Upcoming SlideShare
×

# Lesson 31: The Simplex Method, I

8,260 views

Published on

We illuminate the simplex method for solving linear programming problems

Published in: Technology
5 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
Your message goes here
• thank you sir

Are you sure you want to  Yes  No
Your message goes here
• Excellent ,well done

Are you sure you want to  Yes  No
Your message goes here
Views
Total views
8,260
On SlideShare
0
From Embeds
0
Number of Embeds
35
Actions
Shares
0
599
2
Likes
5
Embeds 0
No embeds

No notes for slide

### Lesson 31: The Simplex Method, I

1. 1. Lesson 31 (KH, Section 11.2) The Simplex Method Math 20 December 5, 2007 Announcements Pset 11 due December 10. Pset 12 due December 17. ML OH: Today 1–3 (SC 323) SS OH: Tonight 8:30–9:30 in Quincy dining hall HW coming Midterm II review slides online Midterm II: tomorrow 7–8:30pm in Hall A
2. 2. Outline Setup Illustrative Problem Slack Variables The Simplex Method, By Example The Initial Basic Feasible Solution Creating a New Tableau Recap of Steps Example
3. 3. Setup A standard linear programming problem is to maximize the quantity c1 x1 + c2 x2 + . . . cn xn = c x subject to constraints a11 x1 + a12 x2 +. . .+ a1n xn ≤ b1 a21 x1 + a22 x2 +. . .+ a2n xn ≤ b2 ... am1 x1 +am2 x2 +. . .+amn xn ≤ bm or Ax ≤ b. We usually include the nonnegativity constraint x ≥ 0. Today we will also assume b ≥ 0.
4. 4. Any vector x which satiﬁes all the inequalities is called a feasible solution to the given problem, and a feasible solution maximizing the objective function is called an optimal solution.
5. 5. Outline Setup Illustrative Problem Slack Variables The Simplex Method, By Example The Initial Basic Feasible Solution Creating a New Tableau Recap of Steps Example
6. 6. Illustrative Problem We will use the baker of before. He is trying to maximize z = 8x + 10y subject to the constraints 2x+ y ≤50 x+ 2y ≤70 x≥ 0 y ≥ 0.
7. 7. Slack Variables We can turn the inequalities into equalities by inserting new variables, which are called slack variables. Thus the ﬁrst equation of constraint becomes 2x + y ≤ 50 =⇒ 2x + y + u = 50, and the second x + 2y ≤ 70 =⇒ x + 2y + v = 70. But u and v are nonnegative. So the new problem is to maximize 8x + 10y subject to constraints 2x+ y +u =50 x+2y +v =70 x ≥0 y ≥0 u≥0 v ≥0
8. 8. In general, we insert slack variables u1 , u2 , . . . , um and the equations of constraint become Ax + u = b, along with x ≥ 0, u ≥ 0. Deﬁnition The vector x in Rn+m is called a basic solution if its obtained by setting n of the variables in this equation equal to zero and solving for the remaining n variables. The m variables are we solve for are called the basic variables, and the n variables set equal to zero are called the nonbasic variables. The vector x is called a basic feasible solution if it is a basic solution that also satisﬁes the inequalities x ≥ 0.
9. 9. Why are basic feasible solutions necessary? Theorem If a LP problem has an optimal solution, then it has a basic optimal solution. This is just a restatement of the corner principle. So we only need to ﬁnd the basic feasible solutions!
10. 10. Back to the Baker y (0, 50) 50 40 (0, 35) (10, 30) 30 x+ 2x 2y 20 +y =7 0 =5 10 0 (0, 0) (25, 0) (70, 0) x 10 20 30 40 50 60 70
11. 11. Back to the Baker y 50 not feasible 40 (0, 35) (10, 30) 30 v= 20 0 u= 0 10 (0, 0) (25, 0) not feasible x 10 20 30 40 50 60 70
12. 12. How many basic feasible solutions are there? Out of the m + n variables, we choose n to set equal to zero, and solve for the rest. This can be done n+m (n + m)! = m m! n! ways. That’s a lot! The simplex method is a way to arrive at an optimal solution by traversing the vertices of the feasible set, in each step increasing the objective function by as much as possible.
13. 13. Outline Setup Illustrative Problem Slack Variables The Simplex Method, By Example The Initial Basic Feasible Solution Creating a New Tableau Recap of Steps Example
