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Material Type: Assignment; Class: Linear Programming; Subject: Operations Research; University: North Carolina State University; Term: Spring 2007;
Typology: Assignments
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Due in class on Thursday, March 22, 2007. No late homeworks will be accepted without prior instructor approval. All problems are from Chv´atal unless otherwise specified. Please read Chapters 7 and 10 in Chv´atal before beginning the assignment.
max Z = −x 1 − x 2 s.t. − 2 x 1 − x 2 ≤ 4 − 2 x 1 + 4x 2 ≤ − 8 −x 1 + 3x 2 ≤ − 7 x 1 , x 2 ≥ 0
(a) Solve the LP using the two phase simplex method. (b) Solve the LP using the dual simplex method, i.e., by applying the simplex method on dual dictionaries as discussed in class. (c) Which method is more efficient with regard to the number of iterations? For the two phase method, you should count the number of iterations in both phases of the simplex method. (d) Solve the LP using the revised dual simplex method (see Box 10.1 on page 155 of Chv´atal). Do only the first two iterations by hand. You can complete the rest of the iterations using the dual simplex code that I have posted on the course webpage. Verify that your results in the various iterations are identical to those in part (b).
x 1 = 2 + x 4 − 12 x 6 − 15 x 7 + x 8 x 2 = 3 − 2 x 4 − x 5 + x 6 − 12 x 8 x 3 = 1 + x 4 + 2x 5 − 5 x 6 + 103 x 7 − 2 x 8 z = θ − x 4 − 2 x 6 − 101 x 7 − 2 x 8. of an LP (maximization problem and all ≤ constraints), where x 6 , x 7 , and x 8 are the slack variables in the problem. (a) Find the optimal objective value θ. (b) Would the solution be altered if a new activity x 9 with coefficients (6, 0 , −3)T^ in the constraints and a cost coefficient 7 were added to the problem? (c) How large can b 1 (rhs of the first constraint) be made without violating feasibility? (d) Suppose we add the constraint 2x 1 − x 2 + 2x 3 ≤ 2 to the problem. Is the earlier solution still optimal? If not, find a new optimal solution.
(a) Sketch the feasible region of (1). To do this, sketch the feasible region of the linear program (LP) obtained by dropping the integrality restrictions on x 1 and x 2 , and then label all the integer points (x 1 , x 2 integer) within the LP feasible region. How many integer points do you find? What is the optimal solution to (1)? (b) We will develop a step by step technique due to Ralph Gomory in the 1950s for solving (1) as follows: First solve an LP relaxation of (1). This LP is obtained simply by dropping the integrality restrictions on x 1 and x 2 in (1). Let x 3 and x 4 be the slack variables corresponding to the first two functional constraints in the LP. Use the primal simplex method with dictionaries to solve the LP relaxation to optimality. You will need two iterations and your optimal dictionary will be
x 1 = 1 −
x 3 +
x 4
x 2 =
x 3 −
x 4
x 3 −
x 4.
problem. This will give you the dictionary
x 1 = 1 − x 5 +
x 6
x 2 = 1 − x 5 + 0x 6
x 3 = 1 + 5x 5 −
x 6
x 4 = 1 − x 5 +
x 6
Z = 1 − x 5 + 0x 6 ,
which is optimal for (1) since x 1 and x 2 are finally integer! This gives the optimal solution x∗^ = (1, 1) for (1).
(e) Plot the two cutting planes x 2 ≤ 1 and x 1 ≥ x 2 on the plot from part (a). Also, trace the path taken by the algorithm to find the optimal solution to (1) here.