What is the maximum area of a rectangle inscribed in a given region?

In summary, the conversation is about finding the area of the largest rectangle that can be inscribed in a specific region, with one person discussing their approach to finding the maximum value and another person pointing out an error in their calculation. They eventually come to the correct solution using the quadratic formula.
  • #1
skateza
45
0

Homework Statement



Find the area of the largest rectangle that can be inscribed in the region bounded by the graph of y = (4-x)/(2+x) and the coordinate aces in the first quadrant.


I think my only problem with this one is taking the derivative,

this is what i get y' = (-x^2 - 4x + 8)/(2+x)^2

Critical numbers: [1-root(48)]/-2, but that doesn't seem to be giving me a maximum value, can someone take a second look this.
 
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  • #2
If I'm not mistaken:

A = xy, x = x, y = (4-x)/(2+x)

A = x(4-x/2+x)
= (4x - x^2 / 2 + x)

Using Quotient Rule (feel free to use the product rule if you want to):
dy/dx = ((2 + x)(4 -2x) - (4x - x^2)(1)) / (2 + x)^2

=8 -4x +4x -2x^2 - 4x + x^2 / g^2
dy/dx = 8 -x^2 -4x / 4 + 4x + x^2

Graphing that, I can see 2 roots.
(can't be bothered actually solving properly for them now though, :) )
They are:
-5.46410 & 1.464101

Using 1.464101 leads to an area of about 1.0717
Graphing the original function to be optimized, I see that this is correct.
 
  • #3
Okay, doing a poly long divide, I get the dy/dx as:
-1 + 12/(x+2)(x+2) = 0
==>
x^2 + 4x - 8 = 0

This is interestingly the numerator of the original function.

Solving this with quadratic formula yields the same results.
 
  • #4
my error was in using the quadratic formula, i took -a instead of -b which is why i got a strange value.
 

Related to What is the maximum area of a rectangle inscribed in a given region?

1. What is an applied optimization problem?

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There are several types of optimization techniques used in applied optimization problems, including linear programming, nonlinear programming, dynamic programming, and genetic algorithms. Each technique has its own advantages and is suitable for different types of problems.

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An applied optimization problem is solved by using mathematical models and algorithms to find the optimal solution. This involves formulating the problem, setting up an objective function and constraints, and then using optimization techniques to find the best possible solution.

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Solving applied optimization problems can lead to improved efficiency, increased profits, and better decision-making in various industries such as business, engineering, and economics. It also allows for the identification of optimal solutions that may not be apparent through traditional problem-solving methods.

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