From a91a7a1964305311133858de96b845da49389922 Mon Sep 17 00:00:00 2001 From: Jitse Niesen Date: Mon, 7 Apr 2014 14:14:48 +0100 Subject: [PATCH] doc: Add references to Cholesky methods in SelfAdjointView. --- Eigen/Cholesky | 6 ++++-- Eigen/src/Cholesky/LDLT.h | 6 ++++-- Eigen/src/Cholesky/LLT.h | 6 ++++-- doc/Doxyfile.in | 3 ++- 4 files changed, 14 insertions(+), 7 deletions(-) diff --git a/Eigen/Cholesky b/Eigen/Cholesky index f727f5d89..7314d326c 100644 --- a/Eigen/Cholesky +++ b/Eigen/Cholesky @@ -10,9 +10,11 @@ * * * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices. - * Those decompositions are accessible via the following MatrixBase methods: - * - MatrixBase::llt(), + * Those decompositions are also accessible via the following methods: + * - MatrixBase::llt() * - MatrixBase::ldlt() + * - SelfAdjointView::llt() + * - SelfAdjointView::ldlt() * * \code * #include diff --git a/Eigen/src/Cholesky/LDLT.h b/Eigen/src/Cholesky/LDLT.h index b43e85e7f..efac7fe40 100644 --- a/Eigen/src/Cholesky/LDLT.h +++ b/Eigen/src/Cholesky/LDLT.h @@ -43,7 +43,7 @@ namespace internal { * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky * decomposition to determine whether a system of equations has a solution. * - * \sa MatrixBase::ldlt(), class LLT + * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT */ template class LDLT { @@ -179,7 +179,7 @@ template class LDLT * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function * computes the least-square solution of \f$ A x = b \f$ is \f$ A \f$ is singular. * - * \sa MatrixBase::ldlt() + * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt() */ template inline const internal::solve_retval @@ -582,6 +582,7 @@ MatrixType LDLT::reconstructedMatrix() const #ifndef __CUDACC__ /** \cholesky_module * \returns the Cholesky decomposition with full pivoting without square root of \c *this + * \sa MatrixBase::ldlt() */ template inline const LDLT::PlainObject, UpLo> @@ -592,6 +593,7 @@ SelfAdjointView::ldlt() const /** \cholesky_module * \returns the Cholesky decomposition with full pivoting without square root of \c *this + * \sa SelfAdjointView::ldlt() */ template inline const LDLT::PlainObject> diff --git a/Eigen/src/Cholesky/LLT.h b/Eigen/src/Cholesky/LLT.h index 2201c641e..45ed8438f 100644 --- a/Eigen/src/Cholesky/LLT.h +++ b/Eigen/src/Cholesky/LLT.h @@ -41,7 +41,7 @@ template struct LLT_Traits; * Example: \include LLT_example.cpp * Output: \verbinclude LLT_example.out * - * \sa MatrixBase::llt(), class LDLT + * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT */ /* HEY THIS DOX IS DISABLED BECAUSE THERE's A BUG EITHER HERE OR IN LDLT ABOUT THAT (OR BOTH) * Note that during the decomposition, only the upper triangular part of A is considered. Therefore, @@ -115,7 +115,7 @@ template class LLT * Example: \include LLT_solve.cpp * Output: \verbinclude LLT_solve.out * - * \sa solveInPlace(), MatrixBase::llt() + * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt() */ template inline const internal::solve_retval @@ -468,6 +468,7 @@ MatrixType LLT::reconstructedMatrix() const #ifndef __CUDACC__ /** \cholesky_module * \returns the LLT decomposition of \c *this + * \sa SelfAdjointView::llt() */ template inline const LLT::PlainObject> @@ -478,6 +479,7 @@ MatrixBase::llt() const /** \cholesky_module * \returns the LLT decomposition of \c *this + * \sa SelfAdjointView::llt() */ template inline const LLT::PlainObject, UpLo> diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in index 85af9f1d4..7bbf693a0 100644 --- a/doc/Doxyfile.in +++ b/doc/Doxyfile.in @@ -223,7 +223,7 @@ ALIASES = "only_for_vectors=This is only for vectors (either row- "note_about_using_kernel_to_study_multiple_solutions=If you need a complete analysis of the space of solutions, take the one solution obtained by this method and add to it elements of the kernel, as determined by kernel()." \ "note_about_checking_solutions=This method just tries to find as good a solution as possible. If you want to check whether a solution exists or if it is accurate, just call this function to get a result and then compute the error of this result, or use MatrixBase::isApprox() directly, for instance like this: \code bool a_solution_exists = (A*result).isApprox(b, precision); \endcode This method avoids dividing by zero, so that the non-existence of a solution doesn't by itself mean that you'll get \c inf or \c nan values." \ "note_try_to_help_rvo=This function returns the result by value. In order to make that efficient, it is implemented as just a return statement using a special constructor, hopefully allowing the compiler to perform a RVO (return value optimization)." \ - "nonstableyet=\warning This is not considered to be part of the stable public API yet. Changes may happen in future releases. See \ref Experimental \"Experimental parts of Eigen\" + "nonstableyet=\warning This is not considered to be part of the stable public API yet. Changes may happen in future releases. See \ref Experimental \"Experimental parts of Eigen\"" ALIASES += "eigenAutoToc= " @@ -1583,6 +1583,7 @@ PREDEFINED = EIGEN_EMPTY_STRUCT \ EIGEN_VECTORIZE \ EIGEN_QT_SUPPORT \ EIGEN_STRONG_INLINE=inline \ + EIGEN_DEVICE_FUNC= \ "EIGEN2_SUPPORT_STAGE=99" \ "EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR)=template const CwiseBinaryOp, const Derived, const OtherDerived> METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS &other) const;" \ "EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp, const LHS, const RHS>"