Chapter 4 applications of differentiation
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Chapter 4 – Applications of Differentiation. 4.7 Optimization Problems. Optimization Problems.

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Chapter 4 – Applications of Differentiation

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Chapter 4 applications of differentiation

Chapter 4 – Applications of Differentiation

4.7 Optimization Problems

4.7 Optimization Problems


Optimization problems

Optimization Problems

  • In solving such practical problems the greatest challenge is often to convert the word problem into a mathematical optimization problem by setting up the function that is to be maximized or minimized.

  • Let’s recall the problem-solving principles.

4.7 Optimization Problems


Steps in solving optimization problems

Steps in Solving Optimization Problems

4.7 Optimization Problems


Example 1

Example 1

Find the maximum area of a rectangle inscribed between the x-axis and making sure to follow all steps.

4.7 Optimization Problems


Example 2

Example 2

A box with a square base and open top must have a volume of 32,000 cm3. Find the dimensions of the box that minimize the amount of material used.

4.7 Optimization Problems


Example 3

Example 3

Find two numbers whose difference is 100 and whose product is a minimum.

4.7 Optimization Problems


Example 4

Example 4

A Norman window has the shape of a rectangle surmounted by a semicircle. (The diameter is equal to the width of the rectangle.) If the perimeter of the window is 30 ft, find the dimensions of the window so that the greatest possible amount of light is admitted.

4.7 Optimization Problems


Example 5

Example 5

  • Show that of all the rectangles with a given area, the one with the smallest perimeter is a square.

  • Show that of all the rectangles with a given perimeter, the one with the greatest area is a square.

4.7 Optimization Problems


Example 6

Example 6

A farmer wants to fence an area of 1.5 million square feet in a rectangular field and then divide it in half with a fence parallel to one of the sides of the rectangle. How can he do this so as to minimize the cost of the fence?

4.7 Optimization Problems


Example 7

Example 7

  • Find a point on the line 6x + y = 9 that is closest to the point (-3,1).

4.7 Optimization Problems


Applications to business and economics

Applications to Business and Economics

  • We know that if C(x), the cost function, is the cost of producing x units of a certain product, then the marginal cost is the rate of change of C with respect to x.

  • In other words, the marginal cost function is the derivative, C(x), of the cost function.

  • Now let’s consider marketing. Let p(x) be the price per unit that the company can charge if it sells x units.

  • Then p is called the demand function (or price function) and we would expect it to be a decreasing function of x.

4.7 Optimization Problems


Applications to business and economics1

Applications to Business and Economics

  • If x units are sold and the price per unit is p(x), then the total revenue is

    R(x) = xp(x)

    and R is called the revenue function.

  • The derivative R of the revenue function is called the marginal revenue function and is the rate of change of revenue with respect to the number of units sold.

4.7 Optimization Problems


Applications to business and economics2

Applications to Business and Economics

  • If x units are sold, then the total profit is

    P(x)= R(x)– C(x)

    and P is called the profit function.

  • The marginal profit function is P, the derivative of the profit function.

4.7 Optimization Problems


Example 8

Example 8

Show that if the profit P(x) is a maximum, then the marginal revenue equals the marginal cost.

If is the cost function and is the demand function, find the production level that will maximize profit.

4.7 Optimization Problems


Example 9

Example 9

A baseball team plays in a stadium that hold 55,000 spectators. With ticket prices at $10, the average attendance has been 27,000. When ticket prices were lowered to $8, the average attendance rose to 33,000.

  • Find the demand function assuming that it is linear.

  • How should ticket prices be set to maximize revenue?

4.7 Optimization Problems


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