MHB What is the maximum of the given function in the closed square $D$?

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The function f(x1, x2) = x1 + x2 achieves its maximum value of 2 within the closed square D defined by the vertices (0,0), (1,0), (1,1), and (0,1). This maximum occurs at the point (1,1), where both x1 and x2 are at their upper limits. The discussion emphasizes that since all other feasible values of the function are less than 2, (1,1) is confirmed as the optimal solution. It is noted that for linear programming problems, only corner points need to be evaluated to find the maximum. Thus, the maximum of the function is conclusively established as 2 at the point (1,1).
evinda
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Hello! (Wave)

We consider the function $f(x_1,x_2)=x_1+x_2$ and the (closed) square $D$ with edges $(0,0), (1,0), (1,1), (0,1)$.

Check if $f$ has a maximum in $D$ and if so, find the points of $D$ at which this maximum is achieved.$$\max_{(x_1,x_2) \in D} (x_1+x_2)$$

View attachment 4792

$$(S)\left\{\begin{matrix}
x_1 \geq 0\\
x_2 \geq 0\\
x_1 \leq 1\\
x_2 \leq 1
\end{matrix}\right.$$

so $D=\{ (x_1, x_2) \in \mathbb{R}^2: x_1, x_2 \text{ satisfy } (S)\}$.

$$x_1, x_2 \geq 0$$

Obviously $\max_{(x_1, x_2) \in D} (x_1+x_2) \leq \max_{(x_1, x_2) \in D} x_1 + \max_{(x_1, x_2) \in D} x_2 \leq 1+1=2 $

and $\max_{(x_1, x_2) \in D} (x_1+x_2)=f(1,1)$, so $(1,1)$ is the optimal solution of the linear programming problem.
How do we deduce that the maximum of the given function is $2$. Because of the fact that the maximum is less or equal to $2$ and there is a point $(1,1)$ for which the value $2$ is achieved?
 

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evinda said:
Hello! (Wave)

and $\max_{(x_1, x_2) \in D} (x_1+x_2)=f(1,1)$, so $(1,1)$ is the optimal solution of the linear programming problem.

Small correction: it's "an" optimal solution.
We don't know yet if it is "the" optimal solution. (Nerd)
How do we deduce that the maximum of the given function is $2$. Because of the fact that the maximum is less or equal to $2$ and there is a point $(1,1)$ for which the value $2$ is achieved?

Yes. (Smile)

Still, I believe the question asks for "all" points... (Sweating)
 
I like Serena said:
Small correction: it's "an" optimal solution.
We don't know yet if it is "the" optimal solution. (Nerd)

Why? Aren't all the other values that $f$ could get smaller? Or am I wrong? (Thinking)
I like Serena said:
Yes. (Smile)

Still, I believe the question asks for "all" points... (Sweating)

So in order to show that we have one optimal solution do we have to reject the other feasible solutions?
 
evinda said:
Why? Aren't all the other values that $f$ could get smaller? Or am I wrong? (Thinking)

So in order to show that we have one optimal solution do we have to reject the other feasible solutions?

Indeed. And that's exactly the point.
Because all other values that $f$ could get are smaller, it is "the" maximum. (Happy)

Moveover, if the boundary were not included, there would be no maximum, only a supremum. (Nerd)
 
I like Serena said:
Indeed. And that's exactly the point.
Because all other values that $f$ could get are smaller, it is "the" maximum. (Happy)

Moveover, if the boundary were not included, there would be no maximum, only a supremum. (Nerd)

In order to reject the other feasible solutions do we have to compute the values $f(0,0), f(1,0), f(0,1)$ and the value of $f\left( \frac{1}{m}, \frac{1}{n}\right), n,m \in \mathbb{N}$ ? (Thinking)
 
evinda said:
In order to reject the other feasible solutions do we have to compute the values $f(0,0), f(1,0), f(0,1)$ and the value of $f\left( \frac{1}{m}, \frac{1}{n}\right), n,m \in \mathbb{N}$ ? (Thinking)

From a linear programming point of view, indeed we need to check all the corner points.
But that only works if the function to maximize is a linear function.

Where do your $f\left( \frac{1}{m}, \frac{1}{n}\right)$ come from? (Wondering)

More generally, we would take the partial derivatives and see where they are zero.
Additionally, we would inspect the boundaries to see if there is a maximum there.
For linear programming problems that can be simplified to just looking at the corner points. (Nerd)
 
I like Serena said:
From a linear programming point of view, indeed we need to check all the corner points.
But that only works if the function to maximize is a linear function.

Where do your $f\left( \frac{1}{m}, \frac{1}{n}\right)$ come from? (Wondering)

More generally, we would take the partial derivatives and see where they are zero.
Additionally, we would inspect the boundaries to see if there is a maximum there.
For linear programming problems that can be simplified to just looking at the corner points. (Nerd)

Ah I didn't know that if we have a linear programming problem we look only at the corner points. (Whew)

So we say that $\max_{(x_1, x_2) \in D} (x_1+x_2) \leq 2$ and

$$f(0,0)=0 \\ f(1,0)=1 \\ f(1,1)=2 \\ f(0,1)=1$$

thus the maximum of the function $f$ is $2$ and is achieved for $(x_1, x_2)=(1,1)$, right?
 
evinda said:
Ah I didn't know that if we have a linear programming problem we look only at the corner points. (Whew)

So we say that $\max_{(x_1, x_2) \in D} (x_1+x_2) \leq 2$ and

$$f(0,0)=0 \\ f(1,0)=1 \\ f(1,1)=2 \\ f(0,1)=1$$

thus the maximum of the function $f$ is $2$ and is achieved for $(x_1, x_2)=(1,1)$, right?

Yep! (Happy)
 
I like Serena said:
Yep! (Happy)

Nice... Thank you! (Smile)
 
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