Maximizing Summation of F_t*R: Partial Derivative w.r.t R

In summary, the conversation discusses finding the value of R that maximizes the summation of a function involving F and R over a specified range. The speaker suggests taking the partial derivative of the function with respect to R and setting it equal to zero to solve for the desired value of R. They also mention that constants such as F_t, I_p, and I_r can be factored out of the sum and that the differentiation operator can be moved inside the sum. The speaker offers some tips for solving the resulting equation and wishes the asker good luck.
  • #1
Patrick94
3
0
I have the summation (from i=0 to n) of (F[itex]_{t}[/itex])(R) / (1+d)^i
where F[itex]_{t}[/itex] = (F[itex]_{t-1}[/itex])[(R)(I[itex]_{p}[/itex]) +(1-R)(I[itex]_{r}[/itex])]

(The F[itex]_{t-1}[/itex] is referring to the value of F in the last period)

I want to find the value of R that maximizes the summation. So must I take the partial derivative wrt R? How do I do this?

Thanks
 
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  • #2
So wait, [itex]F_t[/itex] and [itex]R[/itex] don't depend on [itex]i[/itex]? What is [itex]I_p[/itex] and [itex]I_r[/itex]?

If those things don't depend on [itex]i[/itex] you can pull them out of the sum.

Regardless, you can treat the entire thing as a function of [itex]R[/itex]. ([itex]f(R)=...[/itex]. Summation on right side.)

You can imagine that as you change the value of [itex]R[/itex], the value of [itex]f[/itex] changes as well. It's hard to get an idea of what this function may look like if you plotted it, but the point is to find [itex]\frac{\partial f}{\partial R}[/itex] and set it equal to zero.

[itex]F_t[/itex] is a constant that depends on your choice of R and depends on the already defined constant [itex]F_{t-1}[/itex]. Since you are taking the derivative w.r.t R, you first want to replace [itex]F_t[/itex] in the sum with its definition. You may have to do something similar with [itex]I_p[/itex] and [itex]I_r[/itex], depending on what they are defined as. However, once you have the entire thing expressed explicitly in terms of R, (i.e. all values that depend on R are written out in full), then you can take [itex]\frac{\partial}{\partial R}[/itex]. It's important to remember that the differentiation operator [itex]\frac{\partial}{\partial R}[/itex] can be moved inside the sum and you can take the derivative of each term of the sum independently.

Once you have taken the derivative w.r.t R, (i.e. you've found [itex]\frac{\partial f}{\partial R}[/itex],) you must set it equal to zero and solve like a normal optimization problem. Hopefully there is only one solution to the resulting equation, telling you the desired value of R. It is possible however that there may be multiple places were [itex]\frac{\partial f}{\partial R}[/itex] is equal to zero, in this case you have to do a little bit of investigating to determine which value of R really maximizes the sum.

I hope I am understanding your question right and I hope this helps in some way. Good luck
 

FAQ: Maximizing Summation of F_t*R: Partial Derivative w.r.t R

1. What does the partial derivative of F_t*R mean in terms of maximizing the summation?

The partial derivative of F_t*R with respect to R represents the rate of change in the total sum as R increases. This means that by taking this derivative and setting it equal to 0, we can find the optimal value of R that maximizes the summation.

2. How is this concept applied in scientific research?

The maximization of summation is a common problem in many scientific fields, such as economics, engineering, and physics. The partial derivative with respect to R allows researchers to find the optimal value for a variable in a given equation, leading to more efficient and effective solutions.

3. Can this concept be applied to real-world scenarios?

Yes, the concept of maximizing summation with partial derivatives can be applied to real-world scenarios. For example, in economics, this concept can be used to determine the optimal amount of resources to allocate for maximum profit. In physics, it can be used to optimize the trajectory of a rocket for a successful launch.

4. Are there any limitations to using partial derivatives for maximizing summation?

While partial derivatives can be a powerful tool for maximizing summation, there are limitations to consider. If the equation is complex or has multiple variables, the partial derivative may not provide an accurate or feasible solution. In these cases, other optimization techniques may be necessary.

5. How can one interpret the results of the partial derivative for maximizing summation?

The results of the partial derivative will provide the optimal value for the variable being considered. This value can then be plugged back into the original equation to maximize the summation. Additionally, the sign of the derivative (positive or negative) can indicate if the optimal value is a maximum or minimum for the summation.

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