Exercise 4.8

Consider the linear programming problem of minimizing c′x subject to Ax = b,x ≥0. Let x∗ be an optimal solution, assumed to exist, and let p∗ be an optimal solution to the dual.

(a)
Let x~ be an optimal solution to the primal, when c is replaced by some c~. Show that (c~ −c)′ (x~ −x∗)≤ 0.
(b)
Let the cost vector be fixed at c, but suppose that we now change b to b~, and let x~ be a corresponding optimal solution to the primal. Prove that (p∗)′(b~ −b) ≤c′ (x~ −x∗).

Answers

(a)

Proof. On one hand, x∗ is an optimal solution with respect to the cost c, i.e., c′x∗≤c′x for all feasible x. In particular, c′x∗≤c′x~.
On the other hand, x~ is an optimal solution with respect to the cost c~, i.e., c~′x~ ≤c~′x for all feasible x. In particular, c~′x~ ≤c~′x∗. Combining both facts, we obtain

(c~ −c)′ (x~ −x∗) = c~′ (x~ −x∗)−c′ (x~ −x∗) = (c~′x~ −c~′x∗)− (c′x~ −c′x∗) ≤ 0. □
(b)

Proof. Let p∗ and denote the optimal solutions of the duals of the original and the modified primal problems respectively.
On one hand, by strong duality (Theorem 4.4), we have c′x∗ = (p∗)′b.
On the other hand, for an optimal solution p~∗ of the dual of the modified problem, by strong duality, we have c′x~ = (p~∗)′b~. In particular, p∗ is also feasible for the dual of the modified problem; thus, (p∗)′b~ ≤ (p~∗)′b~ = c′x~. We thus obtain:

(p∗)′(b~ −b) −c′ (x~ −x∗) = ((p∗)′b~ − (p∗)′b) −c′x~ + c′x∗ = ((p∗)′b~ −c′x~) − ((p∗)′b −c′x∗) ≤ 0. □
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2022-03-20 23:17
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