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Fix several documentation issues

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This commit is contained in:
Gael Guennebaud 2012-12-24 13:33:22 +01:00
parent f450303321
commit f41d96deb9
29 changed files with 115 additions and 108 deletions
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2
Eigen/src/Core/ArrayBase.h
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@ -143,7 +143,7 @@ template<typename Derived> class ArrayBase
ArrayBase<Derived>& array() { return *this; }
const ArrayBase<Derived>& array() const { return *this; }
/** \returns an \link MatrixBase Matrix \endlink expression of this array
/** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
* \sa MatrixBase::array() */
MatrixWrapper<Derived> matrix() { return derived(); }
const MatrixWrapper<const Derived> matrix() const { return derived(); }

38
Eigen/src/Core/CwiseNullaryOp.h
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@ -163,11 +163,11 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
/** \returns an expression of a constant matrix of value \a value
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* the returned matrix. Must be compatible with this DenseBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
* it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
* it is redundant to pass \a nbRows and \a nbCols as arguments, so Zero() should be used
* instead.
*
* The template parameter \a CustomNullaryOp is the type of the functor.
@ -176,9 +176,9 @@ DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
*/
template<typename Derived>
EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
DenseBase<Derived>::Constant(Index nbRows, Index nbCols, const Scalar& value)
{
return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
return DenseBase<Derived>::NullaryExpr(nbRows, nbCols, internal::scalar_constant_op<Scalar>(value));
}
/** \returns an expression of a constant matrix of value \a value
@ -292,7 +292,7 @@ DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar,true>(low,high,Derived::SizeAtCompileTime));
}
/** \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
template<typename Derived>
bool DenseBase<Derived>::isApproxToConstant
(const Scalar& val, const RealScalar& prec) const
@ -314,7 +314,7 @@ bool DenseBase<Derived>::isConstant
return isApproxToConstant(val, prec);
}
/** Alias for setConstant(): sets all coefficients in this expression to \a value.
/** Alias for setConstant(): sets all coefficients in this expression to \a val.
*
* \sa setConstant(), Constant(), class CwiseNullaryOp
*/
@ -353,9 +353,9 @@ PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
/** Resizes to the given size, and sets all coefficients in this expression to the given \a value.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param value the value to which all coefficients are set
* \param nbRows the new number of rows
* \param nbCols the new number of columns
* \param val the value to which all coefficients are set
*
* Example: \include Matrix_setConstant_int_int.cpp
* Output: \verbinclude Matrix_setConstant_int_int.out
@ -520,8 +520,8 @@ PlainObjectBase<Derived>::setZero(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to zero.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setZero_int_int.cpp
* Output: \verbinclude Matrix_setZero_int_int.out
@ -540,7 +540,7 @@ PlainObjectBase<Derived>::setZero(Index nbRows, Index nbCols)
/** \returns an expression of a matrix where all coefficients equal one.
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@ -561,7 +561,7 @@ DenseBase<Derived>::Ones(Index nbRows, Index nbCols)
/** \returns an expression of a vector where all coefficients equal one.
*
* The parameter \a size is the size of the returned vector.
* The parameter \a newSize is the size of the returned vector.
* Must be compatible with this MatrixBase type.
*
* \only_for_vectors
@ -627,7 +627,7 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
return setConstant(Scalar(1));
}
/** Resizes to the given \a size, and sets all coefficients in this expression to one.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
*
* \only_for_vectors
*
@ -646,8 +646,8 @@ PlainObjectBase<Derived>::setOnes(Index newSize)
/** Resizes to the given size, and sets all coefficients in this expression to one.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setOnes_int_int.cpp
* Output: \verbinclude Matrix_setOnes_int_int.out
@ -666,7 +666,7 @@ PlainObjectBase<Derived>::setOnes(Index nbRows, Index nbCols)
/** \returns an expression of the identity matrix (not necessarily square).
*
* The parameters \a rows and \a cols are the number of rows and of columns of
* The parameters \a nbRows and \a nbCols are the number of rows and of columns of
* the returned matrix. Must be compatible with this MatrixBase type.
*
* This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
@ -776,8 +776,8 @@ EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setIdentity_int_int.cpp
* Output: \verbinclude Matrix_setIdentity_int_int.out

7
Eigen/src/Core/DiagonalMatrix.h
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@ -56,9 +56,14 @@ class DiagonalBase : public EigenBase<Derived>
inline Index rows() const { return diagonal().size(); }
inline Index cols() const { return diagonal().size(); }
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename MatrixDerived>
const DiagonalProduct<MatrixDerived, Derived, OnTheLeft>
operator*(const MatrixBase<MatrixDerived> &matrix) const;
operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, Derived, OnTheLeft>(matrix.derived(), derived());
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType> >
inverse() const

