Minimizing x in "(A+Bx)/(1-e^(-βx))-(B/β)

  • Thread starter techiejan
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In summary, to minimize the function {(A+Bx)/(1-e^(-βx))}-(B/β), we can use the standard method of setting the derivative equal to 0 and solving for x. However, this does not take into account the constraint x/(1-e^(-βx)))- (1/β)≤ T. To incorporate the constraint, we can use the Kuhn-Tucker conditions and solve for x. The solution may depend on the specific values of the constants A, B, T, and β.
  • #1
techiejan
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How to minimize the following:
{(A+Bx)/(1-e^(-βx))}-(B/β)

Subject to : x/(1-e^(-βx)))- (1/β)≤ T

I need the find the value of x. All A,B,T and β are constants. The numerator is linear. I don't know if the equation remains linear when the denominator is added. When plotted, the equation is a straight increasing line starting from 0. Any help please?
 
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  • #2
All linear equations are of the form "y= Ax+ B" so, no, this does not "remain linear when the denominator is added". The "standard method" of finding minimum values is to set the derivative equal to 0. Here, if I have done the calculations correctly, that reduces to [tex](1- x)e^{\beta x}= 1[/tex] which would be best solved graphically. In fact, the graph of this function looks to me like it does not have any local max or min.
 

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  • #3
techiejan said:
Subject to : x/(1-e^(βx)))- (1/β)≤ T
This bit doesn't make sense. Please clarify. (Look at your parentheses.)) :wink:
 
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  • #4
Thanks HallsofIvy. So let me get this correctly...for the above optimization, we just take derivative of the objective function? How do we incorporate the constraint in that case?

In my case derivation of the objective function yields e^(βx)-βx=1-(βA)/B. And then considering 1/β is large, I get x=√(2A/βB). But then I do not consider the constraint at all?
 
  • #5
I really need this problem solved. Some help please.
 
  • #6
[tex]f(x) = \frac{A+Bx}{1-e^\textrm{ β x}} - \frac{B}{β}[/tex]
[tex]f'(x) = \frac{B(1-e^\textrm{ β x}) + βe^\textrm{ β x}(A+Bx)}{(1-e^\textrm{ β x})^{2}}[/tex]
[tex]f'(x) = 0 = \frac{B-Be^\textrm{ β x} + Aβe^\textrm{ β x}+Bxβe^\textrm{ β x}}{(1-e^\textrm{ β x})^{2}}[/tex]
[tex]B-Be^\textrm{ β x} + Aβe^\textrm{ β x}+Bxβe^\textrm{ β x} = 0[/tex]
[tex]Be^\textrm{ -β x} - B + Aβ+Bxβ = 0[/tex]
[tex]B(e^\textrm{ -β x} + βx) = B - Aβ[/tex]
[tex]\frac{1}{e^\textrm{ β x}} + βx = 1 - \frac{Aβ}{B}[/tex]
 
  • #7
Thanks Math man,

But how do I incorporate the constraint ?
 
  • #8
Sorry, I forgot about the constraint. You can use Kuhn-Tucker conditions:
[tex]f(x) = \frac{A+Bx}{1-e^\textrm{ β x}} - \frac{B}{β}[/tex]
[tex]g(x) = \frac{x}{1-e^\textrm{ β x}} - \frac{1}{β}[/tex]
[tex]g(x) ≤ T[/tex]
The Kuhn-Tucker conditions for minimizing single variable functions are:
[tex]0 = -f'(x) - λg'(x)[/tex]
[tex]g(x) ≤ T[/tex]
[tex]λ ≥ 0[/tex]
[tex]λ(g(x)-T) = 0[/tex]
So to apply the Kuhn Tucker conditions here:
[tex]\frac{-B+Be^\textrm{ β x} - Aβe^\textrm{ β x}-Bxβe^\textrm{ β x}}{(1-e^\textrm{ β x})^{2}} = λ\frac{(1-e^\textrm{ β x}) + βxe^\textrm{ β x}}{(1-e^\textrm{ β x})^{2}}[/tex]
[tex]-B+Be^\textrm{ β x} - Aβe^\textrm{ β x}-Bxβe^\textrm{ β x} = λ-λe^\textrm{ β x} + λβxe^\textrm{ β x}[/tex]
[tex]e^\textrm{ β x}(B - Aβ-Bxβ+λ - λβx)-B-λ = 0[/tex]
This can be combined with the remaining conditions:
[tex]λ(\frac{x}{1-e^\textrm{ β x}}-\frac{1}{β}-T) = 0[/tex]
[tex]\frac{x}{1-e^\textrm{ β x}} ≤ T + \frac{1}{β}[/tex]
[tex]λ ≥ 0[/tex]
Use these to solve for x.
 
  • #9
Minimising {(A+Bx)/(1-e-βx)}-(B/β) is the same as minimising f(x) = (A+Bx)/(1-e-βx) [0]
Ignoring the constraint for the moment, setting the derivative to zero gives me:
eβx = 1 + βx + Aβ/B [1]
(Rather different from what HofI got...)
Whether a solution of the above blows the constraint may well depend on the details of the constants. As oay pointed out, there's a syntax error in your constraint. I assume it's just an extra right parenthesis, so should read:
x/(1-e-βx) ≤ T + 1/β [2]
Since the function to be minimised clearly has exactly one minimum between 0 and +∞, if the solution of [1] is disallowed by [2] then the desired answer gives equality in [2]:
x/(1-e-βx) = T + 1/β [3]
 
  • #10
Thank you very much!
 

What is the purpose of minimizing x in this equation?

The purpose of minimizing x in this equation is to find the value of x that makes the entire expression equal to its minimum value. This is often done in order to optimize a system or find the most efficient solution.

Why is minimizing x important in this context?

Minimizing x is important in this context because it can help to simplify a complex mathematical expression and make it easier to solve. It can also provide valuable insights into the behavior and characteristics of the system being studied.

What is the relationship between x and the other variables in this equation?

The value of x is dependent on the values of A, B, and β in this equation. Changing any of these variables can affect the value of x, and minimizing x requires finding the values of A, B, and β that result in the lowest possible value for x.

How does the presence of e^(-βx) affect the minimization of x in this equation?

The presence of e^(-βx) in this equation makes the minimization of x more complex, as it introduces an exponential function that is sensitive to changes in the value of x. This means that finding the optimal value for x may require additional steps or techniques.

What are the potential applications of minimizing x in this equation?

The applications of minimizing x in this equation can vary, depending on the context in which it is being used. It can be applied in fields such as economics, physics, and engineering to optimize systems and improve efficiency. It can also be used to model and analyze complex systems in various industries.

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