# Introduction to Mathematical Programming IE406. Lecture 10. Dr. Ted Ralphs

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1 Introduction to Mathematical Programming IE406 Lecture 10 Dr. Ted Ralphs

2 IE406 Lecture 10 1 Reading for This Lecture Bertsimas

3 IE406 Lecture 10 2 Duality Theory: Motivation Consider the following minimization problem min x 2 + y 2 s.t. x + y = 1 How could we solve this problem? Idea: Consider the function L(x, y, p) = x 2 + y 2 + p(1 x y) What can we do with this?

4 IE406 Lecture 10 3 Lagrange Multipliers The idea is not to strictly enforce the constraints. We associate a Lagrange multiplier, or price, with each constraint. Then we allow the constraint to be violated for a price. Consider an LP in standard form. Using Lagrange multipliers, we can formulate an alternative LP: min c x + p (b Ax) s.t. x 0 How does the optimal solution of this compare to the original optimum?

5 IE406 Lecture 10 4 Lagrange Multipliers Because we haven t changed the cost of feasible solutions to the original problem, this new problem gives a lower bound. g(p) = min x 0 [ c x + p (b Ax) ] c x + p (b Ax ) = c x Since each value of p gives a lower bound, we consider maximizing g(p). Think of this as finding the best lower bound. This is known as the dual problem.

6 IE406 Lecture 10 5 Simplifying In linear programming, we can obtain an explicit form for the dual. g(p) = min x 0 [ c x + p (b Ax) ] = p b + min x 0 (c p A)x Note that min x 0 (c p A)x = { 0, if c p A 0,, otherwise, Hence, we can show that the dual is equivalent to max p b s.t. p A c

7 IE406 Lecture 10 6 Inequality Form Suppose our feasible region is P = {x R n Ax b, x 0}. We can add slack variables and convert to standard form with constraints [ ] x [A I] = b s This leads to dual constraints p [A I] [ c 0 ] Hence, we get the dual max p b s.t. p A c p 0

8 IE406 Lecture 10 7 From the Primal to the Dual We can dualize general LPs as follows PRIMAL minimize maximize DUAL b i 0 constraints b i 0 variables = b i free 0 c j variables 0 c j constraints free = c j

9 IE406 Lecture 10 8 Properties of the Dual All equivalent forms of the primal give equivalent forms of the dual. The dual of the dual is the primal. Weak Duality: If x is a feasible solution to the primal and p is a feasible solution to the dual, then p b c x Corollaries: If the optimal cost of the primal is, then the dual is infeasible. If the optimal cost of the dual is +, then the primal is infeasible. If x is a feasible primal solution and p is a feasible dual solution such that c x = p b, then both x and p are optimal.

10 IE406 Lecture 10 9 Relationship of the Primal and the Dual The following are the possible relationships between the primal and the dual: Finite Optimum Unbounded Infeasible Finite Optimum Possible Impossible Impossible Unbounded Impossible Impossible Possible Infeasible Impossible Possible Possible

11 IE406 Lecture Strong Duality Proposition 1. (Strong Duality) If a linear programming problem has an optimal solution, so does its dual, and the respective optimal costs are equal. Proof:

12 IE406 Lecture More About the Dual When we interpret the quantity c B B 1 as the vector of dual prices, the reduced costs are then the slack in the constraints of the dual. The condition that all the reduced costs be nonnegative is then equivalent to dual feasibility. Hence, the simplex algorithm can be interpreted as maintaining primal feasibility while trying to achieve dual feasibility. We will shortly see an alternative algorithm which maintains dual feasibility while trying to achieve primal feasibility.

13 IE406 Lecture Complementary Slackness Proposition 2. If x and p are feasible primal and dual solutions to a general linear program with constraint matrix A R m n and right-hand side vector b R m, then x and p are optimal if and only if p (Ax b) = 0, (c p A)x = 0. Proof:

14 IE406 Lecture Optimality Without Simplex Let s consider an LP in standard form. We have now shown that the optimality conditions for (nondegenerate) x are 1. Ax = b (primal feasibility) 2. x 0 (primal feasibility) 3. x i = 0 if p a i c i (complementary slackness) 4. p A c (dual feasibility) In standard form, the complementary slackness condition is simply x c = 0. This condition is always satisfied during the simplex algorithm, since the reduced costs of the basic variables are zero.

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