From 3390db099a086c4cc5c9b90cb0ae339c4ba93832 Mon Sep 17 00:00:00 2001 From: Christoph Hertzberg Date: Thu, 17 Oct 2013 00:03:00 +0200 Subject: Fixes bug #681 Also fixed some spelling issues in the documentation --- Eigen/src/CholmodSupport/CholmodSupport.h | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) (limited to 'Eigen/src/CholmodSupport') diff --git a/Eigen/src/CholmodSupport/CholmodSupport.h b/Eigen/src/CholmodSupport/CholmodSupport.h index 783324b0b..c449960de 100644 --- a/Eigen/src/CholmodSupport/CholmodSupport.h +++ b/Eigen/src/CholmodSupport/CholmodSupport.h @@ -58,10 +58,12 @@ cholmod_sparse viewAsCholmod(SparseMatrix<_Scalar,_Options,_Index>& mat) res.p = mat.outerIndexPtr(); res.i = mat.innerIndexPtr(); res.x = mat.valuePtr(); + res.z = 0; res.sorted = 1; if(mat.isCompressed()) { res.packed = 1; + res.nz = 0; } else { @@ -170,6 +172,7 @@ class CholmodBase : internal::noncopyable CholmodBase() : m_cholmodFactor(0), m_info(Success), m_isInitialized(false) { + m_shiftOffset[0] = m_shiftOffset[1] = RealScalar(0.0); cholmod_start(&m_cholmod); } @@ -241,7 +244,7 @@ class CholmodBase : internal::noncopyable return internal::sparse_solve_retval(*this, b.derived()); } - /** Performs a symbolic decomposition on the sparcity of \a matrix. + /** Performs a symbolic decomposition on the sparsity pattern of \a matrix. * * This function is particularly useful when solving for several problems having the same structure. * @@ -265,7 +268,7 @@ class CholmodBase : internal::noncopyable /** Performs a numeric decomposition of \a matrix * - * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed. + * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed. * * \sa analyzePattern() */ @@ -302,7 +305,7 @@ class CholmodBase : internal::noncopyable { this->m_info = NumericalIssue; } - // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.) + // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) dest = Matrix::Map(reinterpret_cast(x_cd->x),b.rows(),b.cols()); cholmod_free_dense(&x_cd, &m_cholmod); } @@ -323,7 +326,7 @@ class CholmodBase : internal::noncopyable { this->m_info = NumericalIssue; } - // TODO optimize this copy by swapping when possible (be carreful with alignment, etc.) + // TODO optimize this copy by swapping when possible (be careful with alignment, etc.) dest = viewAsEigen(*x_cs); cholmod_free_sparse(&x_cs, &m_cholmod); } @@ -365,8 +368,8 @@ class CholmodBase : internal::noncopyable * * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization * using the Cholmod library. - * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Thefore, it has little practical interest. - * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices + * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest. + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> @@ -412,8 +415,8 @@ class CholmodSimplicialLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimpl * * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization * using the Cholmod library. - * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Thefore, it has little practical interest. - * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices + * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest. + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> @@ -458,7 +461,7 @@ class CholmodSimplicialLDLT : public CholmodBase<_MatrixType, _UpLo, CholmodSimp * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization * using the Cholmod library. * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM. - * The sparse matrix A must be selfajoint and positive definite. The vectors or matrices + * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<> @@ -501,7 +504,7 @@ class CholmodSupernodalLLT : public CholmodBase<_MatrixType, _UpLo, CholmodSuper * \brief A general Cholesky factorization and solver based on Cholmod * * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization - * using the Cholmod library. The sparse matrix A must be selfajoint and positive definite. The vectors or matrices + * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices * X and B can be either dense or sparse. * * This variant permits to change the underlying Cholesky method at runtime. -- cgit v1.2.3