Squashed 'third_party/eigen/' changes from 61d72f6..cf794d3
Change-Id: I9b814151b01f49af6337a8605d0c42a3a1ed4c72
git-subtree-dir: third_party/eigen
git-subtree-split: cf794d3b741a6278df169e58461f8529f43bce5d
diff --git a/Eigen/src/OrderingMethods/Ordering.h b/Eigen/src/OrderingMethods/Ordering.h
index f3c31f9..7ea9b14 100644
--- a/Eigen/src/OrderingMethods/Ordering.h
+++ b/Eigen/src/OrderingMethods/Ordering.h
@@ -19,20 +19,21 @@
/** \internal
* \ingroup OrderingMethods_Module
- * \returns the symmetric pattern A^T+A from the input matrix A.
+ * \param[in] A the input non-symmetric matrix
+ * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
* FIXME: The values should not be considered here
*/
template<typename MatrixType>
-void ordering_helper_at_plus_a(const MatrixType& mat, MatrixType& symmat)
+void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat)
{
MatrixType C;
- C = mat.transpose(); // NOTE: Could be costly
+ C = A.transpose(); // NOTE: Could be costly
for (int i = 0; i < C.rows(); i++)
{
for (typename MatrixType::InnerIterator it(C, i); it; ++it)
it.valueRef() = 0.0;
}
- symmat = C + mat;
+ symmat = C + A;
}
}
@@ -44,14 +45,14 @@
*
* Functor computing the \em approximate \em minimum \em degree ordering
* If the matrix is not structurally symmetric, an ordering of A^T+A is computed
- * \tparam Index The type of indices of the matrix
+ * \tparam StorageIndex The type of indices of the matrix
* \sa COLAMDOrdering
*/
-template <typename Index>
+template <typename StorageIndex>
class AMDOrdering
{
public:
- typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
+ typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
/** Compute the permutation vector from a sparse matrix
* This routine is much faster if the input matrix is column-major
@@ -60,7 +61,7 @@
void operator()(const MatrixType& mat, PermutationType& perm)
{
// Compute the symmetric pattern
- SparseMatrix<typename MatrixType::Scalar, ColMajor, Index> symm;
+ SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
internal::ordering_helper_at_plus_a(mat,symm);
// Call the AMD routine
@@ -72,7 +73,7 @@
template <typename SrcType, unsigned int SrcUpLo>
void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
{
- SparseMatrix<typename SrcType::Scalar, ColMajor, Index> C; C = mat;
+ SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C; C = mat;
// Call the AMD routine
// m_mat.prune(keep_diag()); //Remove the diagonal elements
@@ -88,13 +89,13 @@
* Functor computing the natural ordering (identity)
*
* \note Returns an empty permutation matrix
- * \tparam Index The type of indices of the matrix
+ * \tparam StorageIndex The type of indices of the matrix
*/
-template <typename Index>
+template <typename StorageIndex>
class NaturalOrdering
{
public:
- typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
+ typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
/** Compute the permutation vector from a column-major sparse matrix */
template <typename MatrixType>
@@ -108,15 +109,17 @@
/** \ingroup OrderingMethods_Module
* \class COLAMDOrdering
*
+ * \tparam StorageIndex The type of indices of the matrix
+ *
* Functor computing the \em column \em approximate \em minimum \em degree ordering
* The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
*/
-template<typename Index>
+template<typename StorageIndex>
class COLAMDOrdering
{
public:
- typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
- typedef Matrix<Index, Dynamic, 1> IndexVector;
+ typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+ typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
/** Compute the permutation vector \a perm form the sparse matrix \a mat
* \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
@@ -126,26 +129,26 @@
{
eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
- Index m = mat.rows();
- Index n = mat.cols();
- Index nnz = mat.nonZeros();
+ StorageIndex m = StorageIndex(mat.rows());
+ StorageIndex n = StorageIndex(mat.cols());
+ StorageIndex nnz = StorageIndex(mat.nonZeros());
// Get the recommended value of Alen to be used by colamd
- Index Alen = internal::colamd_recommended(nnz, m, n);
+ StorageIndex Alen = internal::colamd_recommended(nnz, m, n);
// Set the default parameters
double knobs [COLAMD_KNOBS];
- Index stats [COLAMD_STATS];
+ StorageIndex stats [COLAMD_STATS];
internal::colamd_set_defaults(knobs);
IndexVector p(n+1), A(Alen);
- for(Index i=0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
- for(Index i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
+ for(StorageIndex i=0; i <= n; i++) p(i) = mat.outerIndexPtr()[i];
+ for(StorageIndex i=0; i < nnz; i++) A(i) = mat.innerIndexPtr()[i];
// Call Colamd routine to compute the ordering
- Index info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats);
+ StorageIndex info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats);
EIGEN_UNUSED_VARIABLE(info);
eigen_assert( info && "COLAMD failed " );
perm.resize(n);
- for (Index i = 0; i < n; i++) perm.indices()(p(i)) = i;
+ for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
}
};