Fix several documentation issues
diff --git a/doc/C03_TutorialArrayClass.dox b/doc/C03_TutorialArrayClass.dox
index a1d8d69..d15d6d5 100644
--- a/doc/C03_TutorialArrayClass.dox
+++ b/doc/C03_TutorialArrayClass.dox
@@ -122,7 +122,7 @@
The Array class defines other coefficient-wise operations besides the addition, subtraction and multiplication
operators described above. For example, the \link ArrayBase::abs() .abs() \endlink method takes the absolute
value of each coefficient, while \link ArrayBase::sqrt() .sqrt() \endlink computes the square root of the
-coefficients. If you have two arrays of the same size, you can call \link ArrayBase::min() .min() \endlink to
+coefficients. If you have two arrays of the same size, you can call \link ArrayBase::min(const Eigen::ArrayBase<OtherDerived>&) const .min(.) \endlink to
construct the array whose coefficients are the minimum of the corresponding coefficients of the two given
arrays. These operations are illustrated in the following example.
@@ -168,8 +168,8 @@
* to multiply them coefficient-wise and assigns the result to the matrix variable \c result (this is legal
because Eigen allows assigning array expressions to matrix variables).
-As a matter of fact, this usage case is so common that Eigen provides a \link MatrixBase::cwiseProduct()
-.cwiseProduct() \endlink method for matrices to compute the coefficient-wise product. This is also shown in
+As a matter of fact, this usage case is so common that Eigen provides a \link MatrixBase::cwiseProduct() const
+.cwiseProduct(.) \endlink method for matrices to compute the coefficient-wise product. This is also shown in
the example program.
<table class="example">
diff --git a/doc/C09_TutorialSparse.dox b/doc/C09_TutorialSparse.dox
index 6a16c3a..a476c4a 100644
--- a/doc/C09_TutorialSparse.dox
+++ b/doc/C09_TutorialSparse.dox
@@ -25,10 +25,10 @@
<table class="manual">
<tr><th>Module</th><th>Header file</th><th>Contents</th></tr>
-<tr><td>\link Sparse_Module SparseCore \endlink</td><td>\code#include <Eigen/SparseCore>\endcode</td><td>SparseMatrix and SparseVector classes, matrix assembly, basic sparse linear algebra (including sparse triangular solvers)</td></tr>
+<tr><td>\link SparseCore_Module SparseCore \endlink</td><td>\code#include <Eigen/SparseCore>\endcode</td><td>SparseMatrix and SparseVector classes, matrix assembly, basic sparse linear algebra (including sparse triangular solvers)</td></tr>
<tr><td>\link SparseCholesky_Module SparseCholesky \endlink</td><td>\code#include <Eigen/SparseCholesky>\endcode</td><td>Direct sparse LLT and LDLT Cholesky factorization to solve sparse self-adjoint positive definite problems</td></tr>
<tr><td>\link IterativeLinearSolvers_Module IterativeLinearSolvers \endlink</td><td>\code#include <Eigen/IterativeLinearSolvers>\endcode</td><td>Iterative solvers to solve large general linear square problems (including self-adjoint positive definite problems)</td></tr>
-<tr><td></td><td>\code#include <Eigen/Sparse>\endcode</td><td>Includes all the above modules</td></tr>
+<tr><td>\link Sparse_modules Sparse \endlink</td><td>\code#include <Eigen/Sparse>\endcode</td><td>Includes all the above modules</td></tr>
</table>
\section TutorialSparseIntro Sparse matrix representation
diff --git a/doc/C10_TutorialMapClass.dox b/doc/C10_TutorialMapClass.dox
index 09e7927..f4b5db3 100644
--- a/doc/C10_TutorialMapClass.dox
+++ b/doc/C10_TutorialMapClass.dox
@@ -5,7 +5,7 @@
\ingroup Tutorial
\li \b Previous: \ref TutorialSparse
-\li \b Next: \ref TODO
+\li \b Next:
This tutorial page explains how to work with "raw" C++ arrays. This can be useful in a variety of contexts, particularly when "importing" vectors and matrices from other libraries into Eigen.
