# 1. Lecture notes on bipartite matching

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1 Massachusetts Institute of Technology Handout : Combinatorial Optimization February 9th, 2009 Michel X. Goemans 1. Lecture notes on bipartite matching Matching problems are among the fundamental problems in combinatorial optimization. In this set of notes, we focus on the case when the underlying graph is bipartite. We start by introducing some basic graph terminology. A graph G = (V, E) consists of a set V of vertices and a set E of pairs of vertices called edges. For an edge e = (u, v), we say that the endpoints of e are u and v; we also say that e is incident to u and v. A graph G = (V, E) is bipartite if the vertex set V can be partitioned into two sets A and B (the bipartition) such that no edge in E has both endpoints in the same set of the bipartition. A matching M E is a collection of edges such that every vertex of V is incident to at most one edge of M. If a vertex v has no edge of M incident to it then v is said to be exposed (or unmatched). A matching is perfect if no vertex is exposed; in other words, a matching is perfect if its cardinality is equal to A = B exposed 3 8 matching Figure 1.1: Example. The edges (1, 6), (2, 7) and (3, 8) form a matching. Vertices 4, 5, 9 and 10 are exposed. We are interested in the following two problems: Maximum cardinality matching problem: Find a matching M of maximum size. Minimum weight perfect matching problem: Given a cost c ij for all (i, j) E, find a perfect matching of minimum cost where the cost of a matching M is given by c(m) = (i,j) M c ij. This problem is also called the assignment problem. Similar problems (but more complicated) can be defined on non-bipartite graphs.

2 1. Lecture notes on bipartite matching February 9th, Maximum cardinality matching problem Before describing an algorithm for solving the maximum cardinality matching problem, one would like to be able to prove optimality of a matching (without reference to any algorithm). For this purpose, one would like to find upper bounds on the size of any matching and hope that the smallest of these upper bounds be equal to the size of the largest matching. This is a duality concept that will be ubiquitous in this subject. In this case, the dual problem will itself be a combinatorial optimization problem. A vertex cover is a set C of vertices such that all edges e of E are incident to at least one vertex of C. In other words, there is no edge completely contained in V C (we use both and \ to denote the difference of two sets). Clearly, the size of any matching is at most the size of any vertex cover. This follows from the fact that, given any matching M, a vertex cover C must contain at least one of the endpoints of each edge in M. We have just proved weak duality: The maximum size of a matching is at most the minimum size of a vertex cover. As we ll prove later in these notes, equality in fact holds: Theorem 1.1 (König 1931) For any bipartite graph, the maximum size of a matching is equal to the minimum size of a vertex cover. We shall prove this minmax relationship algorithmically, by describing an efficient algorithm which simultaneously gives a maximum matching and a minimum vertex cover. König s theorem gives a good characterization of the problem, namely a simple proof of optimality. In the example above, one can prove that the matching (1, 9), (2, 6), (3, 8) and (5, 7) is of maximum size since there exists a vertex cover of size 4. Just take the set {1, 2, 5, 8}. The natural approach to solving this cardinality matching problem is to try a greedy algorithm: Start with any matching (e.g. an empty matching) and repeatedly add disjoint edges until no more edges can be added. This approach, however, is not guaranteed to give a maximum matching (convince yourself). We will now present an algorithm that does work, and is based on the concepts of alternating paths and augmenting paths. A path is simply a collection of edges (v 0, v 1 ), (v 1, v 2 ),...,(v k 1, v k ) where the v i s are distinct vertices. A path can simply be represented as v 0 -v v k. Definition 1.1 An alternating path with respect to M is a path that alternates between edges in M and edges in E M. Definition 1.2 An augmenting path with respect to M is an alternating path in which the first and last vertices are exposed. In the above example, the paths 4-8-3, or are alternating, but only the last one is augmenting. Notice that an augmenting path with respect to M which contains k edges of M must also contain exactly k + 1 edges not in M. Also, the two endpoints of an augmenting path must be on different sides of the bipartition. The most interesting property of an augmenting path P with respect to a matching M is that if we set M = M P (M P) (P M), then we get a matching M and, moreover, the size of

