Sentence examples for minimum weighted edge from inspiring English sources

Having variables in the order of O ( | S × S | ) and only searching for a minimum weighted (edge and node) subgraph makes up for a very difficult problem.

After that, graph clustering is used to find the best cut point that cuts the minimum weighted edge as shown in Fig. 3c, where vertical and horizontal dashed lines are the best cut points that cut minimum weighted edges of attributes (A_1) and (A_2), respectively (see "Graph clustering-based discretization algorithm" for details).

To address this issue, [ 14] suggested using alternative measures of performance such as minimum weighted edge removal (MWER).

Aguiar and Istrail (2012) introduced a new graph data structure, algorithmic framework and the minimum weighted edge removal (MWER) optimization, which together have several advantages over existing methods.

The CNDP is related to a variety of other graph partitioning problems in the literature; for instance, the minimum multi-cut problem, which aims to separate a set of source-sink pairs by removing a subset of minimum weighted edges.

Example of a the scatter plot of the toy dataset, b the proposed graph representation of data points, and c the cut point selections that cut the minimum weighted edges.

This paper considers the edge covering problem under fuzzy environment, and formulates three models which are expected minimum weight edge cover model, α-minimum weight edge cover model, and the most minimum weight edge cover model.

The HapCompass model defined in Aguiar and Istrail (2012) is composed of the compass graph G C core data structure, which summarizes the rows of M using edges weights and the MWER optimization that aims to remove a minimum weighted set of edges from G C such that a unique phasing may be constructed.

(3) It then constructs an induced subgraph (G_O(V_O, E_O)) from G. (4) It finds a minimum weighted perfect matching M from (G_O), where a perfect matching M is a set of edges that do not have any common nodes.

Then we find a perfect minimum weighted matching for H, i.e. a perfect matching where the sum of weights of the edges in the matching has the minimum possible value by using the well-known Hungarian algorithm (Munkres, 1957).

Minimum Weighted Edit-Distance.