Optimizing Rectangle Dimensions for Area 1000m^2: A Simple Solution

In summary, the conversation discussed a simple optimization problem involving finding the dimensions of a rectangle with the smallest possible perimeter given an area of 1000 m^2. The solution involved using the equations for perimeter and area to set up an equation and then differentiating it to find the minimum value. A simple approach was also suggested. Ultimately, the solution was found to be x = 10√10.
  • #1
physicsed
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0
[SOLVED] simple optimization problem

Homework Statement



find the dimensions of a rectangle with area 1000 m^2 whose perimeter is as small as possible

Homework Equations



perimeter = 2x + 2y

area = xy

1000 = xy

y= 1000/x

perimeter = 2x + 2(1000/x)



The Attempt at a Solution



am stuck

any help from anybody?
 
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  • #2
perimeter = 2x + 2(1000/x)
now differentiate!

should learn concepts from the book (if you don't know why you differentiate)

Simple approach:

plot your perimeter and pick and minimum value
this is realistic problem, so going for really large x and -ve x would be nonsense
 
  • #3
2-(2000/x^2)=0

i think
 
  • #4
physicsed said:
2-(2000/x^2)=0

i think
You think? ... have confidence!
 
  • #5
2/1000=x^-2
 
  • #6
no, try 2=2000/x^2
 
  • #7
x=square root of .001?
 
  • #8
[tex]x=\sqrt{1000}=\sqrt{10\cdot10^2}=10\sqrt{10}[/tex]
 
  • #9
[tex]x=\sqrt{1000}=\sqrt{10\cdot10^2}=10\sqrt{10}[/tex]

how did u get that?
 
  • #10
[tex]2=\frac{2000}{x^2}\rightarrow x^2=\frac{2000}{2}\rightarrow x^2=1000[/tex]
 
  • #11
thanks alot. am messed up today!
 

FAQ: Optimizing Rectangle Dimensions for Area 1000m^2: A Simple Solution

1. What is a simple optimization problem?

A simple optimization problem is a mathematical problem that involves finding the maximum or minimum value of a function. This can be done by adjusting the variables of the function to find the most optimal solution.

2. How do you solve a simple optimization problem?

To solve a simple optimization problem, you need to first identify the objective function, which is the function that you want to optimize. Then, you need to determine the constraints, or limitations, that the variables of the function must satisfy. Finally, you can use mathematical techniques such as differentiation or graphical methods to find the optimal solution.

3. What are some real-life examples of simple optimization problems?

Simple optimization problems can be found in many real-life situations, such as maximizing profits for a business, minimizing travel time for a delivery route, or finding the most efficient way to allocate resources for a project. They can also be used in engineering and science to optimize designs and processes.

4. What are some common techniques used to solve simple optimization problems?

Some common techniques used to solve simple optimization problems include linear programming, gradient descent, and the simplex method. These methods use mathematical concepts and algorithms to find the optimal solution for a given function and its constraints.

5. What are the benefits of solving simple optimization problems?

Solving simple optimization problems can provide numerous benefits, such as improving efficiency, reducing costs, and increasing productivity. It can also help to identify the most efficient and effective solutions to complex problems, leading to better decision making and problem-solving skills.

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