3. Linear Programming and Polyhedral Combinatorics

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1 Massachusetts Institute of Technology Handout : Combinatorial Optimization February 20th, 2009 Michel X. Goemans 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the introductory lectures on linear programming and polyhedral combinatorics. Definition 3.1 A halfspace in R n is a set of the form {x R n : a T x b} for some vector a R n and b R. Definition 3.2 A polyhedron is the intersection of finitely many halfspaces: P = {x R n : Ax b}. Definition 3.3 A polytope is a bounded polyhedron. Definition 3.4 If P is a polyhedron in R n, the proection P k R n 1 of P is defined as {y = (x 1, x 2,, x k 1, x k+1,, x n ) : x P for some x k }. This is a special case of a proection onto a linear space (here, we consider only coordinate proection). By repeatedly proecting, we can eliminate any subset of coordinates. We claim that P k is also a polyhedron and this can be proved by giving an explicit description of P k in terms of linear inequalities. For this purpose, one uses Fourier-Motzkin elimination. Let P = {x : Ax b} and let S + = {i : a ik > 0}, S = {i : a ik < 0}, S 0 = {i : a ik = 0}. Clearly, any element in P k must satisfy the inequality a T i x b i for all i S 0 (these inequalities do not involve x k ). Similarly, we can take a linear combination of an inequality in S + and one in S to eliminate the coefficient of x k. This shows that the inequalities: ( ) ( ) a ik a l x a lk a i x a ik b l a lk b i (1) for i S + and l S are satisfied by all elements of P k. Conversely, for any vector (x 1, x 2,, x k 1, x k+1,, x n ) satisfying (1) for all i S + and l S and also a T i x b i for all i S 0 (2) we can find a value of x k such that the resulting x belongs to P (by looking at the bounds on x k that each constraint imposes, and showing that the largest lower bound is smaller than the smallest upper bound). This shows that P k is described by (1) and (2), and therefore is a polyhedron.

2 3. Linear Programming and Polyhedral Combinatorics February 20th, Definition 3.5 Given points a (1), a (2),, a (k) R n, a linear combination is i λ ia (i) where λ i R for all i, an affine combination is i λ ia (i) where λ i R and i λ i = 1, a conical combination is i λ ia (i) where λ i 0 for all i, a convex combination is i λ ia (i) where λ i 0 for all i and i λ i = 1. The set of all linear combinations of elements of S is called the linear hull of S and denoted by lin(s). Similarly, by replacing linear by affine, conical or convex, we define the affine hull, aff(s), the conic hull, cone(s) and the convex hull, conv(s). We can give an equivalent definition of a polytope. Definition 3.6 A polytope is the convex hull of a finite set of points. The fact that Definition 3.6 implies Definition 3.3 can be shown by using Fourier-Motzkin elimination repeatedly on x λ k a (k) = 0 k λ k = 1 k λ k 0 to eliminate all variables λ k and keep only the variables x. The converse will be discussed later in these notes. 3.1 Solvability of System of Inequalities In linear algebra, we saw that, for A R m n, b R m, Ax = b has no solution x R n if and only if there exists a y R m with A T y = 0 and b T y 0 (in notation/terminology, this is equivalent to saying that the column space C(A) is orthogonal to the left null space N(A T )). One can state a similar Theorem of the Alternatives for systems of linear inequalities. Theorem 3.1 (Theorem of the Alternatives) Ax b has no solution x R n if and only if there exists y R m such that y 0, A T y = 0 and b T y < 0. One can easily show that both systems indeed cannot have a solution since otherwise 0 > b T y = y T b y T Ax = 0 T x = 0. For the other direction, one takes the insolvable system Ax b and use Fourier-Motzkin elimination repeatedly to eliminate all variables and thus obtain an inequality like 0 T x c where c < 0. In the process one has derived a vector y with the desired properties (as Fourier-Motzkin only performs nonnegative combinations of linear inequalities). Another version of the above theorem is Farkas lemma:

3 3. Linear Programming and Polyhedral Combinatorics February 20th, Lemma 3.2 Ax = b, x 0 has no solution if and only if there exists y with A T y 0 and b T y < 0. Exercise 3-1. Prove Farkas lemma from the Theorem of the Alternatives. 3.2 Linear Programming Basics A linear program (LP) is the problem of minimizing or maximizing a linear function over a polyhedron: Max subect to: c T x (P ) Ax b, where A R m n, b R m, c R n and the variables x are in R n. Any x satisfying Ax b is said to be feasible. If no x satisfies Ax b, we say that the linear program is infeasible, and its optimum value is (as we are maximizing over an empty set). If the obective function value of the linear program can be made arbitrarily large, we say that the linear program is unbounded and its optimum value is + ; otherwise it is bounded. If it is neither infeasible, not unbounded, then its optimum value is finite. Other equivalent forms involve equalities as well, or nonnegative constraints x 0. One version that is often considered when discussing algorithms for linear programming (especially the simplex algorithm) is min{c T x : Ax = b, x 0}. Another linear program, dual to (P ), plays a crucial role: (D) Min subect to: b T y A T y = c y 0. (D) is the dual and (P ) is the primal. The terminology for the dual is similar. If (D) has no feasible solution, it is said to be infeasible and its optimum value is + (as we are minimizing over an empty set). If (D) is unbounded (i.e. its value can be made arbitrarily negative) then its optimum value is. The primal and dual spaces should not be confused. If A is m n then we have n primal variables and m dual variables. Weak duality is clear: For any feasible solutions x and y to (P ) and (D), we have that c T x b T y. Indeed, c T x = y T Ax b T y. The dual was precisely built to get an upper bound on the value of any primal solution. For example, to get the inequality y T Ax b T y, we need that y 0 since we know that Ax b. In particular, weak duality implies that if the primal is unbounded then the dual must be infeasible. Strong duality is the most important result in linear programming; it says that we can prove the optimality of a primal solution x by exhibiting an optimum dual solution y.

4 3. Linear Programming and Polyhedral Combinatorics February 20th, Theorem 3.3 (Strong Duality) Assume that (P ) and (D) are feasible, and let z be the optimum value of the primal and w the optimum value of the dual. Then z = w. The proof of strong duality is obtained by writing a big system of inequalities in x and y which says that (i) x is primal feasible, (ii) y is dual feasible and (iii) c T x b T y. Then use the Theorem of the Alternatives to show that the infeasibility of this system of inequalities would contradict the feasibility of either (P ) or (D). Proof: Let x be a feasible solution to the primal, and y be a feasible solution to the dual. The proof is by contradiction. Because of weak duality, this means that there are no solution x R n and y R m such that Ax b A T y = c Iy 0 c T x +b T y 0 By a variant of the Theorem of the Alternatives or Farkas lemma (for the case when we have a combination of inequalities and equalities), we derive that there must exist s R m, t R n, u R m, v R such that: We distinguish two cases. s 0 u 0 v 0 A T s vc = 0 At u + vb = 0 b T s + c T t < 0. Case 1: v = 0. Then s satisfies s 0 and A T s = 0. This means that, for any α 0, y + αs is feasible for the dual. Similarly, At = u 0 and therefore, for any α 0, we have that x αt is primal feasible. By weak duality, this means that, for any α 0, we have or c T (x αt) b T (y + αs) c T x b T y α(b T s + c T t). The right-hand-side tend to as α tends to, and this is a contradiction as the lefthand-side is fixed.

