Sentence examples for maximum matching in from inspiring English sources

The maximum weighted matching problem is to find a maximum matching in a given graph such that the sum of the weights of the edges in it is maximum.

We formalize the problem of finding a schedule to forward the buffered data of all the users in minimum number of transmissions in such a system as a problem of finding a maximum matching in a graph.

By leveraging the inherent parallelism available in custom hardware design, we reformulate maximum matching in terms of Boolean operations rather than matrix computations and introduce three maximum matching implementations in hardware.

In [26], an alternate strategy was proposed using the concept of structural controllability [27] to determine maximum matching in directed complex network graphs, bypassing the need to first transform to a bipartite graph.

It uses O(n3) time in-place computation of maximum matching in a bipartite graph, where the vertices are given in an array, and the existence of an edge between a pair of vertices can be checked by an oracle on demand (from problem specification) in O(1) time.

The matching number of G, denoted by (m(G)), is the size of a maximum matching in G. Let (c(G) = e(G) - n(G) + omega(G)), where (omega(G)) is the number of connected components of G. Wang and Wong [16] obtained the following bounds of the matching number.

We used the link removing algorithm proposed by Régin [ 68] to classify the links in G. Given a maximum matching M in G p, we got two orientated bipartite networks G d 1 = (X+, X-, E d 1) and G d 2 = (X+, X-, E d 2), by orientating the bipartite network G p  = (X+, X-, E).

Precision [ 48] is the ratio of the number of maximum matching nodes in training with standard database to the number of training nodes.

The critical links appear in all the maximum matchings; the redundant links never appear in any maximum matching; and the ordinary links appear in some but not all maximum matchings.

In this paper, we present coarse grained parallel graph algorithms with small message overheads that solve the following standard graph problems related to graph matching: finding maximum matchings in convex bipartite graphs, and finding maximum weight matchings in trees.

In this paper, we show how maximum matching can be reformulated in terms of Boolean operations rather than the more traditional formulations.