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<h2><img src="figs/python.gif" alt="(Python)"><span class=
"refentrytitle">Function kamada_kawai_spring_layout</span></h2>
<p>boost::kamada_kawai_spring_layout &mdash; Kamada-Kawai spring
layout for connected, undirected graphs.</p>
</div>
<h2 xmlns:rev=
"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
"refsynopsisdiv-title">Synopsis</h2>
<div xmlns:rev=
"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
"refsynopsisdiv">
<pre class="synopsis">
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> Topology, <span class=
"bold"><b>typename</b></span> Graph, <span class=
"bold"><b>typename</b></span> PositionMap, <span class=
"bold"><b>typename</b></span> WeightMap, <span class=
"bold"><b>typename</b></span> T, 
         <span class=
"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
"bold"><b>typename</b></span> Done, <span class=
"bold"><b>typename</b></span> VertexIndexMap, 
         <span class=
"bold"><b>typename</b></span> DistanceMatrix, <span class=
"bold"><b>typename</b></span> SpringStrengthMatrix, 
         <span class=
"bold"><b>typename</b></span> PartialDerivativeMap&gt; 
  <span class="type"><span class=
"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph &amp; g, PositionMap position, 
                                  WeightMap weight, 
                                  <b>const</b> Topology&amp; space,
                                  <span class=
"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done, 
                                  <span class=
"bold"><b>typename</b></span> property_traits&lt; WeightMap &gt;::value_type spring_constant, 
                                  VertexIndexMap index, 
                                  DistanceMatrix distance, 
                                  SpringStrengthMatrix spring_strength, 
                                  PartialDerivativeMap partial_derivatives);
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> Topology, <span class=
"bold"><b>typename</b></span> Graph, <span class=
"bold"><b>typename</b></span> PositionMap, <span class=
"bold"><b>typename</b></span> WeightMap, <span class=
"bold"><b>typename</b></span> T, 
         <span class=
"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
"bold"><b>typename</b></span> Done, <span class=
"bold"><b>typename</b></span> VertexIndexMap&gt; 
  <span class="type"><span class=
"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph &amp; g, PositionMap position, 
                                  WeightMap weight, 
                                  <b>const</b> Topology&amp; space,
                                  <span class=
"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done, 
                                  <span class=
"bold"><b>typename</b></span> property_traits&lt; WeightMap &gt;::value_type spring_constant, 
                                  VertexIndexMap index);
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> Topology, <span class=
"bold"><b>typename</b></span> Graph, <span class=
"bold"><b>typename</b></span> PositionMap, <span class=
"bold"><b>typename</b></span> WeightMap, <span class=
"bold"><b>typename</b></span> T, 
         <span class=
"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
"bold"><b>typename</b></span> Done&gt; 
  <span class="type"><span class=
"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph &amp; g, PositionMap position, 
                                  WeightMap weight, 
                                  <b>const</b> Topology&amp; space,
                                  <span class=
"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done, 
                                  <span class=
"bold"><b>typename</b></span> property_traits&lt; WeightMap &gt;::value_type spring_constant = typename property_traits&lt; WeightMap &gt;::value_type(1));
<span class="bold"><b>template</b></span>&lt;<span class=
"bold"><b>typename</b></span> Topology, <span class=
"bold"><b>typename</b></span> Graph, <span class=
"bold"><b>typename</b></span> PositionMap, <span class=
"bold"><b>typename</b></span> WeightMap, <span class=
"bold"><b>typename</b></span> T, 
         <span class=
"bold"><b>bool</b></span> EdgeOrSideLength&gt; 
  <span class="type"><span class=
"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph &amp; g, PositionMap position, 
                                  WeightMap weight, 
                                  <b>const</b> Topology&amp; space,
                                  <span class=
"emphasis"><em>unspecified</em></span> edge_or_side_length);
</pre></div>
<div class="refsect1" lang="en"><a name="id822881" id=
"id822881"></a>
<h2>Where Defined</h2>
<a href=
"../../../boost/graph/kamada_kawai_spring_layout.hpp">boost/graph/kamada_kawai_spring_layout.hpp</a>