5 3. Linear Programming and Polyhedral Combinatorics February 20th, Case 2: v > 0. By dividing throughout by v (and renaming all the variables), we get that there exists s 0, u 0 with A T s = c At u = b b T s + c T t < 0. This means that s is dual feasible and t is primal feasible, and therefore by weak duality c T ( t) b T s contradicting b T s + c T t < 0. Exercise 3-2. Show that the dual of the dual is the primal. Exercise 3-3. Show that we only need either the primal or the dual to be feasible for strong duality to hold. More precisely, if the primal is feasible but the dual is infeasible, prove that the primal will be unbounded, implying that z = w = +. Looking at c T x = y T Ax b T y, we observe that to get equality between c T x and b T y, we need complementary slackness: Theorem 3.4 (Complementary Slackness) If x is feasible in (P ) and y is feasible in (D) then x is optimum in (P ) and y is optimum in (D) if and only if for all i either y i = 0 or a ix = b i (or both). Linear programs can be solved using the simplex method; this is not going to be explained in these notes. No variant of the simplex method is known to provably run in polynomial time, but there are other polynomial-time algorithms for linear programming, namely the ellipsoid algorithm and the class of interior-point algorithms. 3.3 Faces of Polyhedra Definition 3.7 {a (i) R n : i K} are linearly independent if i λ ia (i) = 0 implies that λ i = 0 for all i K. Definition 3.8 {a (i) R n : i K} are affinely independent if i λ ia (i) = 0 and i λ i = 0 together imply that λ i = 0 for all i K. Observe that {a (i) R n : i K} are affinely independent if and only if {[ ] } a (i) R n+1 : i K 1 are linearly independent. Definition 3.9 The dimension, dim(p ), of a polyhedron P is the maximum number of affinely independent points in P minus 1.

6 3. Linear Programming and Polyhedral Combinatorics February 20th, The dimension can be -1 (if P is empty), 0 (when P consists of a single point), 1 (when P is a line segment), and up to n when P affinely spans R n. In the latter case, we say that P is full-dimensional. The dimension of a cube in R 3 is 3, and so is the dimension of R 3 itself (which is a trivial polyhedron). Definition 3.10 α T x β is a valid inequality for P if α T x β for all x P. Observe that for an inequality to be valid for conv(s) we only need to make sure that it is satisfied by all elements of S, as this will imply that the inequality is also satisfied by points in conv(s) \ S. This observation will be important when dealing with convex hulls of combinatorial obects such as matchings or spanning trees. Definition 3.11 A face of a polyhedron P is {x P : α T x = β} where α T x β is some valid inequality of P. By definition, all faces are polyhedra. The empty face (of dimension -1) is trivial, and so is the entire polyhedron P (which corresponds to the valid inequality 0 T x 0). Non-trivial are those whose dimension is between 0 and dim(p ) 1. Faces of dimension 0 are called extreme points or vertices, faces of dimension 1 are called edges, and faces of dimension dim(p ) 1 are called facets. Sometimes, one uses ridges for faces of dimension dim(p ) 2. Exercise 3-4. List all 28 faces of the cube P = {x R 3 : 0 x i 1 for i = 1, 2, 3}. Although there are infinitely many valid inequalities, there are only finitely many faces. Theorem 3.5 Let A R m n. Then any non-empty face of P = {x R n : Ax b} corresponds to the set of solutions to a i x = b i for all i I a i x b i for all i / I, for some set I {1,, m}. Therefore, the number of non-empty faces of P is at most 2 m. Proof: Consider any valid inequality α T x β. Suppose the corresponding face F is non-empty. Thus F are all optimum solutions to Max subect to: α T x (P ) Ax b.

7 3. Linear Programming and Polyhedral Combinatorics February 20th, Choose an optimum solution y to the dual LP. By complementary slackness, the face F is defined by those elements x of P such that a T i x = b i for i I = {i : yi > 0}. Thus F is defined by a i x = b i for all i I a i x b i for all i / I. As there are 2 m possibilities for F, there are at most 2 m non-empty faces. The number of faces given in Theorem 3.5 is tight for polyhedra (see exercise below), but can be considerably improved for polytopes in the so-called upper bound theorem. Exercise 3-5. Let P = {x R n : x i 0 for i = 1,, n}. Show that P has 2 n + 1 faces. How many faces of dimension k does P have? For extreme points (faces of dimension 0), the characterization is even stronger (we do not need the inequalities): Theorem 3.6 Let x be an extreme point for P = {x : Ax b}. Then there exists I such that x is the unique solution to a i x = b i for all i I. Proof: Given an extreme point x, define I by I = {i : a ix = b i }. This means that for i / I, we have a ix < b i. From Theorem 3.5, we know that x is uniquely defined by a i x = b i for all i I (3) a i x b i for all i / I. (4) Now suppose there exists another solution ˆx when we consider only the equalities for i I. Then because of a ix < b i, we get that (1 ɛ)x + ɛˆx also satisfies (3) and (4) for ɛ sufficiently small. A contradiction (as the face was supposed to contain a single point). If P is given as {x : Ax = b, x 0} (as is often the case), the theorem still applies (as we still have a system of inequalities). In this case, the theorem says that every extreme point x can be obtained by setting some of the variables to 0, and solving for the unique solution to the resulting system of equalities. Without loss of generality, we can remove from Ax = b equalities that are redundant; this means that we can assume that A has full row rank (rank(a) = m for A R m n ). Letting N denote the indices of the non-basic variables that we set of 0 and B denote the remaining indices (of the so-called basic variables), we

