Search results
Results from the WOW.Com Content Network
The number of perfect matchings in a complete graph K n (with n even) is given by the double factorial (n − 1)!!. [13] The numbers of matchings in complete graphs, without constraining the matchings to be perfect, are given by the telephone numbers. [14] The number of perfect matchings in a graph is also known as the hafnian of its adjacency ...
Kőnig had announced in 1914 and published in 1916 the results that every regular bipartite graph has a perfect matching, [11] and more generally that the chromatic index of any bipartite graph (that is, the minimum number of matchings into which it can be partitioned) equals its maximum degree [12] – the latter statement is known as Kőnig's ...
However, counting the number of perfect matchings, even in bipartite graphs, is #P-complete. This is because computing the permanent of an arbitrary 0–1 matrix (another #P-complete problem) is the same as computing the number of perfect matchings in the bipartite graph having the given matrix as its biadjacency matrix.
[5] [6] Proof: L(G) can be seen as an n-by-n array of cells, where each row is a vertex on one side, each column is a vertex on the other side, and each cell is an edge. In the graph L(G), each cell is a vertex, and each edge is a pair of two cells in the same column or the same row. CON starts by offering two cells in the same row; if NON ...
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 1 E minus a vector of |E| ones.
In the balanced assignment problem, both parts of the bipartite graph have the same number of vertices, denoted by n. One of the first polynomial-time algorithms for balanced assignment was the Hungarian algorithm. It is a global algorithm – it is based on improving a matching along augmenting paths (alternating paths between unmatched vertices
The matching width of a hypergraph H, denoted mw(H), is the maximum, over all matchings M in H, of the minimum size of a subset of E that pins M. [12] Since E contains all matchings in E, the width of H is obviously at least as large as the matching-width of H. Aharoni and Haxell proved the following condition: Let H = (X + Y, E) be a bipartite ...
For sparse bipartite graphs, the maximum matching problem can be solved in ~ (/) with Madry's algorithm based on electric flows. [ 3 ] For planar bipartite graphs, the problem can be solved in time O ( n log 3 n ) where n is the number of vertices, by reducing the problem to maximum flow with multiple sources and sinks.