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     Copyright (c) Jeremy Siek, Lie-Quan Lee, and Andrew Lumsdaine  2000
    
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<Head>
<Title>Graph Coloring Example</Title>
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        ALINK="#ff0000"> 
<IMG SRC="../../../boost.png" 
     ALT="C++ Boost" width="277" height="86"> 

<BR Clear>

<H1>Graph Coloring</H1>

The graph (or vertex) coloring problem, which involves assigning
colors to vertices in a graph such that adjacenct vertices have
distinct colors, arises in a number of scientific and engineering
applications such as scheduling&nbsp;, register allocation&nbsp;,
optimization&nbsp; and parallel numerical computation.

<P>
Mathmatically, a proper vertex coloring of an undirected graph
<i>G=(V,E)</i> is a map <i>c: V -> S</i> such that <i>c(u) != c(v)</i>
whenever there exists an edge <i>(u,v)</i> in <i>G</i>. The elements
of set <i>S</i> are called the available colors. The problem is often
to determine the minimum cardinality (the number of colors) of
<i>S</i> for a given graph <i>G</i> or to ask whether it is able to
color graph <i>G</i> with a certain number of colors. For example, how
many color do we need to color the United States on a map in such a
way that adjacent states have different color? A compiler needs to
decide whether variables and temporaries could be allocated in fixed
number of registers at some point.  If a target machine has <i>K</i>
registers, can a particular interference graph be colored with
<i>K</i> colors? (Each vertex in the interference graph represents a
temporary value; each edge indicates a pair of temporaries that cannot
be assigned to the same register.)

<P>
Another example is in the estimation of sparse Jacobian matrix by
differences in large scale nonlinear problems in optimization and
differential equations. Given a nonlinear function <i>F</i>, the
estimation of Jacobian matrix <i>J</i> can be obtained by estimating
<i>Jd</i> for suitable choices of vector <i>d</i>. Curtis, Powell and
Reid&nbsp;[<a href="bibliography.html#curtis74:_jacob">9</a>] observed that a group of columns of <i>J</i> can be
determined by one evaluation of <i>Jd</i> if no two columns in this
group have a nonzero in the same row position.  Therefore, a question
is emerged: what is the number of function evaluations need to compute
approximate Jacobian matrix?  As a matter of fact this question is the
same as to compute the minimum numbers of colors for coloring a graph
if we construct the graph in the following matter: A vertex represents
a column of <i>J</i> and there is an edge if and only if the two
column have a nonzero in the same row position.

<P>
However, coloring a general graph with the minimum number of colors
(the cardinality of set <i>S</i>) is known to be an NP-complete
problem&nbsp;[<a
href="bibliography.html#garey79:computers-and-intractability">30</a>].
One often relies on heuristics to find a solution. A widely-used
general greedy based approach is starting from an ordered vertex
enumeration <i>v<sub>1</sub>, ..., v<sub>n</sub></i> of <i>G</i>, to
assign <i>v<sub>i</sub></i> to the smallest possible color for
<i>i</i> from <i>1</i> to <i>n</i>.  In section <a
href="constructing_algorithms.html">Constructing graph
algorithms with BGL</a> we have shown how to write this algorithm in
the generic programming paradigm. However, the ordering of the
vertices dramatically affects the coloring.  The arbitrary order may
perform very poorly while another ordering may produces an optimal
coloring.  Several ordering algorithms have been studied to help the
greedy coloring heuristics including largest-first ordering,
smallest-last ordering and incidence degree ordering.

<P>
In the BGL framework,  the process of constructing/prototyping such a
ordering is fairly easy because  writing such a ordering  follows the
algorithm  description   closely.  As  an  example,   we  present  the
smallest-last ordering algorithm.

