1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199
|
<HTML>
<!--
-- Copyright (c) Jeremy Siek 2000
--
-- Distributed under the Boost Software License, Version 1.0.
-- (See accompanying file LICENSE_1_0.txt or copy at
-- http://www.boost.org/LICENSE_1_0.txt)
-->
<Head>
<Title>Boost Graph Library: Kruskal Minimum Spanning Tree</Title>
<BODY BGCOLOR="#ffffff" LINK="#0000ee" TEXT="#000000" VLINK="#551a8b"
ALINK="#ff0000">
<IMG SRC="../../../boost.png"
ALT="C++ Boost" width="277" height="86">
<BR Clear>
<H1><A NAME="sec:kruskal">
<img src="figs/python.gif" alt="(Python)"/>
<TT>kruskal_minimum_spanning_tree</TT>
</H1>
<PRE>
template <class Graph, class OutputIterator, class P, class T, class R>
OutputIterator
kruskal_minimum_spanning_tree(Graph& g, OutputIterator tree_edges,
const bgl_named_params<P, T, R>& params = <i>all defaults</i>);
</PRE>
<P>
The <tt>kruskal_minimum_spanning_tree()</tt> function find a minimum
spanning tree (MST) in an undirected graph with weighted edges. A MST is a
set of edges that connects all the vertices in the graph where the
total weight of the edges in the tree is minimized. For more details,
see section <a
href="graph_theory_review.html#sec:minimum-spanning-tree">Minimum
Spanning Tree Problem</a>. The edges in the MST are output to the
<tt>tree_edges</tt> output iterator. This function uses Kruskal's
algorithm to compute the MST [<A
HREF="bibliography.html#kruskal56">18</A>,<A
HREF="bibliography.html#clr90">8</A>,<A
HREF="bibliography.html#tarjan83:_data_struct_network_algo">27</A>,<A
HREF="bibliography.html#graham85">15</A>].
</p>
<p>
Kruskal's algorithm starts with each vertex in a tree by itself, and
with no edges in the minimum spanning tree <i>T</i>. The algorithm
then examines each edge in the graph in order of increasing edge
weight. If an edge connects two vertices in different trees the
algorithm merges the two trees into a single tree and adds the edge to
<i>T</i>. We use the ``union by rank'' and ``path compression''
heuristics to provide fast implementations of the disjoint set
operations (<tt>MAKE-SET</tt>, <tt>FIND-SET</tt>, and
<tt>UNION-SET</tt>). The algorithm is as follows:
</p>
<pre>
KRUSKAL-MST(<i>G</i>, <i>w</i>)
<i>T := Ø</i>
<b>for</b> each vertex <i>u in V</i>
MAKE-SET(<i>DS</i>, <i>u</i>)
<b>end for</b>
<b>for</b> each edge <i>(u,v) in E</i> in order of nondecreasing weight
<b>if</b> FIND-SET(<i>DS</i>, <i>u</i>) != FIND-SET(<i>DS</i>, <i>v</i>)
UNION-SET(<i>DS</i>, <i>u</i>, <i>v</i>)
<i>T := T U {(u,v)}</i>
<b>end for</b>
<b>return</b> <i>T</i>
</pre>
<H3>Where Defined</H3>
<P>
<a href="../../../boost/graph/kruskal_min_spanning_tree.hpp"><TT>boost/graph/kruskal_min_spanning_tree.hpp</TT></a>
<P>
<h3>Parameters</h3>
IN: <tt>const Graph& g</tt>
<blockquote>
An undirected graph. The graph type must be a model of
<a href="./VertexListGraph.html">Vertex List Graph</a>
and <a href="./EdgeListGraph.html">Edge List Graph</a>.<br>
<b>Python</b>: The parameter is named <tt>graph</tt>.
</blockquote>
IN: <tt>OutputIterator spanning_tree_edges</tt>
<blockquote>
The edges of the minimum spanning tree are output to this <a
href="http://www.sgi.com/tech/stl/OutputIterator.html">Output
Iterator</a>.<br>
<b>Python</b>: This parameter is not used in Python. Instead, a
Python <tt>list</tt> containing all of the spanning tree edges is
returned.