14. 14. We’ll work with the illustrative problem. We can start with the basic feasible solution x = 0, y = 0. Thus u = 50 and v = 70. This is our initial basic solution. We’ll start writing everything in a table (or tableau), so let’s also write the objective function with a right-hand side of zero. Thus −8x − 10y + z = 0. We put this all together, forming what is called the initial tableau: x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0
15. 15. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Is the solution u = 50, v = 70 (i.e., x = 0, y = 0 optimal?)
16. 16. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Is the solution u = 50, v = 70 (i.e., x = 0, y = 0 optimal?) No, increasing x or y would increase z.
17. 17. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Is the solution u = 50, v = 70 (i.e., x = 0, y = 0 optimal?) No, increasing x or y would increase z. Optimality Criterion If the objective row of a tableau has no negative entries in the columns labeled with variables, then the indicated solution is optimal and we can stop our computation.
18. 18. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Move from one basic solution to another One of the zero (nonbasic) variables becomes nonzero and one of nonzero (basic) variables becomes zero Do this as eﬃciently as possible
19. 19. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Move from one basic solution to another One of the zero (nonbasic) variables becomes nonzero and one of nonzero (basic) variables becomes zero Do this as eﬃciently as possible Which of x or y would you most like to increase?
20. 20. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0 Move from one basic solution to another One of the zero (nonbasic) variables becomes nonzero and one of nonzero (basic) variables becomes zero Do this as eﬃciently as possible Which of x or y would you most like to increase? An increase of 1 in y gives an increase of 10 in z. Let’s make y > 0. y enters the set of basic variables, so it’s called the entering variable for this step.
21. 21. How much can we increase y ? Well, since x is still zero, the equations of constraint can be written u = 50 − y v = 70 − 2y
22. 22. How much can we increase y ? Well, since x is still zero, the equations of constraint can be written u = 50 − y v = 70 − 2y We still need u ≥ 0 and v ≥ 0, so the most we can increase y is to 35. This is the smallest of the ratios 50 = 50 and 70 = 35. So 1 2 we’re going to increase y to 35. This will make v = 0. We call v the departing variable.
23. 23. How much can we increase y ? Well, since x is still zero, the equations of constraint can be written u = 50 − y v = 70 − 2y We still need u ≥ 0 and v ≥ 0, so the most we can increase y is to 35. This is the smallest of the ratios 50 = 50 and 70 = 35. So 1 2 we’re going to increase y to 35. This will make v = 0. We call v the departing variable. The new basic solution therefore has y = 35, v = 0, u = 15, and x = 0. The new value of the objective function is z = 10y = 350.
24. 24. Creating a New Tableau We are exchanging the basic variable v for y . This means the ob- jective row has to be replaced with one that has a zero in the y columns. We can do this by adding multiples of row 2. Since y is an entering variable, we might as well normalize row 2 to have a one. So we scale the second row to have a one in the y column. x y u v z value u 2 1 1 0 0 50 v 1 2 0 1 0 70 z −8 −10 0 0 1 0
25. 25. Creating a New Tableau Now we zero out the rest of this column by adding 10 times row 2 to row 3, and subtracting row 2 from row 1. x y u v z value 2 1 1 0 0 50 1/2 1 0 1/2 0 35 −8 −10 z 0 0 1 0
26. 26. Creating a New Tableau The new tableau. By looking at the columns, we see y and u are the basic variables. The value of the objective function has also changed. x y u vz value 1 −1/2 0 u 3/2 0 15 y 1/2 1 0 1/2 0 35 z −3 0 0 51 350
27. 27. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. xy u vz value 1− u 3/2 0 1/2 0 15 y 1/2 1 0 1/2 0 35 z −3 0 0 51 350
28. 28. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? xy u vz value 1− u 3/2 0 1/2 0 15 y 1/2 1 0 1/2 0 35 z −3 0 0 51 350
29. 29. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum of 15 35 the two ratios 3/2 = 10 and 1/2 = 70. xy u vz value 1− u 3/2 0 1/2 0 15 y 1/2 1 0 1/2 0 35 z −3 0 0 51 350
