# 1 Polyhedra and Linear Programming

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1 CS 598CSC: Combinatorial Optimization Lecture date: January 21, 2009 Instructor: Chandra Chekuri Scribe: Sungjin Im 1 Polyhedra and Linear Programming In this lecture, we will cover some basic material on the structure of polyhedra and linear programming. There is too abundant material on this topic to be covered in a few classes, so pointers will be given for further reading. For algorithmic and computational purposes one needs to work with rational polyhedra. Many basic results, however, are valid for both real and rational polyhedra. Therefore, to expedite our exploration, we will not make a distinction unless necessary. 1.1 Basics Definition 1. Let x 1, x 2,..., x m be points in R n. Let x = m i=1 λ ix i, where each λ i R, 1 i m is a scalar. Then, x is said to be a(n) 1. Linear combination (of x i, 1 i m) for arbitrary scalars λ i. 2. Affine combination if i λ i = Conical combination if λ i Convex combination if λ i = 1 and λ i 0 (affine and also canonical). In the following definitions and propositions, unless otherwise stated, it will be assumed that x 1, x 2,..., x m are points in R n and λ 1, λ 2,..., λ m are scalars in R. Definition 2. x 1, x 2,..., x m are said to be linearly independent if m i=1 λ ix i = 0 i [m] λ i = 0. Definition 3. x 1, x 2,..., x m are said to be affinely independent if the vectors (x i x 1 ), i = 2,..., m are linearly independent, or equivalently if m i=1 λ ix i = 0 and m i=1 λ i = 0 i [m] λ i = 0. The following proposition is easy to check and the proof is left as an exercise to the reader. Proposition 4. x 1, x 2,..., x m are affinely independent if and only if the vectors ( x i 1 ), i = 1, 2,..., m, are linearly independent in R m+1. A set X R n is said to be a(n) subspace [affine set, cone set, convex set] if it is closed under linear [affine, conical, convex] combinations. Note that an affine set is a translation of a subspace. Given X R n, we let Span(X), Aff(X), Cone(X), and Convex(X) denote the closures of X under linear, affine, conical, and convex combinations, respectively. To get an intuitive feel of the above definitions, see Figure 1. Definition 5. Given a convex set X R n, the affine dimension of X is the maximum number of affinely independent points in X.

2 Figure 1: The subspace, cone set, affine set, and convex set of x 1, x 2 (from left to right). Note that the subspace is R 2 and the cone set includes all points inside and on the two arrows. 1.2 Polyhedra, Polytopes, and Cones Definition 6 (Hyperplane, Halfspace). A hyperplane in R n is the set of all points x R n that satisfy a x = b for some a R n and b R. A halfspace is the set of all points x such that a x b for some a R n and b R. Definition 7 (Polyhedron). A Polyhedron in R n is the intersection of finitely many halfspaces. It can be equivalently defined to be the set {x Ax b} for a matrix A R m n and a vector b R m 1. Definition 8 (Polyhedral cone). A polyhedral cone is R n the intersection of finitely many halfspaces that contain the origin, i.e. {x Ax 0} for a matrix A R m n. Definition 9 (Polyotpe). A polytope is a bounded polyhedron. Note that a polyhedron is a convex and closed set. It would be illuminating to classify a polyhedron into the following four categories depending on how it looks. 1. Empty set (when the system Ax b is infeasible.) 2. Polytope (when the polyhedron is bounded.) 3. Cone 4. (Combination of) Cone and Polytope Figure 2: Examples of polyhedrons, left to right: Empty, Polytope, Cone, Combination of cone and polytope. What combination of cone and polytope means will become clear soon in Theorem 12. For the examples, the reader is referred to Figure 2. In 2-D, a cone can have only two extreme rays, while in 3-D there is no bound on the number of extreme rays it can have.

