Optimization With Inequality Constraints:

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Homework Help Overview

The discussion revolves around an optimization problem involving a function with multiple variables and inequality constraints. The original poster is attempting to maximize a complex logarithmic function subject to specific bounds on the variables, which represent servings of food.

Discussion Character

  • Exploratory, Assumption checking, Problem interpretation

Approaches and Questions Raised

  • Participants discuss the method of finding critical points through partial derivatives and express confusion regarding how to handle the inequality constraints. There is mention of the need to test boundaries and the challenge of ensuring that critical points satisfy all constraints.

Discussion Status

Some participants have suggested using Lagrange Multipliers to address the constraints as equalities, while others are exploring the implications of this approach and questioning how many multipliers would be needed. The conversation indicates a productive exploration of methods without reaching a consensus on the best approach.

Contextual Notes

There is an acknowledgment that all variables must be non-negative, and the original poster expresses uncertainty about deriving bounds for each variable from the given constraints.

lynxman72
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Hi all, I'm having trouble with the following problem: It was given as a word problem from which to infer the mathematics but basically it is this:

Maximize: 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:
0<= .5x+y+3z+3y+2.5w<+30
1800<=+130x+150y+200z+70t+110w<=3000

the problem is I don't know how to handle constraints like these, where there is an upper and lower bound, and I'm just confused because it seems like there is a lot going on...the only thing i knew how to do to
1) remark that the max exists because the function is defined on this closed and bounded set 2) compute all of the partials set them equal to zero and find the critical points and hope that I would find the global max and the values at the global max would lie inside the constraints, but this didn't work because when I do this I get the critical point
(2,3,7/3,10/3,pi) which doesn't satisfy the second constraint.
I know that when you have a function defined on a closed bounded set to find the local max you find the crit. points and test the values at the boundaries and just compare but i don't know how to obtain bounds on each individual variable from the constraints or what to do. any help would be appreciated
 
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oh sorry by the way I also know that each variable is >=0 from the context of the problem (each variable represents a serving of food). thank you
 
If you take the first order partials, set them = 0 , and solve the resulting system, you get:

[tex]x = 2, y = 3, z = \frac{10}{3}, t = \frac{7}{3}, w = \pi[/tex]

but these values do not fit the first constraint, namely that

[tex]0\leq \frac{x}{2}+y+3z+3y+\frac{5}{2}w< 30[/tex]

in fact those values of x,y,z,t, and w, will give [itex]\frac{x}{2}+y+3z+3y+\frac{5}{2}w=23+\frac{5}{2}\pi = 30.86 \mbox{ (appox.)}[/itex]

Also note that [itex]f(1,1,6.70000000000000016,1,1)=-\infty[/itex].
 
Right, I was just saying all I could figure out how to do was try to find this global max and hope it fit the constraints (it didn't), so any advice on another way to proceed?
 
Crap, I minimized. Let me look again.

Well, you might try Lagrange Multipliers with the constraints as equalities for the upper and lower bounds of the inequalities.

That version of Lagrange Multipliers goes:

To find the extrema of [itex]f(x,y,z,...)[/itex] subject to the constraints [itex]g_{1}(x,y,z,...)=k_{1}, g_{2}(x,y,z,...)=k_{2}, g_{3}(x,y,z,...)=k_{3},...[/itex] set

[tex]\vec{\nabla} f(x,y,z,...) = \lambda_{1}\vec{\nabla} g_{1}(x,y,z,...) + \lambda_{2}\vec{\nabla} g_{2}(x,y,z,...) + \lambda_{3}\vec{\nabla} g_{3}(x,y,z,...) +\cdot\cdot\cdot[/tex]

and solve as normal.
 
So what you're saying is then I will have four Lagrange multipliers? I treat it as four constraint equalitites?
 

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