Can Lagrange Multipliers solve optimization problems with multiple constraints?

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Discussion Overview

The discussion focuses on the application of Lagrange Multipliers to solve optimization problems involving multiple constraints, specifically when the constraints are inequalities rather than equalities. Participants explore the challenges of formulating these problems and the methods to handle multiple constraints effectively.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions whether Lagrange Multipliers can be applied to optimization problems with inequality constraints and seeks advice on transforming these constraints into a suitable form.
  • Another participant outlines the general approach for using Lagrange Multipliers with multiple constraints, providing a mathematical framework but acknowledges the complexity involved in solving such problems.
  • A participant clarifies that their specific problem involves maximizing a function subject to both upper and lower bounds on constraints, expressing confusion about how to proceed with the inequalities.
  • There is a suggestion to check values around the boundaries in addition to critical points when evaluating the function, emphasizing the importance of boundary conditions in optimization.
  • One participant mentions the concept of slack variables as a potential tool for dealing with inequality constraints, suggesting further research on the topic.
  • A link to a resource is shared that discusses Lagrange Multipliers with inequality constraints, which may provide additional insights.
  • Another participant expresses uncertainty about managing both upper and lower bounds in their constraints, indicating a need for clarification on this aspect.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the application of Lagrange Multipliers to problems with inequality constraints, and multiple competing views remain regarding the best approach to handle such scenarios.

Contextual Notes

Participants highlight limitations in their understanding of how to apply Lagrange Multipliers in the context of inequality constraints, particularly when both upper and lower bounds are present. There is also mention of the need to evaluate boundary conditions, which remains an unresolved aspect of the discussion.

lynxman72
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Hi all, I was wondering how to go about solving an optimization problem for a function f(x,y,z) where the two constraint equations are given by:

a is less than or equal to g(x,y,z) is less than or equal to b
(a and b are two distinct numbers)
h(x,y,z) is less than or equal to c
(c is another number distinct from a and b)

Can Lagrange Multipliers solve this problem? In other words, is there some trick to put the constraint equation in the standard form for which Lagrange Multipliers works? Any help is appreciated. Thanks
 
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lynxman72 said:
Hi all, I was wondering how to go about solving an optimization problem for a function f(x,y,z) where the two constraint equations are given by:
a is less than or equal to g(x,y,z) is less than or equal to b
(a and b are two distinct numbers)
h(x,y,z) is less than or equal to c
(c is another number distinct from a and b)
Can Lagrange Multipliers solve this problem? In other words, is there some trick to put the constraint equation in the standard form for which Lagrange Multipliers works? Any help is appreciated. Thanks


When you have one constraint, you write it as (let p be the lambda thing or whatever you use)...

Note: f_x is the partial derivative with respect to x.

f_x (x,y,z) = p * g_x (x,y,z)
f_y (x,y,z) = p * g_y (x,y,z)
f_z (x,y,z) = p * g_z (x,y,z)
g(x,y,z) = k

... where k is the constraint.

For multiple constraints, simply write as (where w is your lambda_2)...

f_x (x,y,z) = p * g_x (x,y,z) + w * h_x (x,y,z)
f_y (x,y,z) = p * g_y (x,y,z) + w * h_y (x,y,z)
f_z (x,y,z) = p * g_z (x,y,z) + w * h_z (x,y,z)
g(x,y,z) = k
h(x,y,z) = j

...where k and j are the constraints.

See the pattern here? You can add as many as you want, but I do know that it's not fun solving, for most of them anyways. :-p

If you have a <= or >= as a constraint, you simply find the critical points within the boundaries, evaluate them, and evaluate all the points on the boundary, and then take the largest value. This is basically the same thing as finding a maximum/minimum.
 
Jason, thanks for the response. I understand how to use the Lagrange multipliers for an equality constraint. I think I didn't describe my problem clearly, the inequality constraints are on the variables, not on the function itself...here is my specific problem: the function to be maximized is

f(x,y,z,t,w)=ln((y^2-x^2)(z^2-t^2)w^3))+.8x-1.2y-20z/17+14t/17-w^3/pi^3

subject to the constraints:
x/2+y+3z+3t+2.5w<=30
1800<=130x+150y+200z+70t+110a<=3000

I computed all of the partials (for any values of the variables) and set them equal to zero and found (2,3,7/3,10/3/pi) to be the only critical point but this gives a value of -3 which doesn't make much sense for the problem I'm working with, so I don't think I went about it right...
 
lynxman72 said:
Jason, thanks for the response. I understand how to use the Lagrange multipliers for an equality constraint. I think I didn't describe my problem clearly, the inequality constraints are on the variables, not on the function itself...here is my specific problem: the function to be maximized is
f(x,y,z,t,w)=ln((y^2-x^2)(z^2-t^2)w^3))+.8x-1.2y-20z/17+14t/17-w^3/pi^3
subject to the constraints:
x/2+y+3z+3t+2.5w<=30
1800<=130x+150y+200z+70t+110a<=3000
I computed all of the partials (for any values of the variables) and set them equal to zero and found (2,3,7/3,10/3/pi) to be the only critical point but this gives a value of -3 which doesn't make much sense for the problem I'm working with, so I don't think I went about it right...

Did you check the values around the boundaries as well?

You must check those at the critical points AND those on the boundary, and then take the maximum value.
 
That's my problem, I don't understand how to do that; I have two constraint equations for five variables and so I don't know how to figure out the boundaries of each variable...
 
I tihnk the thing you need are called slack variables. google for details about them cos it's been too long since i did anything them with them to know i'll get it right.
 
http://www.mpri.lsu.edu/textbook/Chapter2-b.htm#Inequality

Here's one using Lagrange Multiplies with inequality constraints like you are talking about.
 
Thanks for the link. Where I'm confused is what to do about having both an upper and lower bound as my constraints, if it was just upper bound I see what to do but I don't know how to handle both.
 

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