CppAD: A C++ Algorithmic Differentiation Package
20171217

void CppAD::local::color_symmetric_cppad  (  const VectorSet &  pattern, 
CppAD::vector< size_t > &  row,  
CppAD::vector< size_t > &  col,  
CppAD::vector< size_t > &  color  
) 
CppAD algorithm for determining which rows of a symmetric sparse matrix can be computed together.
VectorSize  is a simple vector class with elements of type size_t. 
VectorSet  is a vector_of_sets class. 
pattern  [in] Is a representation of the sparsity pattern for the matrix. 
row  [in/out] is a vector specifying which row indices to compute. 
col  [in/out] is a vector, with the same size as row, that specifies which column indices to compute. Input: For each valid index k , the index pair (row[k], col[k]) must be present in the sparsity pattern. It may be that some entries in the sparsity pattern do not need to be computed; i.e, do not appear in the set of (row[k], col[k]) entries. Output: On output, some of row and column indices may have been swapped std::swap( row[k], col[k] )

color  [out] is a vector with size m. The input value of its elements does not matter. Upon return, it is a coloring for the rows of the sparse matrix. Note that if color[i] == m, then there is no index k for which row[k] == i (for the return value of row). Fix any (i, j) in the sparsity pattern. Suppose that there is a row index i1 with i1 != i, color[i1] == color[i] and (i1, j) is in the sparsity pattern. If follows that for all j1 with j1 != j and color[j1] == color[j], (j1, i ) is not in the sparsity pattern. This routine tries to minimize, with respect to the choice of colors, the maximum, with respect to k, of color[ row[k] ] . 
Definition at line 81 of file color_symmetric.hpp.
Referenced by CppAD::ADFun< Base >::sparse_hes(), and CppAD::ADFun< Base >::SparseHessianCompute().