</blockquote>
<h3>Named Parameters</h3>
IN: <tt>weight_map(WeightMap w_map)</tt>
<blockquote>
The weight or ``length'' of
each edge in the graph. The <tt>WeightMap</tt> type must be a model
of <a href="../../property_map/ReadablePropertyMap.html">Readable
Property Map</a> and its value type must be <a
href="http://www.sgi.com/tech/stl/LessThanComparable.html">Less Than
Comparable</a>. The key type of this map needs to be the graph's
edge descriptor type.<br>
<b>Default:</b> <tt>get(edge_weight, g)</tt><br>
<b>Python</b>: Must be an <tt>edge_double_map</tt> for the graph.<br>
<b>Python default</b>: <tt>graph.get_edge_double_map("weight")</tt>
</blockquote>
UTIL: <tt>rank_map(RankMap r_map)</tt>
<blockquote>
This is used by the disjoint sets data structure.
The type <tt>RankMap</tt> must be a model of <a
href="../../property_map/ReadWritePropertyMap.html">Read/Write
Property Map</a>. The vertex descriptor type of the graph needs to
be usable as the key type of the rank map. The value type of the
rank map must be an integer type.<br>
<b>Default:</b> an <a
href="../../property_map/iterator_property_map.html">
<tt>iterator_property_map</tt></a> created from a
<tt>std::vector</tt> of the integers of size
<tt>num_vertices(g)</tt> and using the <tt>i_map</tt> for the index
map.<br>
<b>Python</b>: Unsupported parameter.
</blockquote>
UTIL: <tt>predecessor_map(PredecessorMap p_map)</tt>
<blockquote>
This is used by the disjoint sets data structure, and is <b>not</b>
used for storing predecessors in the spanning tree. The predecessors
of the spanning tree can be obtained from the spanning tree edges
output. The type <tt>PredecessorMap</tt> must be a model of <a
href="../../property_map/ReadWritePropertyMap.html">Read/Write
Property Map</a>. The key type value types of the predecessor map
must be the vertex descriptor type of the graph. <br>
<b>Default:</b> an <a
href="../../property_map/iterator_property_map.html">
<tt>iterator_property_map</tt></a> created from a
<tt>std::vector</tt> of vertex descriptors of size
<tt>num_vertices(g)</tt> and using the <tt>i_map</tt> for the index
map.<br>
<b>Python</b>: Unsupported parameter.
</blockquote>
IN: <tt>vertex_index_map(VertexIndexMap i_map)</tt>
<blockquote>
This maps each vertex to an integer in the range <tt>[0,
num_vertices(g))</tt>. This is only necessary if the default is used
for the rank or predecessor maps. The type <tt>VertexIndexMap</tt>
must be a model of <a
href="../../property_map/ReadablePropertyMap.html">Readable Property
Map</a>. The value type of the map must be an integer type. The
vertex descriptor type of the graph needs to be usable as the key
type of the map.<br>
<b>Default:</b> <tt>get(vertex_index, g)</tt>
Note: if you use this default, make sure your graph has
an internal <tt>vertex_index</tt> property. For example,
<tt>adjacenty_list</tt> with <tt>VertexList=listS</tt> does
not have an internal <tt>vertex_index</tt> property.
<br>
<b>Python</b>: Unsupported parameter.
</blockquote>
<H3>Complexity</H3>
<P>
The time complexity is <i>O(E log E)</i>
<H3>Example</H3>
<P>
The file <a
href="../example/kruskal-example.cpp"><TT>examples/kruskal-example.cpp</TT></a>
contains an example of using Kruskal's algorithm.
<br>
<HR>
<TABLE>
<TR valign=top>
<TD nowrap>Copyright © 2000-2001</TD><TD>
<A HREF="http://www.boost.org/people/jeremy_siek.htm">Jeremy Siek</A>, Indiana University (<A HREF="mailto:jsiek@osl.iu.edu">jsiek@osl.iu.edu</A>)
</TD></TR></TABLE>
</BODY>
</HTML>
|