3 For the most of part, we will be largely concerned with polytopes, but we need to have a better understanding of polyhedra first. Although it is geometrically obvious that a polytope is the convex hull of its vertices, the proof is quite non-trivial. We will state the following three theorems without proof. Theorem 10. A bounded polyhedron is the convex hull of a finite set of points. Theorem 11. A polyhedral cone is generated by a finite set of vectors. That is, for any A R m n, there exists a finite set X such that {x = i λ ix i x i X, λ i 0} = {x Ax 0}. Theorem 12. A polyhedron {x Ax b} can be written as the Minkowski sum of a polytope Q and a cone C, i.e. P = Q + C = {x + y x Q, y C}. One can (geometrically) obtain the Minkowski sum of a polytope Q and a cone C by sweeping the origin of the cone C over the polytope Q. If the polyhedron P is pointed (has at least one vertex ), the decomposition is, in fact, modulo scaling factor unique. Further the cone C above is {x Ax 0}, or equivalently the set of unbounded directions in P. The cone C is called the characteristic cone or the recession cone of P. Many facts about polyhedra and linear programming rely on (in addition to convexity) variants of Farkas lemma that characterizes when a system of linear inequalities do not have solution. The simplest proof for one variant is via Fourier-Motzkin elimination that is independently interesting and related to the standard Gauss-Jordan elimination for solving system of linear equations Fourier-Motzkin Elimination Let P = {x Ax b} R n be a polyhedron. For k in [n], we let P k = {(x 1,.., x k 1, x k+1,..., x n ) (x 1, x 2,..., x n ) P } be the projection of P along the x k -axis. Theorem 13. P k is a polyhedron. Proof. We derive a set of inequalities that describe P k. We do this by considering the inequalities in Ax b and eliminating the variables x k as follows. Partition the inequalities in Ax b into three sets: S + = {i [m] a ik > 0}, S = {i [m] a ik < 0}, and S 0 = {i [m] a ik = 0}. Define a new set of inequalities consisting of S 0 and one new inequality for each pair (i, l) in S + S : n n a ik ( a lj x j ) a lk ( a ij x j ) a ik b l a lk b i. j=1 j=1 Note that the combined inequality does not have x k. We now have a total of S 0 + S + S new inequalities. Let P = {x R n 1 A x b } where A x b is the new system of inequalities in variables x 1, x 2,..., x k 1, x k+1,..., x n. We prove the theorem by showing that P k = P.

4 We first show the easier direction: P k P. Consider any point z P k. By definition of P k, there exists x P such that Ax b and x s projection along x k -axis is z. It is easy to see that z satisfies the new system since the new one was obtained in a way oblivious to x k, the real value of x s k th coordinate. We now show that P P k. Without loss of generality, assume k = 1. Consider any x = (x 2, x 3,..., x n ) P. We want to show that there exists x 1 R such that Ax b, where x = (x 1, x 2,..., x n ). For simple notation, define C i = b i n j=2 a ijx j for i [m]. Note that Ax b can be rewritten as a i1 x 1 C i, i [m]. (1) Observe that x satisfies all inequalities consisting of S 0, since the new system as well includes those constraints. Thus we can refine our goal to show It is easy to observe that this is equivalent to x 1 s.t. a i1 x 1 C i, i S + S. max l S C l a l1 x 1 min i S + C i a i1. C l a l1 C i a i1, (i, l) S + S 0 a i1 C l a l1 C i, (i, l) S + S A x b And we know that A x b since x P, completing the proof. From Fourier-Motzkin elimination we get an easy proof of one variant of Farkas lemma. Theorem 14 (Theorem of Alternatives). Let A R m n and b R m. For the system Ax b, exactly one of the following two alternatives hold: The system is feasible. There exists y R m such that y 0, ya = 0 and yb < 0. What the theorem says is that if the system of inequalities Ax b is infeasible then there is a proof (certificate) of this which can be obtained by taking non-linear combination of the inequalities (given by y 0) to derive a contradiction of the following form: 0 = ya yb < 0. Proof of [Theorem 14] Suppose that there exists a vector y 0 s.t. y A = 0 and y b < 0 and a vector x such that Ax b. Then it easily follows that 0 y Ax y b, since y 0, which is a contradiction to the fact that y b < 0. Conversely, suppose Ax b is infeasible. Let P = {x Ax b}. We eliminate variables x 1, x 2,..., x n (we can choose any arbitrary order) to obtain polyhedra P = Q 0, Q 1, Q 2,..., Q n 1, Q n. Note that Q i+1 is non-empty iff Q i is, and that Q n 1 has only one variable and Q n has none. Note by the Fourier-Motzkin elimination procedure the inequalities describing Q i are non-negative combination of the inequalities of P ; this can be formally shown via induction. Thus, Q n is empty iff we have derived an inequality of the form 0 C for some C < 0 at some point in the process. That inequality gives the desired y 0. Two variant of Farkas lemma that are useful can be derived from the theorem of alternatives. Theorem 15. Ax = b, x 0 has no solution iff y R m s.t. A T y 0 and b T y < 0.