8 3. Linear Programming and Polyhedral Combinatorics February 20th, can partition x into x B and x N (corresponding to these two sets of variables) and rewrite Ax = b as A B x B + A N x N = b, where A B and A N are the restrictions of A to the indices in B and N respectively. The theorem says that x is the unique solution to A B x B + A N x N = 0 and x N = 0, which means x N = 0 and A Bx B = b. This latter system must have a unique solution, which means that A B must have full column rank (rank(a B ) = B ). As A itself has rank m, we have that B m and we can augment B to include indices of N such that the resulting B satisfies (i) B = m and (ii) A B is a m m invertible matrix (and thus there is still a unique solution to A B x B = b). In linear programming terminology, a basic feasible solution or bfs of {x : Ax = b, x 0} is obtained by choosing a set B = m of indices with A B invertible and letting x B = A 1 B b and x N = 0 where N are the indices not in B. All extreme points are bfs and vice versa (although two different bases B may lead to the same extreme point, as there might be many ways of extending A B into a m m invertible matrix in the discussion above). One consequence of Theorem 3.5 is: Corollary 3.7 The maximal (inclusion-wise) non-trivial faces of a non-empty polyhedron P are the facets. For the vertices, one needs one additional condition: Corollary 3.8 If rank(a) = n (full column rank) then the minimal (inclusion-wise) nontrivial faces of a non-empty polyhedron P = {x R n : Ax b} are the vertices. Exercise 3-7 shows that the rank condition is necessary. This means that, if a linear program max{c T x : x P } with P = {x : Ax b} is feasible, bounded and rank(a) = n, then there exists an optimal solution which is a vertex of P (indeed, the set of all optimal solutions define a face the optimal face and if this face is not itself a vertex of P, it must contain vertices of P ). We now prove Corollary 3.8. Proof: Let F be a minimal (inclusion-wise) non-trivial face of F. This means that we have a set I such that F = {x : a T i x = b i i I a T x b / I} and adding any element to I makes this set empty. Consider two cases. Either P = {x R n : a T i x = b i for i I} or not. In the first case, it means that for every / I we have a lin({a i : i I}) and therefore since rank(a) = n we have that the system a T i x = b i for i I has a unique solution and thus F is a vertex. On the other hand, if P {x R n : a T i x = b i for i I} then let / I such that there exists x with a T i x = b i i I a T x > b. Since F is not trivial, there exists ˆx F, and in particular, ˆx satisfies a T i ˆx = b i a T ˆx b. i I