<h2>Description</h2>
<p>This algorithm&nbsp;[<a href=
"bibliography.html#kamada89">57</a>] performs graph layout (in two
dimensions) for connected, undirected graphs. It operates by
relating the layout of graphs to a dynamic spring system and
minimizing the energy within that system. The strength of a spring
between two vertices is inversely proportional to the square of the
shortest distance (in graph terms) between those two vertices.
Essentially, vertices that are closer in the graph-theoretic sense
(i.e., by following edges) will have stronger springs and will
therefore be placed closer together.</p>
<p>Prior to invoking this algorithm, it is recommended that the
vertices be placed along the vertices of a regular n-sided polygon
via <a href="circle_layout.html"><tt>circle_layout</tt></a>.</p>
<p><b xmlns:rev=
"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision"><span class="term">
Returns</span></b>: <tt class="computeroutput">true</tt> if layout
was successful or <tt class="computeroutput">false</tt> if a
negative weight cycle was detected or the graph is
disconnected.</p>

<h2>Parameters</h2>
IN: <tt>const Graph&amp; g</tt>
<blockquote>
  The graph, whose type <tt>Graph</tt> must model the 
  <a href="VertexListGraph.html">VertexListGraph</a>, 
  <a href="EdgeListGraph.html">EdgeListGraph</a>, and 
  <a href="IncidenceGraph.html">IncidenceGraph</a> concepts. The
  graph must be undirected and connected. <br>
  <b>Python</b>: This parameter is named <tt>graph</tt> in Python.
</blockquote>

OUT: <tt>PositionMap position</tt>
<blockquote>
  This property map is used to store the position of each vertex.  The
  type <tt>PositionMap</tt> must be a model of <a
  href="../../property_map/doc/WritablePropertyMap.html">Writable Property
  Map</a>, with the graph's vertex descriptor type as its key type and
  <tt>Topology::point_type</tt> as its value type.<br>

  <b>Python</b>: The position map must be a <tt>vertex_point2d_map</tt> for
  the graph.<br>
  <b>Python default</b>: <tt>graph.get_vertex_point2d_map("position")</tt>
</blockquote>

IN: <tt>weight_map(WeightMap w_map)</tt>   
<blockquote>
  The weight or ``length'' of each edge in the graph. The weights
  must all be non-negative, and the algorithm will throw a
  <a href="./exception.html#negative_edge"><tt>negative_edge</tt></a> 
  exception is one of the edges is negative.
  The type <tt>WeightMap</tt> must be a model of
  <a href="../../property_map/doc/ReadablePropertyMap.html">Readable Property Map</a>. The edge descriptor type of
  the graph needs to be usable as the key type for the weight
  map. The value type for this map must be
  the same as the value type of the distance map.<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>

IN: <tt>const Topology&amp; space</tt>
<blockquote>
The topology used to lay out the vertices.  This parameter describes both the
size and shape of the layout area, as well as its dimensionality; up to three
dimensions are supported by the current implementation.  Topologies are
described in more detail
(with a list of BGL-provided topologies) <a href="topology.html">in separate
documentation</a>.
</blockquote>

IN: <tt>EdgeOrSideLength edge_or_side_length</tt>
<blockquote>
Provides either the unit length <tt class= "computeroutput">e</tt> of
an edge in the layout or the length of a side <tt
class="computeroutput">s</tt> of the display area, and must be
either <tt class= "computeroutput">boost::edge_length(e)</tt> or <tt
class= "computeroutput">boost::side_length(s)</tt> , respectively.
<b>Python</b>: In Python, this value always refers to the side length
and may only be a <tt>double</tt>.
</blockquote>

IN: <tt>Done done</tt>
<blockquote>
A 4-argument function object that is passed the current
value of delta_p (i.e., the energy of vertex <tt class=
"computeroutput">p</tt> ), the vertex <tt class=
"computeroutput">p</tt> , the graph <tt class=
"computeroutput">g</tt> , and a boolean flag indicating whether
<tt class="computeroutput">delta_p</tt> is the maximum energy in
the system (when <tt class="computeroutput">true</tt> ) or the
energy of the vertex being moved.
<b>Default</b>: <a href=
"layout_tolerance.html"><tt class=
"computeroutput">layout_tolerance</tt></a> instantiated over the
value type of the weight map.<br>
<b>Python</b>: Any callable Python object with an appropriate signature suffices. 
</blockquote>

