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Preliminaries

Incidence vectors and matrices

Let G = (V, E) be a graph with n = |V| nodes and m = |E| edges.

For every subset U of vertices, its incidence vector 1U is a vector of size n, in which element v is 1 if node v is in U, and 0 otherwise. Similarly, for every subset F of edges, its incidence vector 1F is a vector of size m, in which element e is 1 if edge e is in F, and 0 otherwise.

For every node v in V, the set of edges in E adjacent to v is denoted by E(v). Therefore, each vector 1E(v) is a 1-by-m vector in which element e is 1 if edge e is adjacent to v, and 0 otherwise. The incidence matrix of the graph, denoted by AG, is an n-by-m matrix in which each row v is the incidence vector 1E(V). In other words, each element v,e in the matrix is 1 if node v is adjacent to edge e, and 0 otherwise.

Below are three examples of incidence matrices: the triangle graph (a cycle of length 3), a square graph (a cycle of length 4), and the complete graph on 4 vertices.

Linear programs

For every subset F of edges, the dot product 1E(v) · 1F represents the number of edges in F that are adjacent to v. Therefore, the following statements are equivalent:

• A subset F of edges represents a matching in G;
• For every node v in V: 1E(v) · 1F ≤ 1.
• AG · 1F ≤ 1V.

The cardinality of a set F of edges is the dot product 1E · 1F . Therefore, a maximum cardinality matching in G is given by the following integer linear program:

Maximize 1E · x

Subject to: x in {0,1}m

__________ AG · x ≤ 1V.

Fractional matching polytope

The fractional matching polytope of a graph G, denoted FMP(G), is the polytope defined by the relaxation of the above linear program, in which each x may be a fraction and not just an integer:

Maximize 1E · x

Subject to: x ≥ 0E

__________ AG · x ≤ 1V.

This is a linear program. It has m "at-least-0" constraints and n "less-than-one" constraints. The set of its feasible solutions is a convex polytope. Each point in this polytope is a fractional matching. For example, in the triangle graph there are 3 edges, and the corresponding linear program has the following 6 constraints:

Maximize x1+x2+x3

Subject to: x1≥0, x2≥0, x3≥0.

__________ x1+x2≤1, x2+x3≤1, x3+x1≤1.

This set of inequalities represents a polytope in R3 - the 3-dimensional Euclidean space.

The polytope has five corners (extreme points). These are the points that attain equality in 3 out of the 6 defining inequalities. The corners are (0,0,0), (1,0,0), (0,1,0), (0,0,1), and (1/2,1/2,1/2).² The first corner (0,0,0) represents the trivial (empty) matching. The next three corners (1,0,0), (0,1,0), (0,0,1) represent the three matchings of size 1. The fifth corner (1/2,1/2,1/2) does not represent a matching - it represents a fractional matching in which each edge is "half in, half out". Note that this is the largest fractional matching in this graph - its weight is 3/2, in contrast to the three integral matchings whose size is only 1.

As another example, in the 4-cycle there are 4 edges. The corresponding LP has 4+4=8 constraints. The FMP is a convex polytope in R4. The corners of this polytope are (0,0,0,0), (1,0,0,0), (0,1,0,0), (0,0,1,0), (0,0,0,1), (1,0,1,0), (0,1,0,1). Each of the last 2 corners represents matching of size 2, which is a maximum matching. Note that in this case all corners have integer coordinates.

Integral matching polytope

The integral matching polytope (usually called just the matching polytope) of a graph G, denoted MP(G), is a polytope whose corners are the incidence vectors of the integral matchings in G.

MP(G) is always contained in FMP(G). In the above examples:

• The MP of the triangle graph is strictly contained in its FMP, since the MP does not contain the non-integral corner (1/2, 1/2, 1/2).
• The MP of the 4-cycle graph is identical to its FMP, since all the corners of the FMP are integral.

The matching polytopes in a bipartite graph

The above example is a special case of the following general theorem:³: 274 

G is a bipartite graph if-and-only-if MP(G) = FMP(G) if-and-only-if all corners of FMP(G) have only integer coordinates.

This theorem can be proved in several ways.

Proof using matrices

When G is bipartite, its incidence matrix AG is totally unimodular - every square submatrix of it has determinant 0, +1 or −1. The proof is by induction on k - the size of the submatrix (which we denote by K). The base k = 1 follows from the definition of AG - every element in it is either 0 or 1. For k>1 there are several cases:

• If K has a column consisting only of zeros, then det K = 0.
• If K has a column with a single 1, then det K can be expanded about this column and it equals either +1 or -1 times a determinant of a (k − 1) by (k − 1) matrix, which by the induction assumption is 0 or +1 or −1.
• Otherwise, each column in K has two 1s. Since the graph is bipartite, the rows can be partitioned into two subsets, such that in each column, one 1 is in the top subset and the other 1 is in the bottom subset. This means that the sum of the top subset and the sum of the bottom subset are both equal to 1E minus a vector of |E| ones. This means that the rows of K are linearly dependent, so det K = 0.