9 3. Linear Programming and Polyhedral Combinatorics February 20th, Thus for a suitable convex combination x of ˆx and x, we have a T i x = b i for i I {}, contrdicting the maximality of I. We now go back to the equivalence between Definitions 3.3 and 3.6 and claim that we can show that Definition 3.3 implies Definition 3.6. Theorem 3.9 If P = {x : Ax b} is bounded then P = conv(x) where X is the set of extreme points of P. This is a nice exercise using the Theorem of the Alternatives. Proof: Since X P, we have conv(x) P. Assume, by contradiction, that we do not have equality. Then there must exist x P \ conv(x). The fact that x / conv(x) means that there is no solution to: v X λ vv = x v X λ v = 1 λ v 0 v X. By the Theorem of the alternatives, this implies that c R n, t R: { t + n =1 c v 0 v X t + n =1 c x < 0. Since P is bounded, min{c T x : x P } is finite (say equal to z ), and the face induced by c T x z is non-empty but does not contain any vertex (as all vertices are dominated by x by the above inequalities). This is a contradiction with Corollary 3.8. When describing a polyhedron P in terms of linear inequalities, the only inequalities that are needed are the ones that define facets of P. This is stated in the next few theorems. We say that an inequality in the system Ax b is redundant if the corresponding polyhedron is unchanged by removing the inequality. For P = {x : Ax b}, we let I = denote the indices i such that a T i x = b i for all x P, and I < the remaining ones (i.e. those for which there exists x P with a T i x < b i ). This theorem shows that facets are sufficient: Theorem 3.10 If the face associated with a T i x b i for i I < is not a facet then the inequality is redundant. And this one shows that facets are necessary: Theorem 3.11 If F is a facet of P then there must exists i I < such that the face induced by a T i x b i is precisely F. In a minimal description of P, we must have a set of linearly independent equalities together with precisely one inequality for each facet of P.

10 3. Linear Programming and Polyhedral Combinatorics February 20th, Exercises Exercise 3-6. Prove Corollary 3.7. Exercise 3-7. Show that if rank(a) < n then P = {x R n : Ax b} has no vertices. Exercise 3-8. Suppose P = {x R n : Ax b, Cx d}. Show that the set of vertices of Q = {x R n : Ax b, Cx = d} is a subset of the set of vertices of P. (In particular, this means that if the vertices of P all belong to {0, 1} n, then so do the vertices of Q.) Exercise 3-9. Given two extreme points a and b of a polyhedron P, we say that they are adacent if the line segment between them forms an edge (i.e. a face of dimension 1) of the polyhedron P. This can be rephrased by saying that a and b are adacent on P if and only if there exists a cost function c such that a and b are the only two extreme points of P minimizing c T x over P. Consider the polyhedron (polytope) P defined as the convex hull of all perfect matchings in a (not necessarily bipartite) graph G. Give a necessary and sufficient condition for two matchings M 1 and M 2 to be adacent on this polyhedron (hint: think about M 1 M 2 = (M 1 \ M 2 ) (M 2 \ M 1 )) and prove that your condition is necessary and sufficient.) Exercise Show that two vertices u and v of a polyhedron P are adacent if and only there is a unique way to express their midpoint ( 1 (u + v)) as a convex combination of 2 vertices of P. 3.4 Polyhedral Combinatorics In one sentence, polyhedral combinatorics deals with the study of polyhedra or polytopes associated with discrete sets arising from combinatorial optimization problems (such as matchings for example). If we have a discrete set X (say the incidence vectors of matchings in a graph, or the set of incidence vectors of spanning trees of a graph, or the set of incidence vectors of stable sets 1 in a graph), we can consider conv(x) and attempt to describe it in terms of linear inequalities. This is useful in order to apply the machinery of linear programming. However, in some (most) cases, it is actually hard to describe the set of all inequalities defining conv(x); this occurs whenever optimizing over X is hard and this statement can be made precise in the setting of computational complexity. For matchings, or spanning trees, or several other structures (for which the corresponding optimization problem is polynomially solvable), we will be able to describe their convex hull in terms of linear inequalities. Given a set X and a proposed system of inequalities P = {x : Ax b}, it is usually easy to check whether conv(x) P. Indeed, for this, we only need to check that every member of X satisfies every inequality in the description of P. The reverse inclusion is more difficult. 1 A set S of vertices in a graph G = (V, E) is stable if there are no edges between any two vertices of S.