5 The above theorem has a nice geometric interpretation. Let α 1, α 2,..., α n be the columns of A viewed as vectors in R m. Then Ax = b, x 0 has a solution if and only if b is in the cone generated by α 1, α 2,..., α n ; here the combination is given by x 0. So b is either in the Cone(α 1, α 2,..., α n ) or there is a hyperplane separating b from α 1, α 2,..., α n. In fact the theorem can be strengthened to show that the hyperplane can be chosen to be one that spans t 1 linearly independent vectors in α 1, α 2,..., α n, where t = rank(α 1, α 2,..., α n, b). Proof of [Theorem 15] We can rewrites Ax = b, x 0 as A A x I Hence by the Theorem of Alternatives, Ax = b, x 0 is not feasible if and only if there exists a row vector y = [ u v w ], where u, v are row vectors in R m and w is a row vector in R n such that b b 0 u, v, w 0 ua va w = 0 ub vb < 0 Let y = u v. Note that y R m is now not necessarily positive. From the second and third inequalities, we can easily obtain A T y 0 and b T y < 0. Another variant of Farkas lemma is as follows and the proof is left as an exercise. Theorem 16. Ax b, x 0 has a solution iff yb 0 for each row vector y 0 with ya 0. Another interesting and useful theorem is Carathéodory s Theorem Theorem 17 (Carathéodory). Let x Convexhull(X) for a finite set X of points in R n. Then x Convexhull(X ) for some X X such that vectors in X are affinely independent. In particular, X n + 1. A conic variant of Carathéodory s Theorem is as follows. Theorem 18. Let x Cone(X) where X = {x 1, x 2,..., x m }, x i R n. Then x Cone(X ) for some X X where vectors in X are linearly independent. In particular, X n.

6 Proof. Since x Cone(X), x = i λ ix i for some λ i 0. Choose a combination with mininum support, i.e. the smallest number of non-zero λ i values. Let X = {λ i x i λ i > 0} and I = {i λ i > 0}. If vectors in X are linearly independent, we are done. Otherwise, α i, i I s.t. i I α iλ i x i = 0. By scaling we can assume that i I, α i 1, and j I s.t. α j = 1. Then, x = λ i x i = λ i x i α i λ i x i = λ i (1 α i )x i = λ ix i (λ i 0). i I i I i I i I i I\{j} This contradicts the fact that we chose the conical combination for x with the least support. One can derive the affine version of Carathéodory s Theorem from the conical version, and the proof is left as an exercise. 1.3 Linear Programming Linear programming is an optimization problem of the following form. max c x Ax b (Primal-LP) The above is one of the standard forms. In other words, we wish to maximize a linear objective function over a polyhedron. Given an LP, there are three possibilities: 1. The polyhedron is infeasible. 2. The objective function can be made arbitrarily large in which case we can say it is unbounded. 3. There is a finite optimum value in which case we say it is bounded. Each linear program has its associated dual linear program. The LP we refer to by dual depends on the starting LP, which is called as the primal LP; in fact the dual of dual LP is exactly the same as the primal LP. Let us say that the following LP is the primal LP here. max c x Ax b We can derive the dual by thinking about how we can obtain an upper bound on the optimal value for the primal LP. Given the system Ax b, any inequality obtained by non-negative combination of the inequalities in Ax b is a valid inequality for the system. We can represent a non-negative combination by a m 1 row vector y 0. Thus yax yb is a valid inequality for y 0. Take any vector y 0 s.t. y A = c. Then such a vector gives us an upperbound on the LP value since y Ax = cx y b is a valid inequality. Therefore one can obtain an upperbound by minimizing over all y 0 s.t. y A = c. Therefore the objective function of the primal LP is upperbounded by the optimum value of min yb ya = c y 0 (Dual-LP) The above derivation of the Dual LP immediately implies the Weak Duality Theorem.