3 1. Lecture notes on bipartite matching February 9th, M is one unit larger than the size of M. That is, we can form a larger matching M from M by taking the edges of P not in M and adding them to M while removing from M the edges in M that are also in the path P. We say that we have augmented M along P. The usefulness of augmenting paths is given in the following theorem. Theorem 1.2 A matching M is maximum if and only if there are no augmenting paths with respect to M. Proof: (By contradiction) ( ) Let P be some augmenting path with respect to M. Set M = M P. Then M is a matching with cardinality greater than M. This contradicts the maximality of M. ( ) If M is not maximum, let M be a maximum matching (so that M > M ). Let Q = M M. Then: Q has more edges from M than from M (since M > M implies that M M > M M ). Each vertex is incident to at most one edge in M Q and one edge M Q. Thus Q is composed of cycles and paths that alternate between edges from M and M. Therefore there must be some path with more edges from M in it than from M (all cycles will be of even length and have the same number of edges from M and M). This path is an augmenting path with respect to M. Hence there must exist an augmenting path P with respect to M, which is a contradiction. This theorem motivates the following algorithm. Start with any matching M, say the empty matching. Repeatedly locate an augmenting path P with respect to M, augment M along P and replace M by the resulting matching. Stop when no more augmenting path exists. By the above theorem, we are guaranteed to have found an optimum matching. The algorithm terminates in µ augmentations, where µ is the size of the maximum matching. Clearly, µ n where n = V. 2 In the example, one would thus augment M along an augmenting path, say , obtain the matching (1, 9), (2, 6), (3, 8) and (5, 7), and then realize that no more augmenting paths can be found. The question now is how to decide the existence of an augmenting path and how to find one, if one exists. These tasks can be done as follows. Direct edges in G according to M as follows : An edge goes from A to B if it does not belong to the matching M and from B to A if it does. Call this directed graph D. Claim 1.3 There exists an augmenting path in G with respect to M iff there exists a directed path in D between an exposed vertex in A and an exposed vertex in B. Exercise 1-1. Prove claim 1.3.

4 1. Lecture notes on bipartite matching February 9th, This gives an O(m) algorithm (where m = E ) for finding an augmenting path in G. Let A and B be the set of exposed vertices w.r.t. M in A and B respectively. We can simply attach a vertex s to all the vertices in A and do a depth-first-search from s till we hit a vertex in B and then trace back our path. Thus the overall complexity of finding a maximum cardinality matching is O(nm). This can be improved to O( nm) by augmenting along several augmenting paths simultaneously. If there is no augmenting path with respect to M, then we can also use our search procedure for an augmenting path in order to construct an optimum vertex cover. Consider the set L (for Labelling) of vertices which can be reached by a directed path from an exposed vertex in A. Claim 1.4 When the algorithm terminates, C = (A L) (B L) is a vertex cover. Moreover, C = M where M is the matching returned by the algorithm. This claim immediately proves König s theorem. Proof: We first show that C is a vertex cover. Assume not. Then there must exist an edge e = (a, b) E with a A L and b (B L). The edge e cannot belong to the matching. If it did, then b should be in L for otherwise a would not be in L. Hence, e must be in E M and is therefore directed from A to B. This therefore implies that b can be reached from an exposed vertex in A (namely go to a and then take the edge (a, b)), contradicting the fact that b / L. To show the second part of the proof, we show that C M, since the reverse inequality is true for any matching and any vertex cover. The proof follows from the following observations. 1. No vertex in A L is exposed by definition of L, 2. No vertex in B L is exposed since this would imply the existence of an augmenting path and, thus, the algorithm would not have terminated, 3. There is no edge of the matching between a vertex a (A L) and a vertex b (B L). Otherwise, a would be in L. These remarks imply that every vertex in C is matched and moreover the corresponding edges of the matching are distinct. Hence, C M. Although the concepts of maximum matchings and minimum vertex covers can be defined also for general (i.e. non-bipartite) graphs, we should remark that König s theorem does not generalize to all graphs. Indeed, although it is true that the size of a maximum matching is always at most the minimum size of a vertex cover, equality does not necessarily hold. Consider indeed the cycle C 3 on 3 vertices (the smallest non-bipartite graph). The maximum matching has size 1, but the minimum vertex cover has size 2. We will derive a minmax relation involving maximum matchings for general graphs, but it will be more complicated than König s theorem.