IN: <tt>typename property_traits&lt;WeightMap&gt;::value_type spring_constant</tt>
<blockquote>
The constant multiplied by each spring's strength.
Larger values create systems with more energy that can take longer
to stabilize; smaller values create systems with less energy that
stabilize quickly but do not necessarily result in pleasing
layouts.<br>
<b>Default</b>: 1.
</blockquote>

IN: <tt>VertexIndexMap index</tt>
<blockquote>
As a mapping from vertices to index values between 0 and
<tt class="computeroutput">num_vertices(g)</tt> .<br>
<b>Default</b>:<tt class="computeroutput">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>adjacency_list</tt> with <tt>VertexList=listS</tt> does
    not have an internal <tt>vertex_index</tt> property.
   <br>
<b>Python</b>: Unsupported parameter.
</blockquote>

UTIL/OUT: <tt>DistanceMap distance</tt>
<blockquote>
This parameter will be used to store the distance from every vertex to
every other vertex, which is computed in the first stages of the
algorithm. This value's type must be a model of <a
href="BasicMatrix.html"><tt>BasicMatrix</tt></a> with value type equal to
the value type of the weight map.<br>
<b>Default</b>: A vector of vectors.<br>
<b>Python</b>: Unsupported parameter.
</blockquote>

UTIL/OUT: <tt>SpringStrengthMatrix spring_strength</tt>
<blockquote>

This matrix will be used to store the strength of the spring between
every pair of vertices. This value's type must be a model of <a
href="BasicMatrix.html">BasicMatrix</a> with value type equal to the
value type of the weight map.<br>
<b>Default</b>: A vector of vectors of the value type of the weight map.<br>
<b>Python</b>: Unsupported parameter.
</blockquote>

UTIL: <tt>PartialDerivativeMap partial_derivatives</tt>
<blockquote>
A property map that will be used to store the partial derivatives of
each vertex with respect to the vertex's current coordinates.
coordinates. This must be a
<a href="../../property_map/doc/ReadWritePropertyMap.html">Read/Write
Property Map</a> whose value type is <tt>Topology::point_difference_type</tt>.
The default is an iterator property map built using the graph's vertex index
map.<br>
<b>Default</b>: An <tt>iterator_property_map</tt> created from
an <tt>std::vector</tt> of <tt>Topology::point_difference_type</tt>.<br>
<b>Python</b>: Unsupported parameter.
</blockquote>

<b>Python</b> IN: <tt>bool progressive</tt>
<blockquote>
  When <tt>false</tt>, performs layout of the graph on a circle before
  running the Kamada-Kawai algorithm. If <tt>true</tt>, the algorithm
  is executing starting with the vertex configuration in
  the <tt>position</tt> map.<br>
  <b>Default</b>: <tt>False</tt>.
</blockquote>

