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Section 5.7

Section 5.7. Systems of Inequalities And Linear Programming. Linear Inequality in Two Variables. An inequality that can be written as Ax + By < C or Ax + By > C , where A, B, and C are real numbers and A and B are not both 0. The symbol < may be replaced with , >, or .

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Section 5.7

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  1. Section 5.7 Systems of Inequalities And Linear Programming

  2. Linear Inequality in Two Variables An inequality that can be written as Ax + By < C or Ax + By > C, where A, B, and C are real numbers and A and B are not both 0. The symbol < may be replaced with , >, or .

  3. Linear Inequalities The solution set of an inequality is the set of all ordered pairs that make it true. The graph of an inequality represents its solution set.

  4. Graphing an Inequality • Draw the boundary line • Make the inequality an equation. • Graph the equation. >or <Solid line > or < Dashed line • Choose a test point. (Any point not on the graph.) • Substitute test point into original inequality. • Shade the appropriate region. • Shade the region that includes the test point if it makes the inequality true. • If the test point does not make the inequality true, shade the other side of the line.

  5. Graph y > x 4. We begin by graphing the related equationy = x  4. We use a dashed line because the inequality symbol is >. This indicates that the line itself is not in the solution set. Example

  6. Example continued • Determine which half-plane satisfies the inequality by choosing a test point.

  7. To Graph a Linear Inequality:A Recap • Replace the inequality symbol with an equals sign and graph this related equation. If the inequality symbol is < or >, draw the line dashed. If the inequality symbol is  or , draw the line solid. • The graph consists of a half-plane on one side of the line and, if the line is solid, the line as well. To determine which half-plane to shade, test a point not on the line in the original inequality. If that point is a solution, shade the half-plane containing that point. If not, shade the opposite half-plane.

  8. Graph: 4x + 2y 8 Graph the related equation, using a solid line. Example

  9. Example continued Determine which half-plane to shade by choosing a test point.

  10. Graph x > 2 on a plane. 1. Graph the related equation. 2. Pick a test point (0, 0). x > 2 0 > 2 False Because (0, 0) is not a solution, we shade the half-plane that does not contain that point. Example

  11. Graph y 2 on a plane. 1. Graph the related equation. 2. Select a test point (0, 0). y  2 0  2 True Because (0, 0) is a solution, we shade the region containing that point. Example

  12. Graph the solution set of the system. First, we graph x + y 3 using a solid line. Choose a test point (0, 0) and shade the correct plane. Next, we graph x  y > 1 using a dashed line. Choose a test point and shade the correct plane. Systems of Linear Inequalities The solution set of the system of equations is the region shaded both red and green, including part of the line x + y 3.

  13. Graph the following system of inequalities and find the coordinates of any vertices formed: Example

  14. Example continued We graph the related equations using solid lines. We shade the region common to all three solution sets.

  15. The system of equations from inequalities (1) and (2): y + 2 = 0 x + y = 2 The vertex is (4, 2). The system of equations from inequalities (1) and (3): y + 2 = 0 x + y = 0 The vertex is (2, 2). The system of equations from inequalities (2) and (3): x + y = 2 x + y = 0 The vertex is (1, 1). Example continued To find the vertices, we solve three systems of equations.

  16. Linear Programming • In many applications, we want to find a maximum or minimum value. Linear programming can tell us how to do this. • Constraints are expressed as inequalities. The solution set of the system of inequalities made up of the constraints contains all the feasible solutions of a linear programming problem. • The function that we want to maximize or minimize is called the objective function.

  17. Linear Programming Procedure • To find the maximum or minimum value of a linear objective function subject to a set of constraints: 1. Set up objective function and define constraints. 2. Graph the region of feasible solutions. 3. Determine the coordinates of the vertices of the region. 4. Evaluate the objective function at each vertex. The largest and smallest of those values are the maximum and minimum values of the function, respectively.

  18. Example • A tray of corn muffins requires 4 cups of milk and 3 cups of wheat flour. A tray of pumpkin muffins requires 2 cups of milk and 3 cups of wheat flour. There are 16 cups of milk and 15 cups of wheat flour available, and the baker makes $3 per tray profit on corn muffins and $2 per tray profit on pumpkin muffins. How many trays of each should the baker make in order to maximize profits?

  19. Example continued • A tray of corn muffins requires 4 cups of milk and 3 cups of wheat flour. A tray of pumpkin muffins requires 2 cups of milk and 3 cups of wheat flour. There are 16 cups of milk and 15 cups of wheat flour available, and the baker makes $3 per tray profit on corn muffins and $2 per tray profit on pumpkin muffins. Solution: We let x = the number of corn muffins and y = the number of pumpkin muffins. Then the profit P is given by the function P = 3x + 2y.

  20. Example continued • A tray of corn muffins requires 4 cups of milk and 3 cups of wheat flour. A tray of pumpkin muffins requires 2 cups of milk and 3 cups of wheat flour. There are 16 cups of milk and 15 cups of wheat flour available, and the baker makes $3 per tray profit on corn muffins and $2 per tray profit on pumpkin muffins. • We know that x muffins require 4 cups of milk and y muffins require 2 cups of milk. Since there are no more than 16 cups of milk, we have one constraint. 4x + 2y 16 • Similarly, the muffins require 3 and 3 cups of wheat flour. There are no more than 15 cups of flour available, so we have a second constraint. 3x + 3y 15 • We also know x  0 and y  0 because the baker cannot make a negative number of either muffin.

  21. Example continued • Thus we want to maximize the objective function P = 3x + 2y subject to the constraints: 4x + 2y 16, 3x + 3y 15, x  0, y  0. We graph the system of inequalities and determine the vertices. Next, we evaluate the objective function P at each vertex.

  22. Vertices Profit P = 3x+ 2y (0, 0) P = 3(0) + 2(0) = 0 (4, 0) P = 3(4) + 2(0) = 12 (0, 5) P = 3(0) + 2(5) = 10 (3, 2) P = 3(3) + 2(2) = 13 Maximum Example continued The baker will make a maximum profit when 3 trays of corn muffins and 2 trays of pumpkin muffins are produced.

  23. Example • Omar owns a car and a moped. He can afford 12 gal of gasoline to be split between the car and the moped. Omar’s car gets 20 mpg and, with the fuel currently in the tank, can hold at most an additional 10 gal of gas. His moped gets 100 mpg and can hold at most 3 gal of gas. • How many gallons of gasoline should each vehicle use if Omar wants to travel as far as possible? • What is the maximum number of miles that he can travel?

  24. Example continued Omar owns a car and a moped. He can afford 12 gal of gasoline to be split between the car and the moped. Omar’s car gets 20 mpg and, with the fuel currently in the tank, can hold at most an additional 10 gal of gas. His moped gets 100 mpg and can hold at most 3 gal of gas.

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