As an example, in the 4-cycle (which is bipartite), the det AG = 1. In contrast, in the 3-cycle (which is not bipartite), det AG = 2.

Each corner of FMP(G) satisfies a set of m linearly-independent inequalities with equality. Therefore, to calculate the corner coordinates we have to solve a system of equations defined by a square submatrix of AG. By Cramer's rule, the solution is a rational number in which the denominator is the determinant of this submatrix. This determinant must by +1 or −1; therefore the solution is an integer vector. Therefore all corner coordinates are integers.

By the n "less-than-one" constraints, the corner coordinates are either 0 or 1; therefore each corner is the incidence vector of an integral matching in G. Hence FMP(G) = MP(G).

The facets of the matching polytope

A facet of a polytope is the set of its points which satisfy an essential defining inequality of the polytope with equality. If the polytope is d-dimensional, then its facets are (d − 1)-dimensional. For any graph G, the facets of MP(G) are given by the following inequalities:⁴: 275–279 

• x ≥ 0E
• 1E(v) · x ≤ 1 (where v is a non-isolated vertex such that, if v has only one neighbor u, then {u,v} is a connected component of G, and if v has exactly two neighbors, then they are not adjacent).
• 1E(S) · x ≤ (|S| − 1)/2 (where S spans a 2-connected factor-critical subgraph.)

Perfect matching polytope

The perfect matching polytope of a graph G, denoted PMP(G), is a polytope whose corners are the incidence vectors of the integral perfect matchings in G.⁵: 274  Obviously, PMP(G) is contained in MP(G); In fact, PMP(G) is the face of MP(G) determined by the equality:

1E · x = n/2.

Edmonds⁶ proved that, for every graph G, PMP(G) can be described by the following constraints:

1E(v) · x = 1 for all v in V (-- exactly one edge adjacent to v is in the matching)

1E(W) · x ≥ 1 for every subset W of V with |W| odd (-- at least one edge should connect W to V\W). These constraints are called odd cut constraints.

x ≥ 0E

Using this characterization and Farkas lemma, it is possible to obtain a good characterization of graphs having a perfect matching.⁷: 206  By solving algorithmic problems on convex sets, one can find a minimum-weight perfect matching.⁸: 206--208 

See also

• Stable matching polytope

External links

• The matching polytope, by Michael Goemans
• The matching polytope, by Jan Vondrak
• The matching polytope, by Vincent Jost

References

1. Lovász, László; Plummer, M. D. (1986), Matching Theory, Annals of Discrete Mathematics, vol. 29, North-Holland, ISBN 0-444-87916-1, MR 0859549 0-444-87916-1 ↩

2. "1 Bipartite Matching Polytope, Stable Matching Polytope x1 x2 x3 Lecture 10: Feb ppt download". slideplayer.com. Retrieved 2020-07-17. https://slideplayer.com/slide/5120421/ ↩

3. Lovász, László; Plummer, M. D. (1986), Matching Theory, Annals of Discrete Mathematics, vol. 29, North-Holland, ISBN 0-444-87916-1, MR 0859549 0-444-87916-1 ↩

4. Lovász, László; Plummer, M. D. (1986), Matching Theory, Annals of Discrete Mathematics, vol. 29, North-Holland, ISBN 0-444-87916-1, MR 0859549 0-444-87916-1 ↩

5. Lovász, László; Plummer, M. D. (1986), Matching Theory, Annals of Discrete Mathematics, vol. 29, North-Holland, ISBN 0-444-87916-1, MR 0859549 0-444-87916-1 ↩

6. Edmonds, Jack (January 1965). "Maximum matching and a polyhedron with 0,1-vertices" (PDF). Journal of Research of the National Bureau of Standards, Section B. 69B (1 and 2): 125. doi:10.6028/jres.069B.013. ISSN 0022-4340. https://nvlpubs.nist.gov/nistpubs/jres/69B/jresv69Bn1-2p125_A1b.pdf ↩

7. Grötschel, Martin; Lovász, László; Schrijver, Alexander (1993), Geometric algorithms and combinatorial optimization, Algorithms and Combinatorics, vol. 2 (2nd ed.), Springer-Verlag, Berlin, doi:10.1007/978-3-642-78240-4, ISBN 978-3-642-78242-8, MR 1261419 978-3-642-78242-8 ↩

8. Grötschel, Martin; Lovász, László; Schrijver, Alexander (1993), Geometric algorithms and combinatorial optimization, Algorithms and Combinatorics, vol. 2 (2nd ed.), Springer-Verlag, Berlin, doi:10.1007/978-3-642-78240-4, ISBN 978-3-642-78242-8, MR 1261419 978-3-642-78242-8 ↩