30. 30. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum 15 35 of the two ratios 3/2 = 10 and 1/2 = 70. So x in the entering variable and u is the departing variable. ↓x y u vz value ←u 1− 3/2 0 1/2 0 15 y 1/2 1 0 1/2 0 35 −3 z 0 0 51 350
31. 31. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum 15 35 of the two ratios 3/2 = 10 and 1/2 = 70. So x in the entering variable and u is the departing variable. We scale row one by 2/3 to make it one in the basic column. xy u vz value 1− u 3/2 0 1/2 0 15 y 1/2 1 0 1/2 0 35 z −3 0 0 51 350
32. 32. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum 15 35 of the two ratios 3/2 = 10 and 1/2 = 70. So x in the entering variable and u is the departing variable. We scale row one by 2/3 to make it one in the basic column. And we zero out the rest of the column by subtracting half of row 1 from row 2, and adding 3 times row 1 to row 3. x y u v z value −1/3 u 1 0 2/3 0 10 y 1/2 1 0 1/2 0 35 −3 0 0 51 350
33. 33. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum 15 35 of the two ratios 3/2 = 10 and 1/2 = 70. So x in the entering variable and u is the departing variable. We scale row one by 2/3 to make it one in the basic column. And we zero out the rest of the column by subtracting half of row 1 from row 2, and adding 3 times row 1 to row 3. x y u v z value −1/3 u 1 0 2/3 0 10 y 1/2 1 0 1/2 0 35 −3 0 0 51 350
34. 34. Rinse, Lather, Repeat The x column in the objective row has a negative entry, so increasing x will increase z. How much can we increase it? The minimum 15 35 of the two ratios 3/2 = 10 and 1/2 = 70. So x in the entering variable and u is the departing variable. We scale row one by 2/3 to make it one in the basic column. And we zero out the rest of the column by subtracting half of row 1 from row 2, and adding 3 times row 1 to row 3. x y u vz value 2/3 −1/3 0 x 1 0 10 1 −1/3 y 0 2/3 0 30 0 0 2 41 380
35. 35. x y u vz value 2/3 −1/3 0 x 1 0 10 1 −1/3 y 0 2/3 0 30 0 0 2 41 380 Now any increase in the decision variables or slack variables would result in a decrease of z. We are done!
36. 36. Outline Setup Illustrative Problem Slack Variables The Simplex Method, By Example The Initial Basic Feasible Solution Creating a New Tableau Recap of Steps Example
37. 37. Recap of Steps 1. Set up the initial tableau. 2. Apply the optimality test. If the objective row has no negative entries in the columns labeled with variables, then the indicated solution is optimal; we can stop. 3. Choose a pivotal column by determining the column with the most negative entry in the objective row. If there are several candidates for a pivotal column, choose any one. 4. Choose a pivotal row. Form the ratios of the entries above the objective row in the rightmost column by the corresponding entries of the pivotal column for those entries in the pivotal column which are positive. The pivotal row is the row for which the smallest of these ratios occurs. If there is a tie, choose any one of the qualifying rows. If none of the entries in the pivotal column above the objective row is positive, the problem has no ﬁnite optimum. We stop. 5. Perform pivotal elimination to construct a new tableau and return to Step 2.
38. 38. Outline Setup Illustrative Problem Slack Variables The Simplex Method, By Example The Initial Basic Feasible Solution Creating a New Tableau Recap of Steps Example
39. 39. Another Example Example Maximize z = 3x1 − x2 + 6x3 subject to the constraints 2x1 +4x2 + x3 ≤ 4 −2x1 +2x2 −3x3 ≥−4 2x1 + x2 − x3 ≤ 8 x1 ≥ 0 x2 ≥ 0 x3 ≥ 0. Negating row two puts this problem into standard form.
40. 40. Another Example Example Maximize z = 3x1 − x2 + 6x3 subject to the constraints 2x1 +4x2 + x3 ≤ 4 −2x1 +2x2 −3x3 ≥−4 2x1 + x2 − x3 ≤ 8 x1 ≥ 0 x2 ≥ 0 x3 ≥ 0. Negating row two puts this problem into standard form. Answer. x1 = 0, x2 = 4/7, x3 = 12/7, z = 687.
41. 41. We insert slack variables u1 , u2 , and u3 . The equations of constraint become ≤4 2x1 +4x2 + x3 +u1 2x1 −2x2 +3x3 ≤4 +u2 2x1 + x2 − x3 +u3 ≤8 with all variables nonnegative.