5 1. Lecture notes on bipartite matching February 9th, Exercises Exercise 1-2. An edge cover of a graph G = (V, E) is a subset of R of E such that every vertex of V is incident to at least one edge in R. Let G be a bipartite graph with no isolated vertex. Show that the cardinality of the minimum edge cover R of G is equal to V minus the cardinality of the maximum matching M of G. Give an efficient algorithm for finding the minimum edge cover of G. Is this true also for non-bipartite graphs? Exercise 1-3. Show that in any graph G = (V, E) (not necessarily bipartite), the size of any maximal matching M (i.e. a matching M in which one cannot add an edge while keeping it a matching) is at least half the size of a maximum matching M. Exercise 1-4. Can the following figure be tiled by dominoes (a domino being 2 adjacent squares)? Give a tiling or a short proof that no tiling exists. Exercise 1-5. Consider a bipartite graph G = (V, E) with bipartition (A, B): V = A B. Assume that, for some vertex sets A 1 A and B 1 B, there exists a matching M A covering all vertices in A 1 and a matching M B covering all vertices in B 1. Prove that there always exists a matching covering all vertices in A 1 B 1. Exercise 1-6. Consider the following 2-person game on a (not necessarily bipartite) graph G = (V, E). Players 1 and 2 alternate and each selects a (yet unchosen) edge e of the graph so that e together with the previously selected edges form a simple path. The first player unable to select such an edge loses. Show that if G has a perfect matching then player 1 has a winning strategy Hall s Theorem Hall s theorem gives a necessary and sufficient condition for a bipartite graph to have a matching which saturates (or matches) all vertices of A (i.e. a matching of size A ).

6 1. Lecture notes on bipartite matching February 9th, Theorem 1.5 (Hall 1935) Given a bipartite graph G = (V, E) with bipartition A, B (V = A B), G has a matching of size A if and only if for every S A we have N(S) S, where N(S) = {b B : a S with (a, b) E}. Clearly, the condition given in Hall s theorem is necessary; its sufficiency can be derived from König s theorem. Exercise 1-7. Deduce Hall s theorem from König s theorem. 1.2 Minimum weight perfect matching By assigning infinite costs to the edges not present, one can assume that the bipartite graph is complete. The minimum cost (weight) perfect matching problem is often described by the following story: There are n jobs to be processed on n machines or computers and one would like to process exactly one job per machine such that the total cost of processing the jobs is minimized. Formally, we are given costs c ij R { } for every i A, j B and the goal is to find a perfect matching M minimizing (i,j) M c ij. In these notes, we present an algorithm for this problem which is based upon linear programming, and we will take this opportunity to illustrate several important concepts in linear programming. These concepts will be formalized and generalized in a subsequent chapter. The first algorithm given for the assignment problem was given by Kuhn [1955], but he showed only finiteness of the algorithm. A refined version was given by Jim Munkres [1957], and showed a polynomial running time. An algorithm is polynomial-time if its running time (the number of basic operations to run it) is upper bounded by a polynomial in the size of the input (i.e. the number of bits needed to represent the input). Munkres analysis even shows that the algorithm is strongly polynomial, and this means that the running time is polynomial in the number of numbers involved (i.e. does not depend on the size of the costs c ij ). In this algorithm, the number of operations is upper bounded by O(n 3 ) where n = V. The algorithm is often called the Hungarian method, as it relies on ideas developed by Hungarians, and especially König and Egerváry. In 2006, it was discovered that the method had actually been discovered in the 19th century by Jacobi and this was posthumously published in 1890 in Latin, see ollivier/jacobi/bibliographieen.html. We start by giving a formulation of the problem as an integer program, i.e. an optimization problem in which the variables are restricted to integer values and the constraints and teh objective function are linear as a function of these variables. We first need to associate a point to every matching. For this purpose, given a matching M, let its incidence vector be x where x ij = 1 if (i, j) M and 0 otherwise. One can formulate the minimum weight perfect matching problem as follows: Min i,j c ij x ij

7 1. Lecture notes on bipartite matching February 9th, subject to: x ij = 1 j x ij = 1 i x ij 0 i A j B i A, j B x ij integer i A, j B. This is not a linear program, but a so-called integer program. Notice that any solution to this integer program corresponds to a matching and therefore this is a valid formulation of the minimum weight perfect matching problem in bipartite graphs. Consider now the linear program (P) obtained by dropping the integrality constraints: Min i,j c ij x ij (P) subject to: x ij = 1 i A j x ij = 1 i j B x ij 0 i A, j B. This is the linear programming relaxation of the above integer program. In a linear program, the variables can take fractional values and therefore there are many feasible solutions to the set of constraints above which do not correspond to matchings. But we only care about the optimum solutions. The set of feasible solutions to the constraints in (P) forms a polytope, and when we optimize a linear constraint over a polytope, the optimum will be attained at one of the corners or extreme points of the polytope. An extreme point x of a set Q is an element x Q which cannot be written as λy + (1 λ)z with 0 < λ < 1, y, z Q with y z. (This will be formalized and discussed in more details when we discuss polyhedral theory.) In general, even if all the coefficients of the constraint matrix in a linear program are either 0 or 1, the extreme points of a linear program are not guaranteed to have all coordinates integral (this is of no surprise since the general integer programming problem is NP-hard, while linear programming is polynomially solvable). As a result, in general, there is no guarantee that the value Z IP of an integer program is equal to the value Z LP of its LP relaxation. However, since the integer program is more constrained than the relaxation, we always have that Z IP Z LP, implying that Z LP is a lower bound on Z IP for a minimization problem. Moreover, if an optimum solution to a linear programming relaxation is integral then it must also be an optimum solution to the integer program. Exercise 1-8. Prove this last claim.

