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Unformatted text preview: CO350 Linear Programming Chapter 6: The Simplex Method 10th June 2005 Chapter 6: The Simplex Method 1 Recap On Wednesday, we learned four common choice rules for entering variables: Largest coefficient rule (Dantzigs rule); Smallest subscript rule; Largest improvement rule; Steepest edge rule. We also learned that the simplex method solves the dual problem implicitly. Chapter 6: The Simplex Method 2 The Simplex Method and Duality (contd) Suppose at the end of the simplex method, we have an optimal solution x * determined by a basis B and the cor responding tableau ( T ) z X j N c j x j = v x i + X j N a ij x j = b i ( i B ) Recall that 1. The zrow is [ z c T x = 0 ] + [linear combination of Ax = b ] I.e., there is some y = [ y 1 , y 2 ,..., y m ] T such that z P j N c j x j = v is equivalent to z c T x + y T Ax = y T b 2. Comparing coefficients gives c i A T i y = 0 ( i B ) c j A T j y = c j ( j N ) which shows that y is optimal for the dual problem. Chapter 6: The Simplex Method 3 Finding dual optimal solution The abovementioned dual optimal solution y satisfies c i A T i y = 0 ( i B ) i.e. A T B y = c B [Note: c B to denotes the column matrix [ c i : i B ] .] Since B is a basis, A B is nonsingular, and so is A T B . Thus the system A T B y = c B has the unique solution y . Chapter 6: The Simplex Method 4 Example (Not in notes) Recall the example LP. max. z = 5 x 1 + 3 x 2 s.t. 2 x 1 + 3 x 2 + x 3 = 15 2 x 1 + x 2 + x 4 = 9 x 1 x 2 + x 5 = 3 x 1 , x 2 , x 3 , x 4 , x 5 We have applied the simplex method to get the optimal basis B = {...
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This note was uploaded on 06/11/2011 for the course C 350 taught by Professor Wolkowicz during the Fall '97 term at Waterloo.
 Fall '97
 Wolkowicz

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