</div>
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<td nowrap>Copyright &copy; 2004, 2010 Trustees of Indiana University</td>
<td><a href="http://www.boost.org/people/doug_gregor.html">Douglas Gregor</a>,
Indiana University (dgregor -at cs.indiana.edu)<br>
<a href="http://www.osl.iu.edu/~lums">Andrew Lumsdaine</a>, Indiana
University (<a href=
"mailto:lums@osl.iu.edu">lums@osl.iu.edu</a>)</td>
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	31	<hr>
	32	<div class="refentry" lang="en"><a name="id103831-bb" id=
	33	"id103831-bb"></a>
	34	<div class="titlepage"></div>
	35	<div class="refnamediv">
	36	<h2><img src="figs/python.gif" alt="(Python)"><span class=
	37	"refentrytitle">Function kamada_kawai_spring_layout</span></h2>
	38	<p>boost::kamada_kawai_spring_layout — Kamada-Kawai spring
	39	layout for connected, undirected graphs.</p>
	40	</div>
	41	<h2 xmlns:rev=
	42	"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
	43	"refsynopsisdiv-title">Synopsis</h2>
	44	<div xmlns:rev=
	45	"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" class=
	46	"refsynopsisdiv">
	47	<pre class="synopsis">
	48	<span class="bold"><b>template</b></span><<span class=
	49	"bold"><b>typename</b></span> Topology, <span class=
	50	"bold"><b>typename</b></span> Graph, <span class=
	51	"bold"><b>typename</b></span> PositionMap, <span class=
	52	"bold"><b>typename</b></span> WeightMap, <span class=
	53	"bold"><b>typename</b></span> T,
	54	<span class=
	55	"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
	56	"bold"><b>typename</b></span> Done, <span class=
	57	"bold"><b>typename</b></span> VertexIndexMap,
	58	<span class=
	59	"bold"><b>typename</b></span> DistanceMatrix, <span class=
	60	"bold"><b>typename</b></span> SpringStrengthMatrix,
	61	<span class=
	62	"bold"><b>typename</b></span> PartialDerivativeMap>
	63	<span class="type"><span class=
	64	"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph & g, PositionMap position,
65	WeightMap weight,
66	<b>const</b> Topology& space,
67	<span class=
68	"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done,
69	<span class=
70	"bold"><b>typename</b></span> property_traits< WeightMap >::value_type spring_constant,
71	VertexIndexMap index,
72	DistanceMatrix distance,
73	SpringStrengthMatrix spring_strength,
74	PartialDerivativeMap partial_derivatives);
75	<span class="bold"><b>template</b></span><<span class=
76	"bold"><b>typename</b></span> Topology, <span class=
77	"bold"><b>typename</b></span> Graph, <span class=
78	"bold"><b>typename</b></span> PositionMap, <span class=
79	"bold"><b>typename</b></span> WeightMap, <span class=
80	"bold"><b>typename</b></span> T,
81	<span class=
82	"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
83	"bold"><b>typename</b></span> Done, <span class=
84	"bold"><b>typename</b></span> VertexIndexMap>
85	<span class="type"><span class=
86	"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph & g, PositionMap position,
87	WeightMap weight,
88	<b>const</b> Topology& space,
89	<span class=
90	"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done,
91	<span class=
92	"bold"><b>typename</b></span> property_traits< WeightMap >::value_type spring_constant,
93	VertexIndexMap index);
94	<span class="bold"><b>template</b></span><<span class=
95	"bold"><b>typename</b></span> Topology, <span class=
96	"bold"><b>typename</b></span> Graph, <span class=
97	"bold"><b>typename</b></span> PositionMap, <span class=
98	"bold"><b>typename</b></span> WeightMap, <span class=
99	"bold"><b>typename</b></span> T,
100	<span class=
101	"bold"><b>bool</b></span> EdgeOrSideLength, <span class=
102	"bold"><b>typename</b></span> Done>
103	<span class="type"><span class=
104	"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph & g, PositionMap position,
105	WeightMap weight,
106	<b>const</b> Topology& space,
107	<span class=
108	"emphasis"><em>unspecified</em></span> edge_or_side_length, Done done,
109	<span class=
110	"bold"><b>typename</b></span> property_traits< WeightMap >::value_type spring_constant = typename property_traits< WeightMap >::value_type(1));
111	<span class="bold"><b>template</b></span><<span class=
112	"bold"><b>typename</b></span> Topology, <span class=
113	"bold"><b>typename</b></span> Graph, <span class=
114	"bold"><b>typename</b></span> PositionMap, <span class=
115	"bold"><b>typename</b></span> WeightMap, <span class=
116	"bold"><b>typename</b></span> T,
117	<span class=
118	"bold"><b>bool</b></span> EdgeOrSideLength>
119	<span class="type"><span class=