8 1. Lecture notes on bipartite matching February 9th, Exercise 1-9. Give an example of an integer program where Z IP Z LP. However, in the case of the perfect matching problem, the constraint matrix has a very special form and one can show the following very important result. Theorem 1.6 Any extreme point of (P) is a 0-1 vector and, hence, is the incidence vector of a perfect matching. Because of the above theorem, the polytope P = {x : x ij = 1 j x ij = 1 i i A j B x ij 0 i A, j B} is called the bipartite perfect matching polytope. To demonstrate the beauty of matchings, we shall give two completely different proofs of this result, one purely algorithmic here and one purely algebraic in the chapter on polyhedral theory. The algebraic proof is related to the notion of totally unimodularity. To prove it algorithmically, we describe an algorithm for solving the minimum weight perfect matching problem. The algorithm is primal-dual. To explain what this means, we need to introduce the notion of duality of linear programs, and let s do it in the specific case of our bipartite matching problem. Suppose we have values u i for i A and v j for j B such that u i + v j c ij for all i A and j B. Then for any perfect matching M, we have that c ij u i + v j. (1) i A j B (i,j) M Thus, i A u i + j B v j is a lower bound on the cost of the minimum cost perfect matching (for bipartite graphs). To get the best lower bound, we would like to maximize this quantity, and therefore we obtain another linear program Max i A subject to: u i + j B v j (D) u i + v j c ij i A, j B. This is called the dual linear program (D), and it can be shown to be dual to (P) in the sense of linear programming duality. The dual constraints can be interpreted as w ij 0 where w ij = c ij u i v j. If, for any instance, we could always find a feasible solution u, v to (D) and a perfect matching M such that we have equality in (1) (i.e. the cost of the perfect matching is equal

9 1. Lecture notes on bipartite matching February 9th, to the value of the dual solution) then we would know that the matching found is optimum. Given a solution u, v to the dual, a perfect matching M would satisfy equality if it contains only edges (i, j) such that w ij = c ij u i v j = 0. This is what is referred to as complementary slackness. However, for a given u, v, we may not be able to find a perfect matching among the edges with w ij = 0. The algorithm performs a series of iterations. It always maintains a dual feasible solution and tries to find an almost primal feasible solution x satisfying complementary slackness. The fact that complementary slackness is imposed is crucial in any primal-dual algorithm. In fact, the most important (and elegant) algorithms in combinatorial optimization are primal-dual. This is one of the most important tool for designing efficient algorithms for combinatorial optimization problems (for problems which, of course, admit such efficient solutions). More precisely, the algorithm works as follows. It first starts with any dual feasible solution, say u i = 0 for all i and v j = min i c ij for all j. In a given iteration, the algorithm has a dual feasible solution (u, v) or say (u, v, w). Imposing complementary slackness means that we are interested in matchings which are subgraphs of E = {(i, j) : w ij = 0}. If E has a perfect matching then the incidence vector of that matching is a feasible solution in (P) and satisfies complementary slackness with the current dual solution and, hence, must be optimal. To check whether E has a perfect matching, one can use the cardinality matching algorithm developed earlier in these notes. If the maximum matching output is not perfect then the algorithm will use information from the optimum vertex cover C to update the dual solution in such a way that the value of the dual solution increases (we are maximizing in the dual). In particular, if L is as in the previous section then there is no edge of E between A L and B L. In other words, for every i (A L) and every j (B L), we have w ij > 0. Let δ = min w ij. i (A L),j (B L) By the above argument, δ > 0. The dual solution is updated as follows: { ui i A L u i = u i + δ i A L and v j = { vj i B L v j δ j B L One easily check that this dual solution is feasible, in the sense that the corresponding vector w satisfies w ij 0 for all i and j. What is the value of the new dual solution? The difference between the values of the new dual solution and the old dual solution is equal to: δ( A L B L ) = δ( A L + A L A L B L ) = δ( n 2 C ), where A has size n/2 and C is the optimum vertex cover for the bipartite graph with edge set E. But by assumption C < n, implying that the value of the dual solution strictly 2 increases.

