Is the Objective Function Always Nonnegative in Linear Optimization Problems?

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SUMMARY

The discussion centers on the nonnegativity of the objective function in linear optimization problems, specifically examining the function w = y_1 - 2y_2 + y_3. The user demonstrates that for the solution y = (2t, 3t, t)^t, the objective function evaluates to w = -3t, which approaches negative infinity as t approaches positive infinity. The confusion arises from the assumption that objective functions must always be nonnegative, which is not universally applicable in theoretical contexts. The professor's input suggests that while nonnegativity is common in applied problems, it is not a strict requirement in all theoretical formulations.

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Homework Statement



Given the follow LOP P

I am just going to write down the obj function because that is most important for my question and the constraints aren't

[tex]w = y_1 - 2y_2 + y_3[/tex]

I was asked to show that [tex]y = (2t, 3t,t)^t[/tex] is a solution for all [tex]t\geq 0[/tex]

So w = 2t - 2(3t) +t = 2t - 6t + t = -4t + t = -3t

Now initally I thought that as [tex]t \to \infty[/tex], [tex]-3t \to -\infty[/tex]

I checked the key provided by my prof and he took [tex]t \to -\infty[/tex] and [tex]-3t \to \infty[/tex]

Is it because we always assume w >0?

The flaw I made is that I never consider [tex]t \to \pm \infty[/tex].




Homework Equations





The Attempt at a Solution

 
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In applied problems, the objective function is usually nonnegative, but in more theoretic presentations, I don't see why this needs to be true.
 


Mark44 said:
In applied problems, the objective function is usually nonnegative, but in more theoretic presentations, I don't see why this needs to be true.

I asked my prof today and he kinda said the same thing about "yes intuitively that is right, we want obj f > 0". Then he added a bunch of things that confused me even more...

He stated something like this

max z = -min(-z)
 

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