120	"bold"><b>bool</b></span></span> kamada_kawai_spring_layout(<span class="bold"><b>const</b></span> Graph & g, PositionMap position,
121	WeightMap weight,
122	<b>const</b> Topology& space,
123	<span class=
124	"emphasis"><em>unspecified</em></span> edge_or_side_length);
125	</pre></div>
126	<div class="refsect1" lang="en"><a name="id822881" id=
127	"id822881"></a>
128	<h2>Where Defined</h2>
129	<a href=
130	"../../../boost/graph/kamada_kawai_spring_layout.hpp">boost/graph/kamada_kawai_spring_layout.hpp</a>
131
132	<h2>Description</h2>
133	<p>This algorithm [<a href=
134	"bibliography.html#kamada89">57</a>] performs graph layout (in two
135	dimensions) for connected, undirected graphs. It operates by
136	relating the layout of graphs to a dynamic spring system and
137	minimizing the energy within that system. The strength of a spring
138	between two vertices is inversely proportional to the square of the
139	shortest distance (in graph terms) between those two vertices.
140	Essentially, vertices that are closer in the graph-theoretic sense
141	(i.e., by following edges) will have stronger springs and will
142	therefore be placed closer together.</p>
143	<p>Prior to invoking this algorithm, it is recommended that the
144	vertices be placed along the vertices of a regular n-sided polygon
145	via <a href="circle_layout.html"><tt>circle_layout</tt></a>.</p>
146	<p><b xmlns:rev=
147	"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision"><span class="term">
148	Returns</span></b>: <tt class="computeroutput">true</tt> if layout
149	was successful or <tt class="computeroutput">false</tt> if a
150	negative weight cycle was detected or the graph is
151	disconnected.</p>
152
153	<h2>Parameters</h2>
154	IN: <tt>const Graph& g</tt>
155	<blockquote>
156	The graph, whose type <tt>Graph</tt> must model the
157	<a href="VertexListGraph.html">VertexListGraph</a>,
158	<a href="EdgeListGraph.html">EdgeListGraph</a>, and
159	<a href="IncidenceGraph.html">IncidenceGraph</a> concepts. The
160	graph must be undirected and connected. <br>
161	<b>Python</b>: This parameter is named <tt>graph</tt> in Python.
162	</blockquote>
163
164	OUT: <tt>PositionMap position</tt>
165	<blockquote>
166	This property map is used to store the position of each vertex. The
167	type <tt>PositionMap</tt> must be a model of <a
168	href="../../property_map/doc/WritablePropertyMap.html">Writable Property
169	Map</a>, with the graph's vertex descriptor type as its key type and
170	<tt>Topology::point_type</tt> as its value type.<br>
171
172	<b>Python</b>: The position map must be a <tt>vertex_point2d_map</tt> for
173	the graph.<br>
174	<b>Python default</b>: <tt>graph.get_vertex_point2d_map("position")</tt>
175	</blockquote>
176
177	IN: <tt>weight_map(WeightMap w_map)</tt>
178	<blockquote>
179	The weight or ``length'' of each edge in the graph. The weights
180	must all be non-negative, and the algorithm will throw a
181	<a href="./exception.html#negative_edge"><tt>negative_edge</tt></a>
182	exception is one of the edges is negative.
183	The type <tt>WeightMap</tt> must be a model of
184	<a href="../../property_map/doc/ReadablePropertyMap.html">Readable Property Map</a>. The edge descriptor type of
185	the graph needs to be usable as the key type for the weight
186	map. The value type for this map must be
187	the same as the value type of the distance map.<br>
188	<b>Default:</b> <tt>get(edge_weight, g)</tt><br>
189
190	<b>Python</b>: Must be an <tt>edge_double_map</tt> for the graph.<br>
191	<b>Python default</b>: <tt>graph.get_edge_double_map("weight")</tt>
192	</blockquote>
193
194	IN: <tt>const Topology& space</tt>
195	<blockquote>
196	The topology used to lay out the vertices. This parameter describes both the
197	size and shape of the layout area, as well as its dimensionality; up to three
198	dimensions are supported by the current implementation. Topologies are
199	described in more detail
200	(with a list of BGL-provided topologies) <a href="topology.html">in separate
201	documentation</a>.
202	</blockquote>
203
204	IN: <tt>EdgeOrSideLength edge_or_side_length</tt>
205	<blockquote>
206	Provides either the unit length <tt class= "computeroutput">e</tt> of
207	an edge in the layout or the length of a side <tt
208	class="computeroutput">s</tt> of the display area, and must be
209	either <tt class= "computeroutput">boost::edge_length(e)</tt> or <tt
210	class= "computeroutput">boost::side_length(s)</tt> , respectively.
211	<b>Python</b>: In Python, this value always refers to the side length