@@ -89,7 +89,7 @@
}
\endcode
-\li \b Next: \ref TODO
+\li \b Next:
*/
diff --git a/doc/Doxyfile.in b/doc/Doxyfile.in
index 167e650..8751822 100644
--- a/doc/Doxyfile.in
+++ b/doc/Doxyfile.in
@@ -1214,7 +1214,9 @@
EIGEN_VECTORIZE \
EIGEN_QT_SUPPORT \
EIGEN_STRONG_INLINE=inline \
- EIGEN2_SUPPORT_STAGE=99
+ "EIGEN2_SUPPORT_STAGE=99" \
+ "EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR)=template<typename OtherDerived> const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const;" \
+ "EIGEN_CWISE_PRODUCT_RETURN_TYPE(LHS,RHS)=CwiseBinaryOp<internal::scalar_product_op<typename LHS::Scalar, typename RHS::Scalar >, const LHS, const RHS>"
# If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then
# this tag can be used to specify a list of macro names that should be expanded.
@@ -1224,11 +1226,10 @@
EXPAND_AS_DEFINED = EIGEN_MAKE_TYPEDEFS \
EIGEN_MAKE_FIXED_TYPEDEFS \
EIGEN_MAKE_TYPEDEFS_ALL_SIZES \
- EIGEN_MAKE_CWISE_BINARY_OP \
EIGEN_CWISE_UNOP_RETURN_TYPE \
EIGEN_CWISE_BINOP_RETURN_TYPE \
- EIGEN_CWISE_PRODUCT_RETURN_TYPE \
EIGEN_CURRENT_STORAGE_BASE_CLASS \
+ EIGEN_MATHFUNC_IMPL \
_EIGEN_GENERIC_PUBLIC_INTERFACE \
EIGEN2_SUPPORT
diff --git a/doc/I14_PreprocessorDirectives.dox b/doc/I14_PreprocessorDirectives.dox
index f29f072..948f352 100644
--- a/doc/I14_PreprocessorDirectives.dox
+++ b/doc/I14_PreprocessorDirectives.dox
@@ -29,8 +29,8 @@
Eigen3; see \ref Eigen2SupportModes.
- \b EIGEN_DEFAULT_DENSE_INDEX_TYPE - the type for column and row indices in matrices, vectors and array
(DenseBase::Index). Set to \c std::ptrdiff_t by default.
- - \b EIGEN_DEFAULT_IO_FORMAT - the IOFormat to use when printing a matrix if no #IOFormat is specified.
- Defaults to the #IOFormat constructed by the default constructor IOFormat().
+ - \b EIGEN_DEFAULT_IO_FORMAT - the IOFormat to use when printing a matrix if no %IOFormat is specified.
+ Defaults to the %IOFormat constructed by the default constructor IOFormat::IOFormat().
- \b EIGEN_INITIALIZE_MATRICES_BY_ZERO - if defined, all entries of newly constructed matrices and arrays are
initializes to zero, as are new entries in matrices and arrays after resizing. Not defined by default.
- \b EIGEN_NO_AUTOMATIC_RESIZING - if defined, the matrices (or arrays) on both sides of an assignment
diff --git a/doc/I17_SparseLinearSystems.dox b/doc/I17_SparseLinearSystems.dox
index cc8987d..e0179d1 100644
--- a/doc/I17_SparseLinearSystems.dox
+++ b/doc/I17_SparseLinearSystems.dox
@@ -39,7 +39,7 @@
DirectSolverClassName<SparseMatrix<double>, OrderingMethod<IndexType> > solver;
\endcode
-See \link Ordering_Modules the Ordering module \endlink for the list of available methods and the associated options.
+See the \link OrderingMethods_Module OrderingMethods module \endlink for the list of available methods and the associated options.
In factorize(), the factors of the coefficient matrix are computed. This step should be called each time the values of the matrix change. However, the structural pattern of the matrix should not change between multiple calls.
@@ -64,10 +64,10 @@
x2 = solver.solve(b2);
// ...
\endcode
-For direct methods, the solution are computed at the machine precision. Sometimes, the solution need not be too accurate. In this case, the iterative methods are more suitable and the desired accuracy can be set before the solve step using setTolerance(). For all the available functions, please, refer to the documentation of the \link IterativeLinearSolvers_module Iterative solvers module \endlink.
+For direct methods, the solution are computed at the machine precision. Sometimes, the solution need not be too accurate. In this case, the iterative methods are more suitable and the desired accuracy can be set before the solve step using setTolerance(). For all the available functions, please, refer to the documentation of the \link IterativeLinearSolvers_Module Iterative solvers module \endlink.
\section BenchmarkRoutine
-Most of the time, all you need is to know how much time it will take to qolve your system, and hopefully, what is the most suitable solver. In Eigen, we provide a benchmark routine that can be used for this purpose. It is very easy to use. First, it should be activated at the configuration step with the flag TEST_REAL_CASES. Then, in bench/spbench, you can compile the routine by typing \b make \e spbenchsolver. You can then run it with --help option to get the list of all available options. Basically, the matrices to test should be in \link http://math.nist.gov/MatrixMarket/formats.html MatrixMarket Coordinate format \endlink, and the routine returns the statistics from all available solvers in Eigen.