11 3. Linear Programming and Polyhedral Combinatorics February 20th, Here are 3 general techniques to prove that P conv(x) (if it is true!) (once we know that conv(x) P ). 1. Algorithmically. This involves linear programming duality. This is what we did in the notes about the assignment problem (minimum weight matchings in bipartite graphs). In general, consider any cost function c and consider the combinatorial optimization problem of maximizing c T x over x X. We know that: max{c T x : x X} = max{c T x : x conv(x)} max{c T x : Ax b} = min{b T y : A T y = c, y 0}, the last equality coming from strong duality. If we can exhibit a solution x X (say a perfect matching in the assignment problem) and a dual feasible solution y (values u i, v in the assignment problem) such that c T x = b T y we will have shown that we have equality throughout, and if this is true for any cost function, this implies that P = conv(x). This is usually the most involved approach but also the one that works most often. 2. Focusing on extreme points. Show first that P = {x : Ax b} is bounded (thus a polytope) and then study its extreme points. If we can show that every extreme point of P is in X then we would be done since P = conv(ext(p )) conv(x), where ext(p ) denotes the extreme points of P (see Theorem 3.9). The assumption that P is bounded is needed to show that indeed P = conv(ext(p )) (not true if P is unbounded). In the case of the convex hull of bipartite matchings, this can be done easily and this leads to the notion of Totally Unimodular Matrices (TUM), see the next section. 3. Focusing on the facets of conv(x). This leads usually to the shortest and cleanest proofs. Suppose that our proposed P is of the form {x R n : Ax b, Cx = d}. We have already argued that conv(x) P and we want to show that P conv(x). First we need to show that we are not missing any equality. This can be done for example by showing that dim(conv(x)) = dim(p ) (i.e. showing that if there are n d linearly independent rows in C we can find d + 1 affinely independent points in X). Then we need to show that we are not missing a valid inequality that induces a facet of conv(x). Consider any valid inequality α T x β for conv(x) with α 0. We can assume that α is any vector in R n \ {0} and that β = max{α T x : x conv(x)}. The face of conv(x) this inequality defines is F = conv({x X : α T x = β}). Assume that this is a non-trivial face; this will happen precisely when α is not in the row space of C. We need to make sure that if F is facet then we have in our description of P an inequality representing it. What we will show is that if F is non-trivial then we can find an inequality a T i x b i in our description of P such that F {x : a T i x = b i }, or simply

12 3. Linear Programming and Polyhedral Combinatorics February 20th, that every optimum solution to max{α T x : x X} satisfies a T i x = b i. This means that if F was a facet, by maximality, we have a representative of F in our description. This is a very simple and powerful technique, and this is best illustrated on an example. Example. claim that Let X = {(σ(1), σ(2),, σ(n)) : σ is a permutation of {1, 2,, n}}. We conv(x) = {x R n : This is known as the permutahedron. n i=1 x i = ( ) n+1 2 i S x i ( ) S +1 2 S {1,, n}}. Here conv(x) is not full-dimensional; we only need to show that we are not missing any facets and any equality in the description of conv(p ). For the equalities, this can be seen easily as it is easy to exhibit n affinely independent permutations in X. For the facets, suppose that α T x β defines a non-trivial facet F of conv(x). Consider maximizing α T x over all permutations x. Let S = arg min{α i }; by our assumption that F is non-trivial we have that S {1, 2,, n} (otherwise, we would have the equality n i=1 x i = ( ) n+1 2 ). Moreover, it is easy to see (by an exchange argument) that any permutationσ whose incidence vector x maximizes α T x will need to satisfy σ(i) {1, 2,, S } for i S, in other words, it will satisfy the inequality i S x i ( ) S +1 2 at equality. Hence, F is contained in the face corresponding to an inequality in our description, and hence our description contains inequalities for all facets. This is what we needed to prove. That s it! Exercises Exercise Consider the set X = {(σ(1), σ(2),, σ(n)) : σ is a permutation of {1, 2, n}}. Show that dim(conv(x)) = n 1. (To show that dim(conv(x)) n 1, exhibit n affinely independent permutations σ (and prove that they are affinely independent).) Exercise A stable set S (sometimes, it is called also an independent set) in a graph G = (V, E) is a set of vertices such that there are no edges between any two vertices in S. If we let P denote the convex hull of all (incidence vectors of) stable sets of G = (V, E), it is clear that x i + x 1 for any edge (i, ) E is a valid inequality for P. 1.Give a graph G for which P is not equal to {x R V : x i + x 1 for all (i, ) E x i 0 for all i V } 2.Show that if the graph G is bipartite then P equals {x R V : x i + x 1 for all (i, ) E x i 0 for all i V }.

