrema on the boundary.

ehild

No. The fact that the Lagrangian equations plus the constraints have no solution precludes any extrema on the boundary.

This can also be verified directly: the two constraints can be used to find x and z in terms of y, then the results can be substituted into f(x,y,z); there are two solutions for x and z in terms of y, hence two versions of f. The two resulting problems are each one-dimensional problems, which can be examined graphically to check for the absence of local extrema. In other words, the "max" is +∞ and the "min" is -∞, but without any finite local constrained extrema at all.
 
Last edited:

Related to Solving a system in five unknowns for lagrange multiplier

1. What is a system in five unknowns?

A system in five unknowns refers to a set of five equations with five variables, where the goal is to find values for each variable that satisfy all of the equations simultaneously. This type of system is commonly used in mathematics and engineering to model complex relationships.

2. What is the Lagrange multiplier method?

The Lagrange multiplier method is a mathematical technique used to solve systems of equations with constraints. It involves introducing a new variable, known as the Lagrange multiplier, to incorporate the constraints into the equations and find the optimal solution.

3. When is the Lagrange multiplier method used?

The Lagrange multiplier method is used when solving optimization problems with multiple variables and constraints. It is commonly used in economics, physics, and engineering to find the maximum or minimum values of a function subject to certain conditions.

4. What are the steps for solving a system in five unknowns using the Lagrange multiplier method?

The steps for solving a system in five unknowns using the Lagrange multiplier method are as follows:

  • Set up the system of equations with five unknowns and the constraints.
  • Introduce the Lagrange multiplier and create a new function by adding the constraint equations multiplied by the Lagrange multiplier.
  • Take the partial derivatives of the new function with respect to each variable and set them equal to zero.
  • Solve the resulting equations to find the values of the five unknowns.
  • Check if the solution satisfies all of the constraints.

5. What are the advantages of using the Lagrange multiplier method?

The Lagrange multiplier method offers several advantages, including:

  • It can be used to solve complex systems with multiple variables and constraints.
  • It provides a systematic approach for finding the optimal solution.
  • It can be applied to both linear and nonlinear systems.
  • It can handle both equality and inequality constraints.

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