+Most of the time, all you need is to know how much time it will take to qolve your system, and hopefully, what is the most suitable solver. In Eigen, we provide a benchmark routine that can be used for this purpose. It is very easy to use. First, it should be activated at the configuration step with the flag TEST_REAL_CASES. Then, in bench/spbench, you can compile the routine by typing \b make \e spbenchsolver. You can then run it with --help option to get the list of all available options. Basically, the matrices to test should be in <a href="http://math.nist.gov/MatrixMarket/formats.html">MatrixMarket Coordinate format</a>, and the routine returns the statistics from all available solvers in Eigen.
The following table gives an example of XHTML statistics from several Eigen built-in and external solvers.
<TABLE border="1">
diff --git a/doc/QuickReference.dox b/doc/QuickReference.dox
index 31b8030..f91b96e 100644
--- a/doc/QuickReference.dox
+++ b/doc/QuickReference.dox
@@ -31,7 +31,7 @@
<tr><td>\link SVD_Module SVD \endlink</td><td>\code#include <Eigen/SVD>\endcode</td><td>SVD decomposition with least-squares solver (JacobiSVD)</td></tr>
<tr class="alt"><td>\link QR_Module QR \endlink</td><td>\code#include <Eigen/QR>\endcode</td><td>QR decomposition with solver (HouseholderQR, ColPivHouseholderQR, FullPivHouseholderQR)</td></tr>
<tr><td>\link Eigenvalues_Module Eigenvalues \endlink</td><td>\code#include <Eigen/Eigenvalues>\endcode</td><td>Eigenvalue, eigenvector decompositions (EigenSolver, SelfAdjointEigenSolver, ComplexEigenSolver)</td></tr>
-<tr class="alt"><td>\link Sparse_Module Sparse \endlink</td><td>\code#include <Eigen/Sparse>\endcode</td><td>%Sparse matrix storage and related basic linear algebra (SparseMatrix, DynamicSparseMatrix, SparseVector)</td></tr>
+<tr class="alt"><td>\link Sparse_modules Sparse \endlink</td><td>\code#include <Eigen/Sparse>\endcode</td><td>%Sparse matrix storage and related basic linear algebra (SparseMatrix, DynamicSparseMatrix, SparseVector)</td></tr>
<tr><td></td><td>\code#include <Eigen/Dense>\endcode</td><td>Includes Core, Geometry, LU, Cholesky, SVD, QR, and Eigenvalues header files</td></tr>
<tr class="alt"><td></td><td>\code#include <Eigen/Eigen>\endcode</td><td>Includes %Dense and %Sparse header files (the whole Eigen library)</td></tr>
</table>
@@ -89,8 +89,8 @@
\endcode
In the rest of this document we will use the following symbols to emphasize the features which are specifics to a given kind of object:
-\li <a name="matrixonly"><a/>\matrixworld linear algebra matrix and vector only
-\li <a name="arrayonly"><a/>\arrayworld array objects only
+\li <a name="matrixonly"></a>\matrixworld linear algebra matrix and vector only
+\li <a name="arrayonly"></a>\arrayworld array objects only
\subsection QuickRef_Basics Basic matrix manipulation
@@ -458,7 +458,7 @@
\endcode</td></tr>
</table>
-Special versions of \link DenseBase::minCoeff(Index*,Index*) minCoeff \endlink and \link DenseBase::maxCoeff(Index*,Index*) maxCoeff \endlink:
+Special versions of \link DenseBase::minCoeff(IndexType*,IndexType*) const minCoeff \endlink and \link DenseBase::maxCoeff(IndexType*,IndexType*) const maxCoeff \endlink:
\code
int i, j;
s = vector.minCoeff(&i); // s == vector[i]
diff --git a/doc/UsingIntelMKL.dox b/doc/UsingIntelMKL.dox
index 379ee3f..f444433 100644
--- a/doc/UsingIntelMKL.dox
+++ b/doc/UsingIntelMKL.dox
@@ -61,7 +61,7 @@
<tr><td>\c EIGEN_USE_MKL_ALL </td><td>Defines \c EIGEN_USE_BLAS, \c EIGEN_USE_LAPACKE, and \c EIGEN_USE_MKL_VML </td></tr>
</table>
-Finally, the PARDISO sparse solver shipped with Intel MKL can be used through the \ref PardisoLU, \ref PardisoLLT and \ref PardisoLDLT classes of the \ref PARDISOSupport_Module.
+Finally, the PARDISO sparse solver shipped with Intel MKL can be used through the \ref PardisoLU, \ref PardisoLLT and \ref PardisoLDLT classes of the \ref PardisoSupport_Module.
\section TopicUsingIntelMKL_SupportedFeatures List of supported features