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Linear Algebra, Theory And Applications, 2012a

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152 LINEAR PROGRAMMING<br />

Example 6.4.3 Maximize x 1 − x 2 +2x 3 subject to the constraints, 2x 1 + x 2 − x 3 ≥ 3,x 1 +<br />

x 2 + x 3 ≥ 2,x 1 + x 2 + x 3 ≤ 7 and x ≥ 0.<br />

From (6.20) you can immediately assemble an initial simplex tableau. You begin with<br />

the first 6 columns and top 3 rows in (6.20). Then add in the column and row for z. This<br />

yields<br />

⎛<br />

⎞<br />

⎜<br />

⎝<br />

2<br />

1<br />

3<br />

0 − 1 3<br />

− 1 5<br />

3<br />

0 0<br />

3<br />

1<br />

1<br />

0<br />

3<br />

1<br />

3<br />

− 2 1<br />

3<br />

0 0<br />

3<br />

0 0 0 0 1 1 0 5<br />

−1 1 −2 0 0 0 1 0<br />

and you first do row operations to make the first and third columns simple columns. Thus<br />

the next simplex tableau is<br />

⎛<br />

⎞<br />

2<br />

1<br />

3<br />

0 − 1 3<br />

− 1 5<br />

3<br />

0 0<br />

3<br />

1 1<br />

⎜ 0<br />

3<br />

1<br />

3<br />

− 2 1<br />

3<br />

0 0 3 ⎟<br />

⎝ 0 0 0 0 1 1 0 5 ⎠<br />

7 1<br />

0<br />

3<br />

0<br />

3<br />

− 5 7<br />

3<br />

0 1<br />

3<br />

You are trying to get rid of negative entries in the bottom left row. There is only one, the<br />

−5/3. The pivot is the 1. The next simplex tableau is then<br />

⎛<br />

⎞<br />

2<br />

1<br />

3<br />

0 − 1 1 10<br />

3<br />

0<br />

3<br />

0<br />

3<br />

1 1 2 11<br />

⎜ 0<br />

3<br />

1<br />

3<br />

0<br />

3<br />

0 3 ⎟<br />

⎝ 0 0 0 0 1 1 0 5 ⎠<br />

7 1 5 32<br />

0<br />

3<br />

0<br />

3<br />

0<br />

3<br />

1<br />

3<br />

and so the maximum value of z is 32/3 and it occurs when x 1 =10/3,x 2 = 0 and x 3 =11/3.<br />

6.5 Duality<br />

You can solve minimization problems by solving maximization problems. You can also go<br />

the other direction and solve maximization problems by minimization problems. Sometimes<br />

this makes things much easier. To be more specific, the two problems to be considered are<br />

A.) Minimize z = cx subject to x ≥ 0 and Ax ≥ b and<br />

B.) Maximize w = yb such that y ≥ 0 and yA ≤ c,<br />

(<br />

equivalently A T y T ≥ c T and w = b T y T ) .<br />

⎟<br />

⎠<br />

In these problems it is assumed A is an m × p matrix.<br />

I will show how a solution of the first yields a solution of the second and then show how<br />

a solution of the second yields a solution of the first. The problems, A.) andB.) are called<br />

dual problems.<br />

Lemma 6.5.1 Let x be a solution of the inequalities of A.) and let y be a solution of the<br />

inequalities of B.). Then<br />

cx ≥ yb.<br />

and if equality holds in the above, then x is the solution to A.) and y is a solution to B.).<br />

Proof: This follows immediately. Since c ≥ yA, cx ≥ yAx ≥ yb.<br />

It follows from this lemma that if y satisfies the inequalities of B.) andx satisfies the<br />

inequalities of A.) then if equality holds in the above lemma, it must be that x is a solution<br />

of A.) andy is a solution of B.).

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