mirror/eigen
2
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2024-12-21 07:19:46 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add assertion and warning on the requirements of SparseQR and COLAMDOrdering

Browse Source
This commit is contained in:
Gael Guennebaud 2014-06-20 14:43:47 +02:00
parent 1fdef63d1f
commit 98ef44fe55
2 changed files with 23 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
Eigen/src/OrderingMethods/Ordering.h
View File

@ -109,7 +109,7 @@ class NaturalOrdering
* \class COLAMDOrdering
*
* Functor computing the \em column \em approximate \em minimum \em degree ordering
* The matrix should be in column-major format
* The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
*/
template<typename Index>
class COLAMDOrdering
@ -118,10 +118,14 @@ class COLAMDOrdering
typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType;
typedef Matrix<Index, Dynamic, 1> IndexVector;
/** Compute the permutation vector form a sparse matrix */
/** 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()).
*/
template <typename MatrixType>
void operator() (const MatrixType& mat, PermutationType& perm)
{
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();

17
Eigen/src/SparseQR/SparseQR.h
View File

@ -58,6 +58,7 @@ namespace internal {
* \tparam _OrderingType The fill-reducing ordering method. See the \link OrderingMethods_Module
* OrderingMethods \endlink module for the list of built-in and external ordering methods.
*
* \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
*
*/
template<typename _MatrixType, typename _OrderingType>
@ -77,10 +78,23 @@ class SparseQR
SparseQR () : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
{ }
/** Construct a QR factorization of the matrix \a mat.
*
* \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
*
* \sa compute()
*/
SparseQR(const MatrixType& mat) : m_isInitialized(false), m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false)
{
compute(mat);
}
/** Computes the QR factorization of the sparse matrix \a mat.
*
* \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
*
* \sa analyzePattern(), factorize()
*/
void compute(const MatrixType& mat)
{
analyzePattern(mat);
@ -255,6 +269,8 @@ class SparseQR
};
/** \brief Preprocessing step of a QR factorization
*
* \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
*
* In this step, the fill-reducing permutation is computed and applied to the columns of A
* and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
@ -264,6 +280,7 @@ class SparseQR
template <typename MatrixType, typename OrderingType>
void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
{
eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
// Compute the column fill reducing ordering
OrderingType ord;
ord(mat, m_perm_c);