Maximize Product of 3 Non-Negative Summands

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In summary, the problem asks you to find a decomposition of a number into three nonnegative summands so that the product of them is maximized. You were given the idea of decomposing it into w=x+y+z, w>0, x \geq{}0, y \geq{}0, z \geq{}0, where f(x,y,z)=xyz. You realized that this doesn't seem to produce a maximum, and you don't know how to use the Lagrange multiplier method.
  • #1
Telemachus
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Well, I have this multivariable calculus optimization problem. It says: Decompose a positive number on three non negative summands so that the product of them is maximum.

I thought of something like
[tex]w=x+y+z[/tex], [tex]w>0, x \geq{}0 , y \geq{}0 , z \geq{}0 [/tex]
[tex]f(x,y,z)=xyz[/tex]
[tex]f_x=yz,f_y=xz,f_z=xy[/tex]
The thing is I don't see any maximum here, clearly I'm not setting the things right.
 
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  • #2


F(x,y,z)=xyz has to be maximum with the condition G(x,y,z)= x+y+z - w=0. This conditional maximum can be found by the method of Lagrange multiplier, multiplying the condition with a parameter L and finding the possible extrema of the new function F-LG=xyz-L(x+y+z - w). You get three equation from setting the partial derivatives equal to zero, the forth equation is the condition G=0.



ehild
 
  • #3


Thanks!
 
  • #4


Mmm I think I didn't get it. Let's see what's wrong. I haven't used lagrange multipliers yet, but I want to follow your indications anyway, cause I'll have to use it later. So, It would be great if you can guide me with this, and tell me what I'm doing wrong.

[tex]w=x+y+z\Rightarrow{G=x+y+z-w=0}[/tex]
[tex]F=xyz[/tex]
[tex]F-\lambda G=xyz-\lambda(x+y+z-w)[/tex]

[tex]\begin{Bmatrix}{ f_x=yz-\lambda=0\\f_y=xz-\lambda=0\\f_z=xy-\lambda=0\\x+y+z-w=0 \end{matrix}[/tex]
[tex]\begin{Bmatrix}{ y=\displaystyle\frac{\lambda}{z}\\x=\displaystyle\frac{\lambda}{z}\end{matrix}[/tex]
[tex]\Longrightarrow{xy=\lambda\rightarrow{\displaystyle\frac{\lambda^2}{z^2}=\lambda\rightarrow{z=\sqrt[ ]{\lambda}}}}[/tex]
[tex]w=3\sqrt[ ]{\lambda}[/tex]
Which I think its inconclusive, I'm think I'm not using the Lagrange multipliers on the right way, I don't know how to, so if I'm not so far away from it, It would be great if you can guide me a little more.

Bye there, and thanks again!
 
  • #5


No, you haven't used the Lagrange multipliers wrong, your calculations are correct (except that you diveded by z somewhere, which could be 0). However, you searched conditions on [tex] \lambda [/tex]. You should look for conditions on (x,y,z) instead.

So, to resume, you have following situation

[tex] \left\{\begin{array}{l}
\lambda=yz\\
\lambda=xy\\
\lambda=xz\\
x+y+z=w
\end{array} \right.
[/tex]

So we got xy=yz=xz.
Now there are a few situations which can occur:

1) x=0
Then yz=0, so either y or z equals zero. The fourth equation then yields that (x,y,z) is either the point (0,0,w) or (0,w,0)

2) y=0
analogous to 1, this gives the point (w,0,0) and (0,0,w)

3) z=0
analogous to 1, this gives the point (w,0,0) and (0,w,0)

4) neither x,y or z equal 0
Then xy=yz=xz must imply that x=y=z. The fourth equation then yields that x=y=z=w. So this gives us the point (w,w,w).


So the only points which satisfy the 4 equations are (w,0,0), (0,w,0), (0,0,w) and (w,w,w). Now you can check manually which of these 4 maximizes f.
 
  • #6


Thanks.
 

1. What is the "Maximize Product of 3 Non-Negative Summands" problem?

The "Maximize Product of 3 Non-Negative Summands" problem is a mathematical optimization problem where the goal is to find three non-negative numbers whose product is maximum, given a certain constraint. This problem has various real-life applications, such as maximizing profits in business or maximizing efficiency in resource allocation.

2. How is this problem different from other optimization problems?

This problem is different from other optimization problems because it specifically involves finding three non-negative summands rather than just a single maximum value. This adds an additional layer of complexity to the problem and requires a different approach to finding the optimal solution.

3. What are some common approaches to solving this problem?

Some common approaches to solving this problem include brute force search, dynamic programming, and using mathematical techniques such as Lagrange multipliers. Other methods, such as heuristic algorithms and genetic algorithms, can also be used to find approximate solutions to the problem.

4. What is the time complexity of solving this problem?

The time complexity of solving this problem depends on the chosen method and the size of the input. Brute force search, for example, has a time complexity of O(n^3), while dynamic programming can have a time complexity of O(n^2). Some methods, such as heuristic algorithms, may have a better time complexity but may not always find the optimal solution.

5. Are there any real-life applications of this problem?

Yes, there are many real-life applications of this problem. One example is in finance, where the problem can be used to maximize profits by allocating resources among different investments. It can also be used in resource allocation problems, such as maximizing efficiency in manufacturing processes or optimizing supply chain management.

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