# Boost C++ Libraries

...one of the most highly regarded and expertly designed C++ library projects in the world.

# Constructing graph algorithms with BGL

The main goal of BGL is not to provide a nice graph class, or to provide a comprehensive set of reusable graph algorithms (though these are goals). The main goal of BGL is to encourage others to write reusable graph algorithms. By reusable we mean maximally reusable. Generic programming is a methodology for making algorithms maximally reusable, and in this section we will discuss how to apply generic programming to constructing graph algorithms.

To illustrate the generic programming process we will step though the construction of a graph coloring algorithm. The graph coloring problem (or more specifically, the vertex coloring problem) is to label each vertex in a graph G with a color such that no two adjacent vertices are labeled with the same color and such that the minimum number of colors are used. In general, the graph coloring problem is NP-complete, and therefore it is impossible to find an optimal solution in a reasonable amount of time. However, there are many algorithms that use heuristics to find colorings that are close to the minimum.

The particular algorithm we present here is based on the linear time SEQ subroutine that is used in the estimation of sparse Jacobian and Hessian matrices [9,7,6]. This algorithm visits all of the vertices in the graph according to the order defined by the input order. At each vertex the algorithm marks the colors of the adjacent vertices, and then chooses the smallest unmarked color for the color of the current vertex. If all of the colors are already marked, a new color is created. A color is considered marked if its mark number is equal to the current vertex number. This saves the trouble of having to reset the marks for each vertex. The effectiveness of this algorithm is highly dependent on the input vertex order. There are several ordering algorithms, including the largest-first [31], smallest-last [29], and incidence degree [32] algorithms, which improve the effectiveness of this coloring algorithm.

The first decision to make when constructing a generic graph algorithm is to decide what graph operations are necessary for implementing the algorithm, and which graph concepts the operations map to. In this algorithm we will need to traverse through all of the vertices to intialize the vertex colors. We also need to access the adjacent vertices. Therefore, we will choose the VertexListGraph concept because it is the minimum concept that includes these operations. The graph type will be parameterized in the template function for this algorithm. We do not restrict the graph type to a particular graph class, such as the BGL adjacency_list, for this would drastically limit the reusability of the algorithm (as most algorithms written to date are). We do restrict the graph type to those types that model VertexListGraph. This is enforced by the use of those graph operations in the algorithm, and furthermore by our explicit requirement added as a concept check with function_requires() (see Section Concept Checking for more details about concept checking).

Next we need to think about what vertex or edge properties will be used in the algorithm. In this case, the only property is vertex color. The most flexible way to specify access to vertex color is to use the propery map interface. This gives the user of the algorithm the ability to decide how they want to store the properties. Since we will need to both read and write the colors we specify the requirements as ReadWritePropertyMap. The key_type of the color map must be the vertex_descriptor from the graph, and the value_type must be some kind of integer. We also specify the interface for the order parameter as a property map, in this case a ReadablePropertyMap. For order, the key_type is an integer offset and the value_type is a vertex_descriptor. Again we enforce these requirements with concept checks. The return value of this algorithm is the number of colors that were needed to color the graph, hence the return type of the function is the graph's vertices_size_type. The following code shows the interface for our graph algorithm as a template function, the concept checks, and some typedefs. The implementation is straightforward, the only step not discussed above is the color initialization step, where we set the color of all the vertices to ``uncolored''.

```namespace boost {
template <class VertexListGraph, class Order, class Color>
typename graph_traits<VertexListGraph>::vertices_size_type
sequential_vertex_color_ting(const VertexListGraph& G,
Order order, Color color)
{
typedef graph_traits<VertexListGraph> GraphTraits;
typedef typename GraphTraits::vertex_descriptor vertex_descriptor;
typedef typename GraphTraits::vertices_size_type size_type;
typedef typename property_traits<Color>::value_type ColorType;
typedef typename property_traits<Order>::value_type OrderType;

function_requires< VertexListGraphConcept<VertexListGraph> >();
function_requires< IntegerConcept<ColorType> >();
typedef typename same_type<OrderType, vertex_descriptor>::type req_same;

size_type max_color = 0;
const size_type V = num_vertices(G);
std::vector<size_type>
mark(V, numeric_limits_max(max_color));

typename GraphTraits::vertex_iterator v, vend;
for (tie(v, vend) = vertices(G); v != vend; ++v)
color[*v] = V - 1; // which means "not colored"

for (size_type i = 0; i < V; i++) {
vertex_descriptor current = order[i];

// mark all the colors of the adjacent vertices
for (tie(ai, aend) = adjacent_vertices(current, G); ai != aend; ++ai)
mark[color[*ai]] = i;

// find the smallest color unused by the adjacent vertices
size_type smallest_color = 0;
while (smallest_color < max_color && mark[smallest_color] == i)
++smallest_color;

// if all the colors are used up, increase the number of colors
if (smallest_color == max_color)
++max_color;

color[current] = smallest_color;
}
return max_color;
}
} // namespace boost
```

 Copyright © 2000-2001 Jeremy Siek, Indiana University (jsiek@osl.iu.edu) Lie-Quan Lee, Indiana University (llee@cs.indiana.edu) Andrew Lumsdaine, Indiana University (lums@osl.iu.edu)