Is the Objective Function Always Nonnegative in Linear Optimization Problems?

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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)