<P>
The basic idea of the smallest-last ordering&nbsp;[<a
href="bibliography.html#matula72:_graph_theory_computing">29</a>] is
as follows: Assuming that the vertices <i>v<sub>k+1</sub>, ...,
v<sub>n</sub></i> have been selected, choose <i>v<sub>k</sub></i> so
that the degree of <i>v<sub>k</sub></i> in the subgraph induced by
<i>V - {v<sub>k+1</sub>, ..., v<sub>n</sub>}</i> is minimal.

<P>
The algorithm uses a bucket sorter for the vertices in the graph where
bucket is the degree. Two vertex property maps, <TT>degree</TT> and
<TT>marker</TT>,  are used  in the  algorithm.  The former  is to  store
degree of  every vertex while the  latter is to mark  whether a vertex
has been ordered or processed during traversing adjacent vertices. The
ordering is stored in the <TT>order</TT>. The algorithm is as follows:

<UL>
<LI>put all vertices in the bucket sorter
</LI>
<LI>find the vertex <tt>node</tt> with smallest degree in the bucket sorter
</LI>
<LI>number <tt>node</tt> and traverse through its adjacent vertices to
 update its degree and the position in the bucket sorter.
</LI>
<LI>go to the step 2 until all vertices are numbered.
</LI>
</UL>

<P>
<PRE>
namespace boost {
  template &lt;class VertexListGraph, class Order, class Degree, 
            class Marker, class BucketSorter&gt;
  void 
  smallest_last_ordering(const VertexListGraph&amp; G, Order order, 
                         Degree degree, Marker marker, 
                         BucketSorter&amp; degree_buckets) {
    typedef typename graph_traits&lt;VertexListGraph&gt; GraphTraits;

    typedef typename GraphTraits::vertex_descriptor Vertex;
    //typedef typename GraphTraits::size_type size_type;
    typedef size_t size_type;

    const size_type num = num_vertices(G);
    
    typename GraphTraits::vertex_iterator v, vend;
    for (boost::tie(v, vend) = vertices(G); v != vend; ++v) {
      put(marker, *v, num);
      put(degree, *v, out_degree(*v, G));
      degree_buckets.push(*v);
    }
 
    size_type minimum_degree = 1;
    size_type current_order = num - 1;
    
    while ( 1 ) {
      typedef typename BucketSorter::stack MDStack;
      MDStack minimum_degree_stack = degree_buckets[minimum_degree];
      while (minimum_degree_stack.empty())
        minimum_degree_stack = degree_buckets[++minimum_degree];
      
      Vertex node = minimum_degree_stack.top();
      put(order, current_order, node);
      
      if ( current_order == 0 ) //find all vertices
        break;
      
      minimum_degree_stack.pop();
      put(marker, node, 0); //node has been ordered.
      
      typename GraphTraits::adjacency_iterator v, vend;
      for (boost::tie(v,vend) = adjacent_vertices(node, G); v != vend; ++v)
        
        if ( get(marker, *v) &gt; current_order ) { //*v is unordered vertex
          put(marker, *v, current_order);   //mark the columns adjacent to node
          
          //It is possible minimum degree goes down
          //Here we keep tracking it.
          put(degree, *v, get(degree, *v) - 1); 
          minimum_degree = std::min(minimum_degree, get(degree, *v)); 
          
          //update the position of *v in the bucket sorter
          degree_buckets.update(*v);
        }

      current_order--;
    }
    //at this point, get(order, i) == vertex(i, g);
  }
} // namespace boost
</PRE>

<br>
<HR>
<TABLE>
<TR valign=top>
<TD nowrap>Copyright &copy; 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>)<br>
<A HREF="http://www.boost.org/people/liequan_lee.htm">Lie-Quan Lee</A>, Indiana University (<A HREF="mailto:llee@cs.indiana.edu">llee@cs.indiana.edu</A>)<br>
<A HREF="https://homes.cs.washington.edu/~al75">Andrew Lumsdaine</A>,
Indiana University (<A
HREF="mailto:lums@osl.iu.edu">lums@osl.iu.edu</A>)
</TD></TR></TABLE>

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