7 Theorem 19 (Weak Duality). If x and y are feasible solutions to Primal-LP and Dual-LP then cx y b. Corollary 20. If the primal-lp is unbounded then the Dual-LP is infeasible. Exercise: Prove that the dual of the Dual-LP is the Primal-LP. The main result in the theory of linear programming is the following Strong Duality Theorem which is essentially a min-max result. Theorem 21 (Strong Duality). If Primal-LP and Dual-LP have feasible solutions, then there exist feasible solutions x and y such that cx = y b. Proof. Note that by weak duality we have that cx y b for any feasible pair of x and y. Thus to show the existence of x and y it suffices to show that the system of inequalities below has a feasible solution whenever the two LPs are feasible. cx yb Ax b ya = c y 0 We rewrite this as Ax b ya c ya c y 0 cx + yb 0 and apply the Theorem of Alternatives. Note that we have inequalities [ in n ] + m variables corresponding to x the x and y variables. By expressing those variables as a vector z = y T, we have A 0 0 A T 0 A T 0 I c b T [ x y ] If the above system does not have a solution then there exists a vector [ s t t u v ] 0, where s is a m 1 row vector, t, t are n 1 row vectors, u is a m 1 row vector and v is a scalar such that b c T c T 0 0 sa v c = 0 ta T t A T u + v b = 0 sb + t c t c < 0

8 We replace t t by w and note that now w is not necessarily positive. Hence we obtain that if the strong duality does not hold then there exist vectors s, u R m, w R n, v R such that We consider two cases. Case 1: v = 0. (note that v is a scalar.) In this case, the above system simplifies to s, u, v 0 sa v c = 0 wa T u + v b = 0 sb + wc T < 0 s 0 sa = 0 wa T = 0 sb + wc T < 0 Since we have y A = c and sa = 0, y + αs is a feasible solution for the dual for any scalar α 0. Similarly knowing that Ax b and Aw T = 0 (from wa T = 0), it follows that x αw T is feasible for the primal for any scalar α 0. Applying the Weak Duality Theorem, we have that α 0, c(x αw T ) (y + αs) b cx y b α(s b + c w T ) However, the LHS is fixed while the RHS can be made arbitrarily small because s b + c w T < 0 and α can be chosen arbitrarily large. Case 2: v > 0. Let s = 1 v (s), w = 1 v (w), and u = 1 v (u). Then, we have s, u 0 s A = c w A T u = b A(w ) T = b u b [Since u 0.] s b + w c T < 0 From the inequalities above, we observe that s is dual feasible and w is primal feasible. Thus by the Weak Duality, we have w c T s b, contradicting that s b + w c T < 0. Finally, we make a remark on where the contradiction really comes from for each case. For the first case where v = 0, note that the inequality cx + yb 0, which forces the optimal values for the primal and the dual meet each other, was never used. In other words, we got a contradiction because there do not exist feasible solutions satisfying both the primal and the dual. On the other hand, for the second case v > 0, we had feasible solutions for both LPs, but obtained a contradiction essentially from the assumption that the two optimal values are different. Complementary Slackness is a very useful consequence of Strong Duality. Theorem 22 (Complementary Slackness). Let x, y be feasible solutions to the primal and dual LPs. Then x, y are optimal solutions if and only if i [m], either y i = 0 or a ix = b i.

9 Proof. Let α 1, α 2,...α m be the row vectors of A. Suppose that the given condition is satisfied. Then we have y Ax = m i=1 y i (α ix ) = m i=1 y i b i = y b. Also we know that cx = y Ax since y is a feasible solution for the dual. Thus we have cx = y b, and by the Weak Duality, we conclude that x, y are optimal. Conversely, suppose that x and y both are optimal. Then by the Strong Duality and because x, y are feasible, we have that y b = cx = y Ax y b. Thus we obtain the equality y Ax = y b, that is m i=1 y i (α ix ) = m i=1 y i b i. This equality forces the desired condition, since α i x b i, yi 0 because x and y are feasible solutions. References [1] L. Schrijver. Theory of Linear and Integer Programming (Paperback). Wiley, Chapters 7, 8, [2] J. Lee. A First Course in Combinatorial Optimization. Cambridge University Press, [3] M. Goemans. Lecture notes on linear programming and polyhedral combinatorics. goemans/18433s09/polyhedral.pdf, 2009.

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