13 3. Linear Programming and Polyhedral Combinatorics February 20th, Exercise Let e k R n (k = 0,..., n 1) be a vector with the first k entries being 1, and the following n k entries being 1. Let S = {e 0, e 1,..., e n 1, e 0, e 1,..., e n 1 }, i.e. S consists of all vectors consisting of +1 followed by 1 or vice versa. In this problem set, you will study conv(s). 1.Consider any vector a { 1, 0, 1} n such that (i) n i=1 a i = 1 and (ii) for all = 1,..., n 1, we have 0 i=1 a i 1. (For example, for n = 5, the vector (1, 0, 1, 1, 0) satisfies these conditions.) Show that n i=1 a ix i 1 and n i=1 a ix i 1 are valid inequalities for conv(s). 2.How many such inequalities are there? 3.Show that any such inequality defines a facet of conv(s). (This can be done in several ways. Here is one approach, but you are welcome to use any other one as well. First show that either e k or e k satisfies this inequality at equality, for any k. Then show that the resulting set of vectors on the hyperplane are affinely independent (or uniquely identifies it).) 4.Show that the above inequalities define the entire convex hull of S. (Again this can be done in several ways. One possibility is to consider the 3rd technique described above.) 3.5 Total unimodularity Definition 3.12 A matrix A is totally unimodular if every square submatrix of A has determinant 1, 0 or +1. The importance of total unimodularity stems from the following theorem. This theorem gives a subclass of integer programs which are easily solved. A polyhedron P is said to be integral if all its vertices or extreme points are integral (belong to Z n ). Theorem 3.12 Let A be a totally unimodular matrix. Then, for any integral right-hand-side b, the polyhedron P = {x : Ax b, x 0} is integral. Before we prove this result, two remarks can be made. First, the proof below will in fact show that the same result holds for the polyhedrons {x : Ax b, x 0} or {x : Ax = b, x 0}. In the latter case, though, a slightly weaker condition than totally unimodularity is sufficient to prove the result. Secondly, in the above theorem, one can prove the converse as well: If P = {x : Ax b, x 0} is integral for all integral b then A must be totally unimodular (this is not true though, if we consider for example {x : Ax = b, x 0}). Proof: Adding slacks, we get the polyhedron Q = {(x, s) : Ax + Is = b, x 0, s 0}. One can easily show (see exercise below) that P is integral iff Q is integral.

14 3. Linear Programming and Polyhedral Combinatorics February 20th, Consider now any bfs of Q. The basis B consists of some columns of A as well as some columns of the identity matrix I. Since the columns of I have only one nonzero entry per column, namely a one, we can expand the determinant of A B along these entries and derive that, in absolute values, the determinant of A B is equal to the determinant of some square submatrix of A. By definition of totally unimodularity, this implies that the determinant of A B must belong to { 1, 0, 1}. By definition of a basis, it cannot be equal to 0. Hence, it must be equal to ±1. We now prove that the bfs must be integral. The non-basic variables, by definition, must have value zero. The vector of basic variables, on the other hand, is equal to A 1 B linear algebra, A 1 B can be expressed as 1 A ad B det A B b. From where A ad B is the adoint (or adugate) matrix of A B and consists of subdeterminants of A B. Hence, both b and A ad B are integral which implies that A 1 B b is integral since det A B = 1. This proves the integrality of the bfs. Exercise Let P = {x : Ax b, x 0} and let Q = {(x, s) : Ax + Is = b, x 0, s 0}. Show that x is an extreme point of P iff (x, b Ax) is an extreme point of Q. Conclude that whenever A and b have only integral entries, P is integral iff Q is integral. In the case of the bipartite matching problem, the constraint matrix A has a very special structure and we show below that it is totally unimodular. This alongs with Theorem 3.12 proves Theorem 1.6 from the notes on the bipartite matching problem. First, let us restate the setting. Suppose that the bipartition of our bipartite graph is (U, V ) (to avoid any confusion with the matrix A or the basis B). Consider P = {x : x i = 1 i U x i = 1 i V x i 0 i U, V } = {x : Ax = b, x 0}. Theorem 3.13 The matrix A is totally unimodular. The way we defined the matrix A corresponds to a complete bipartite graph. If we were to consider any bipartite graph then we would simply consider a submatrix of A, which is also totally unimodular by definition. Proof: Consider any square submatrix T of A. We consider three cases. First, if T has a column or a row with all entries equal to zero then the determinant is zero. Secondly, if there exists a column or a row of T with only one +1 then by expanding the determinant

