# Ticket #8786: trac_8786-reviewer.patch

File trac_8786-reviewer.patch, 8.2 KB (added by mvngu, 11 years ago)
• ## sage/graphs/digraph.py

```# HG changeset patch
# User Minh Van Nguyen <nguyenminh2@gmail.com>
# Date 1272458371 25200
# Node ID 9188e33e33ed5708fcccff34d429d701dec3baaa
# Parent  5af3155634cd101e69191b123948f78e6981b931
#8786: reviewer patch: autogenerated broken links to Wikipedia

diff --git a/sage/graphs/digraph.py b/sage/graphs/digraph.py```
 a Directed graph. A digraph or directed graph is a set of vertices connected by oriented edges. edges. For more information, see the `Wikipedia article on digraphs `_ `_. One can very easily create a directed graph in Sage by typing:: The minimum feedback edge set of a digraph is a set of edges that intersect all the circuits of the digraph. Equivalently, a minimum feedback arc set of a DiGraph is a set `S` of arcs such that the digraph `G-S` is acyclic. `Wikipedia article on Feedback arc sets `S` of arcs such that the digraph `G-S` is acyclic. For more information, see the `Wikipedia article on feedback arc sets `_. INPUT : The minimum feedback vertex set of a digraph is a set of vertices that intersect all the circuits of the digraph. Equivalently, a minimum feedback vertex set of a DiGraph is a set `S` of vertices such that the digraph `G-S` is acyclic. `Wikipedia article on Feedback vertex sets `S` of vertices such that the digraph `G-S` is acyclic. For more information, see the `Wikipedia article on feedback vertex sets `_. INPUT :
• ## sage/graphs/digraph_generators.py

`diff --git a/sage/graphs/digraph_generators.py b/sage/graphs/digraph_generators.py`
 a In this digraph, there is an arc `w_1w_2` if `w_2` can be obtained from `w_1` by removing the leftmost letter and adding a new letter at its right end. `Wikipedia article on the De Bruijn graph `_ For more information, see the `Wikipedia article on De Bruijn graph `_. INPUT:
• ## sage/graphs/generic_graph.py

`diff --git a/sage/graphs/generic_graph.py b/sage/graphs/generic_graph.py`
 a A minimum edge cut between two vertices `s` and `t` of self is a set `A` of edges of minimum weight such that the graph obtained by removing `A` from self is disconnected. obtained by removing `A` from self is disconnected. For more information, see the `Wikipedia article on cuts `_. A vertex cut between two non adjacent vertices is a set `U` of vertices of self such that the graph obtained by removing `U` from self is disconnected. `U` from self is disconnected. For more information, see the `Wikipedia article on cuts `_ `_. INPUT: A minimum vertex cover of a graph is a set `S` of vertices such that each edge is incident to at least one element of `S`, and such that `S` is of minimum cardinality. cardinality. For more information, see the `Wikipedia article on vertex cover `_. The minimum feedback edge set of a digraph is a set of edges that intersect all the circuits of the digraph. Equivalently, a minimum feedback arc set of a DiGraph is a set `S` of arcs such that the digraph `G-S` is acyclic. `S` of arcs such that the digraph `G-S` is acyclic. For more information, see the `Wikipedia article on feedback arc sets `_. The minimum feedback vertex set of a digraph is a set of vertices that intersect all the circuits of the digraph. Equivalently, a minimum feedback vertex set of a DiGraph is a set `S` of vertices such that the digraph `G-S` is acyclic. `Wikipedia article on Feedback vertex sets `S` of vertices such that the digraph `G-S` is acyclic. For more information, see the `Wikipedia article on feedback vertex sets `_. INPUT : def max_cut(self,value_only=True,use_edge_labels=True, vertices=False): r""" Returns a maximum edge cut of the graph Returns a maximum edge cut of the graph. For more information, see the `Wikipedia article on cuts `_. def flow(self,x,y,value_only=True,integer=False, use_edge_labels=True,vertex_bound=False): r""" Returns a maximum flow in the graph from ``x`` to ``y`` represented by an optimal valuation of the edges. `Wikipedia article on flows `_. represented by an optimal valuation of the edges. For more information, see the `Wikipedia article on maximum flow `_. As an optimization problem, is can be expressed this way : def matching(self,value_only=False, use_edge_labels=True): r""" Returns a maximum weighted matching of the graph represented by the list of its edges. represented by the list of its edges. For more information, see the `Wikipedia article on matchings `_. `_. Given a graph `G` such that each edge `e` has a weight `w_e`, a maximum matching is a subset `S` of the edges of `G` of def dominating_set(self, independent=False, value_only=False,log=0): r""" Returns a minimum dominating set of the graph represented by the list of its vertices. represented by the list of its vertices. For more information, see the `Wikipedia article on dominating sets `_. def edge_connectivity(self,value_only=True,use_edge_labels=False, vertices=False): r""" Returns the edge connectivity of the graph Returns the edge connectivity of the graph. For more information, see the `Wikipedia article on connectivity `_. def vertex_connectivity(self,value_only=True, sets=False): r""" Returns the vertex connectivity of the graph Returns the vertex connectivity of the graph. For more information, see the `Wikipedia article on connectivity `_. INPUT: Performs a Lex BFS on the graph. A Lex BFS ( or Lexicographic Breadth-First Search ) is a Breadth First Search used for the recognition of Chordal Graphs. First Search used for the recognition of Chordal Graphs. For more information, see the `Wikipedia article on Lex-BFS `_ `_. INPUT: