Optimization With Inequality Constraints:

In summary, the problem is that the author does not know how to find the global max or the values at the global max that would satisfy the constraints.
  • #1
lynxman72
16
0
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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  • #2
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
 
  • #3
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].
 
  • #4
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?
 
  • #5
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.
 
  • #6
So what you're saying is then I will have four Lagrange multipliers? I treat it as four constraint equalitites?
 

1. What is optimization with inequality constraints?

Optimization with inequality constraints is a mathematical technique used to find the maximum or minimum value of a function while satisfying a set of constraints. These constraints can take the form of inequalities, such as greater than or less than statements.

2. How is optimization with inequality constraints different from unconstrained optimization?

In unconstrained optimization, there are no restrictions on the variables and the goal is simply to find the maximum or minimum value of a function. In optimization with inequality constraints, the variables must also satisfy a set of constraints in addition to optimizing the function.

3. What are some common methods used for optimization with inequality constraints?

Some commonly used methods for optimization with inequality constraints include the interior point method, barrier method, and penalty method. These methods involve iteratively adjusting the variables to satisfy the constraints while optimizing the function.

4. Can optimization with inequality constraints be applied to real-world problems?

Yes, optimization with inequality constraints has many real-world applications, such as in economics, engineering, and operations research. For example, it can be used to optimize production processes while considering resource limitations, or to optimize investment portfolios while accounting for risk constraints.

5. What are some challenges of optimization with inequality constraints?

One challenge of optimization with inequality constraints is that it can be computationally intensive, especially for complex problems with many constraints. Additionally, finding an optimal solution may not always be possible, and trade-offs may need to be made between satisfying the constraints and optimizing the function.

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