212	and may only be a <tt>double</tt>.
213	</blockquote>
214
215	IN: <tt>Done done</tt>
216	<blockquote>
217	A 4-argument function object that is passed the current
218	value of delta_p (i.e., the energy of vertex <tt class=
219	"computeroutput">p</tt> ), the vertex <tt class=
220	"computeroutput">p</tt> , the graph <tt class=
221	"computeroutput">g</tt> , and a boolean flag indicating whether
222	<tt class="computeroutput">delta_p</tt> is the maximum energy in
223	the system (when <tt class="computeroutput">true</tt> ) or the
224	energy of the vertex being moved.
225	<b>Default</b>: <a href=
226	"layout_tolerance.html"><tt class=
227	"computeroutput">layout_tolerance</tt></a> instantiated over the
228	value type of the weight map.<br>
229	<b>Python</b>: Any callable Python object with an appropriate signature suffices.
230	</blockquote>
231
232	IN: <tt>typename property_traits<WeightMap>::value_type spring_constant</tt>
233	<blockquote>
234	The constant multiplied by each spring's strength.
235	Larger values create systems with more energy that can take longer
236	to stabilize; smaller values create systems with less energy that
237	stabilize quickly but do not necessarily result in pleasing
238	layouts.<br>
239	<b>Default</b>: 1.
240	</blockquote>
241
242	IN: <tt>VertexIndexMap index</tt>
243	<blockquote>
244	As a mapping from vertices to index values between 0 and
245	<tt class="computeroutput">num_vertices(g)</tt> .<br>
246	<b>Default</b>:<tt class="computeroutput">get(vertex_index,g)</tt>.
247	Note: if you use this default, make sure your graph has
248	an internal <tt>vertex_index</tt> property. For example,
249	<tt>adjacency_list</tt> with <tt>VertexList=listS</tt> does
250	not have an internal <tt>vertex_index</tt> property.
251	<br>
252	<b>Python</b>: Unsupported parameter.
253	</blockquote>
254
255	UTIL/OUT: <tt>DistanceMap distance</tt>
256	<blockquote>
257	This parameter will be used to store the distance from every vertex to
258	every other vertex, which is computed in the first stages of the
259	algorithm. This value's type must be a model of <a
260	href="BasicMatrix.html"><tt>BasicMatrix</tt></a> with value type equal to
261	the value type of the weight map.<br>
262	<b>Default</b>: A vector of vectors.<br>
263	<b>Python</b>: Unsupported parameter.
264	</blockquote>
265
266	UTIL/OUT: <tt>SpringStrengthMatrix spring_strength</tt>
267	<blockquote>
268
269	This matrix will be used to store the strength of the spring between
270	every pair of vertices. This value's type must be a model of <a
271	href="BasicMatrix.html">BasicMatrix</a> with value type equal to the
272	value type of the weight map.<br>
273	<b>Default</b>: A vector of vectors of the value type of the weight map.<br>
274	<b>Python</b>: Unsupported parameter.
275	</blockquote>
276
277	UTIL: <tt>PartialDerivativeMap partial_derivatives</tt>
278	<blockquote>
279	A property map that will be used to store the partial derivatives of
280	each vertex with respect to the vertex's current coordinates.
281	coordinates. This must be a
282	<a href="../../property_map/doc/ReadWritePropertyMap.html">Read/Write
283	Property Map</a> whose value type is <tt>Topology::point_difference_type</tt>.
284	The default is an iterator property map built using the graph's vertex index
285	map.<br>
286	<b>Default</b>: An <tt>iterator_property_map</tt> created from
287	an <tt>std::vector</tt> of <tt>Topology::point_difference_type</tt>.<br>
288	<b>Python</b>: Unsupported parameter.
289	</blockquote>
290
291	<b>Python</b> IN: <tt>bool progressive</tt>
292	<blockquote>
293	When <tt>false</tt>, performs layout of the graph on a circle before
294	running the Kamada-Kawai algorithm. If <tt>true</tt>, the algorithm
295	is executing starting with the vertex configuration in
296	the <tt>position</tt> map.<br>
297	<b>Default</b>: <tt>False</tt>.
298	</blockquote>
299
300	</div>
301	</div>
302	</div>
303	<table xmlns:rev=
304	"http://www.cs.rpi.edu/~gregod/boost/tools/doc/revision" width=
305	"100%">
306	<tr>
307	<td align="left"></td>
308	<td align="right"></td>
309	</tr>
310	</table>
311	<hr>
312	<table>
313	<tr valign="top">
314	<td nowrap>Copyright © 2004, 2010 Trustees of Indiana University</td>
315	<td><a href="http://www.boost.org/people/doug_gregor.html">Douglas Gregor</a>,
316	Indiana University (dgregor -at cs.indiana.edu)<br>
317	<a href="http://www.osl.iu.edu/~lums">Andrew Lumsdaine</a>, Indiana
318	University (<a href=
319	"mailto:lums@osl.iu.edu">lums@osl.iu.edu</a>)</td>
320	</tr>
321	</table>
322	</body>
323	</html>