10 1. Lecture notes on bipartite matching February 9th, One repeats this procedure until the algorithm terminates. At that point, we have an incidence vector of a perfect matching and also a dual feasible solution which satisfy complementary slackness. They must therefore be optimal and this proves the existence of an integral optimum solution to (P). Since, by carefully choosing the cost function, one can make any extreme point be the unique optimum solution to the linear program, this proves Theorem 1.6. Of course, as some of the readers might have noticed, the proof is not complete yet since one needs to prove that the algorithm indeed terminates. This can be proved by noticing that at least one more vertex of B must be reachable from an exposed vertex of A (and no vertex of B becomes unreachable), since an edge e = (i, j) with i (A L) and j B L now has w ij = 0 by our choice of δ. This also gives an estimate of the number of iterations. In at most n/2 iterations, all vertices of B are reachable or the matching found has increased by at least one unit. Therefore, after O(n 2 ) iterations, the matching found is perfect. The overall running time of the algorithm is thus O(n 4 ) since it takes O(n 2 ) to compute the set L in each iteration. By looking more closely at how vertices get labelled between two increases of the size of the matching, one can reduce the running time analysis to O(n 3 ). Exercise Check that the running time of the algorithm is indeed O(n 3 ). Example: Consider the instance given by the following cost matrix defined on a bipartite graph with 5 vertices on each side of the bipartition: Assume that u T = (2, 3, 0, 2, 0) and v T = ( 2, 0, 3, 0, 0). The set E of edges with w ij = 0 corresponds exactly to the set of edges in Figure 1.1. The maximum cardinality matching algorithm finds the matching (1, 9), (2, 6), (3, 8) and (5, 7), and the set of labelled vertices is {3, 4, 8}. We compute δ as δ = min w ij = 1 i {3,4},j {6,7,9,10} corresponding to the edge (3, 9). The new vectors u and v are u T = (2, 3, 1, 1, 0) and v T = ( 2, 0, 2, 0, 0). The value of the dual solution has increased from 4 units to 5. The corresponding set E now has a perfect matching, namely (1, 6), (2, 7), (3, 9), (4, 8) and (5, 10) of cost 5. Both the matching and the dual solution are optimal. Exercises Exercise Consider a bipartite graph G = (V, E) in which every vertex has degree k (a so-called k-regular bipartite graph). Prove that such a graph always has a perfect matching in two different ways:

11 1. Lecture notes on bipartite matching February 9th, by using König s theorem, 2.by using the linear programming formulation we have derived in this section. Exercise Using the previous exercise 11, show that the edges of a k-regular bipartite graph G can be partitioned into k matchings (i.e. the number of colors needed to color the edges of a k-regular bipartite graph such that no two edges with a common endpoint have the same color is precisely k). (Optional: Are this result and the one in the previous exercise also true also for non-bipartite graphs?) Exercise We have shown that there always exists a solution x to the linear program (P) with all components integral. Reprove this result in the following way. Take a (possibly non-integral) optimum solution x. If there are many optimum solutions, take one with as few non-integral values x ij as possible. Show that, if x is not integral, there exists a cycle C with all edges e = (i, j) C having a non-integral value x ij. Now show how to derive another optimum solution with fewer non-integral values, leading to a contradiction. Exercise In this exercise, you will do a little experiment with the (minimum cost) assignment problem. Take a complete bipartite graph with n vertices on each side of the bipartition, and let us assume that all c ij (for i, j {1,, n}) are all independent uniform random variables between 0 and 1. Take 5 different values for n (the largest being a few hundreds) and for each compute the minimum cost assignment value for 5 instances. Any guess on how this value increases as n goes to infinity. Does it seem to converge? To what value? Surprised? (Yes, you should be, and it is normal that you do not understand why.) To solve the assignment problem, you can use MATLAB (it is available on athena). To access MATLAB on athena, you need to type: >> add matlab >> matlab If you have never used MATLAB before, you can find some tutorial on the webpage There is a MATLAB m-file at goemans/bghungar.m which implements the Hungarian method (just put it in the directory from which you run MATLAB). If C is an n n matrix, then >> a=bghungar(c) gives a vector a such that (1, a(1)), (2, a(2)), is the maximizing matching. So to get the value of the minimum assignment, you can just do: >> a=bghungar(-c); >> v=sum(diag(c(1:n,a(1:n))))

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