15 3. Linear Programming and Polyhedral Combinatorics February 20th, along that +1, we can consider a smaller sized matrix T. The last case is when T has at least two nonzero entries per column (and per row). Given the special structure of A, there must in fact be exactly 2 nonzero entries per column. By adding up the rows of T corresponding to the vertices of U and adding up the rows of T corresponding to the vertices of V, one therefore obtains the same vector which proves that the rows of T are linearly dependent, implying that its determinant is zero. This proves the totally unimodularity of A. We conclude with a technical remark. One should first remove one of the rows of A before applying Theorem 3.12 since, as such, it does not have full row rank and this fact was implicitly used in the definition of a bfs. However, deleting a row of A still preserves its totally unimodularity. Exercise Exercise If A is totally unimodular then A T is totally unimodular. Use total unimodularity to prove König s theorem. The following theorem gives a necessary and sufficient condition for a matrix to be totally unimodular. Theorem 3.14 Let A be a m n matrix with entries in { 1, 0, 1}. Then A is TUM if and only if for all subsets R {1, 2,, n} of rows, there exists a partition of R into R 1 and R 2 such that for all {1, 2,, m}: a i a i {0, 1, 1}. i R 1 i R 2 We will prove only the if direction. Proof: Assume that, for every R, the desired partition exists. We need to prove that the determinant of any k k submatrix of A is in { 1, 0, 1}, and this must be true for any k. Let us prove it by induction on k. It is trivially true for k = 1. Assume it is true for k 1 and we will prove it for k. Let B be a k k submatrix of A, and we can assume that B is invertible (otherwise the determinant is 0 and there is nothing to prove). The inverse B 1 1 can be written as det(b) B, where all entries of B correspond to (k 1) (k 1) submatrices of A. By our inductive hypothesis, all entries of B are in { 1, 0, 1}. Let b 1 be the first row of B and e 1 be the k-dimensional row vector [ ], thus b 1 = e 1 B. By the relationship between B and B, we have that b 1B = e 1 B B = det(b)e 1 B 1 B = det(b)e 1. (5) Let R = {i : b 1i { 1, 1}}. By assumption, we know that there exists a partition of R into R 1 and R 2 such that for all : b i b i { 1, 0, 1}. (6) i R 1 i R 2

16 3. Linear Programming and Polyhedral Combinatorics February 20th, From (5), we have that b 1ib i = i R { det(b) = Since the left-hand-sides of equations (6) and (7) differ by a multiple of 2 for each (since b 1i { 1, 1}), this implies that { = 0 1 b i b i (8) { 1, 1} = 1 i R 1 i R 2 The fact that we could not get 0 for = 1 follows from the fact that otherwise B would be singular (we would get exactly the 0 vector by adding and subtracting rows of B). If we define y R k by we get that yb = ±e 1. Thus y i = 1 i R 1 1 i R 2 0 otherwise (7) y = ±e 1 B 1 = ± 1 det B e 1B = ± 1 det B b 1, which implies that det B must be either 1 or -1. Exercise Suppose we have n activities to choose from. Activity i starts at time t i and ends at time u i (or more precisely ust before u i ); if chosen, activity i gives us a profit of p i units. Our goal is to choose a subset of the activities which do not overlap (nevertheless, we can choose an activity that ends at t and one that starts at the same time t) and such that the total profit (i.e. sum of profits) of the selected activities is maximum. 1.Defining x i as a variable that represents whether activity i is selected (x i = 1) or not (x i = 0), write an integer program of the form max{p T x : Ax b, x {0, 1} n } that would solve this problem. 2.Show that the matrix A is totally unimodular, implying that one can solve this problem by solving the linear program max{p T x : Ax b, 0 x i 1 for every i}.

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