10
Eigen/src/Core/DiagonalProduct.h
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@ -108,16 +108,6 @@ MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal)
return DiagonalProduct<Derived, DiagonalDerived, OnTheRight>(derived(), a_diagonal.derived());
}
/** \returns the diagonal matrix product of \c *this by the matrix \a matrix.
*/
template<typename DiagonalDerived>
template<typename MatrixDerived>
inline const DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>
DiagonalBase<DiagonalDerived>::operator*(const MatrixBase<MatrixDerived> &matrix) const
{
return DiagonalProduct<MatrixDerived, DiagonalDerived, OnTheLeft>(matrix.derived(), derived());
}
} // end namespace Eigen
#endif // EIGEN_DIAGONALPRODUCT_H

3
Eigen/src/Core/EigenBase.h
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@ -139,7 +139,8 @@ MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
return derived();
}
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=() */
/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
*/
template<typename Derived>
template<typename OtherDerived>
inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)

22
Eigen/src/Core/Map.h
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@ -133,20 +133,20 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the fixed-size case.
*
* \param data pointer to the array to map
* \param stride optional Stride object, passing the strides.
* \param dataPtr pointer to the array to map
* \param a_stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType data, const StrideType& stride = StrideType())
: Base(cast_to_pointer_type(data)), m_stride(stride)
inline Map(PointerArgType dataPtr, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr)), m_stride(a_stride)
{
PlainObjectType::Base::_check_template_params();
}
/** Constructor in the dynamic-size vector case.
*
* \param data pointer to the array to map
* \param size the size of the vector expression
* \param stride optional Stride object, passing the strides.
* \param dataPtr pointer to the array to map
* \param a_size the size of the vector expression
* \param a_stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, Index a_size, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), a_size), m_stride(a_stride)
@ -156,10 +156,10 @@ template<typename PlainObjectType, int MapOptions, typename StrideType> class Ma
/** Constructor in the dynamic-size matrix case.
*
* \param data pointer to the array to map
* \param rows the number of rows of the matrix expression
* \param cols the number of columns of the matrix expression
* \param stride optional Stride object, passing the strides.
* \param dataPtr pointer to the array to map
* \param nbRows the number of rows of the matrix expression
* \param nbCols the number of columns of the matrix expression
* \param a_stride optional Stride object, passing the strides.
*/
inline Map(PointerArgType dataPtr, Index nbRows, Index nbCols, const StrideType& a_stride = StrideType())
: Base(cast_to_pointer_type(dataPtr), nbRows, nbCols), m_stride(a_stride)

2
Eigen/src/Core/MatrixBase.h
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@ -317,7 +317,7 @@ template<typename Derived> class MatrixBase
MatrixBase<Derived>& matrix() { return *this; }
const MatrixBase<Derived>& matrix() const { return *this; }
/** \returns an \link ArrayBase Array \endlink expression of this matrix
/** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
* \sa ArrayBase::matrix() */
ArrayWrapper<Derived> array() { return derived(); }
const ArrayWrapper<const Derived> array() const { return derived(); }

10
Eigen/src/Core/Random.h
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@ -112,7 +112,7 @@ inline Derived& DenseBase<Derived>::setRandom()
return *this = Random(rows(), cols());
}
/** Resizes to the given \a size, and sets all coefficients in this expression to random values.
/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
*
* \only_for_vectors
*
@ -123,16 +123,16 @@ inline Derived& DenseBase<Derived>::setRandom()
*/
template<typename Derived>
EIGEN_STRONG_INLINE Derived&
PlainObjectBase<Derived>::setRandom(Index size)
PlainObjectBase<Derived>::setRandom(Index newSize)
{
resize(size);
resize(newSize);
return setRandom();
}
/** Resizes to the given size, and sets all coefficients in this expression to random values.
*
* \param rows the new number of rows
* \param cols the new number of columns
* \param nbRows the new number of rows
* \param nbCols the new number of columns
*
* Example: \include Matrix_setRandom_int_int.cpp
* Output: \verbinclude Matrix_setRandom_int_int.out

4
Eigen/src/Core/util/Constants.h
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@ -264,9 +264,9 @@ enum {
ColMajor = 0,
/** Storage order is row major (see \ref TopicStorageOrders). */
RowMajor = 0x1, // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
/** \internal Align the matrix itself if it is vectorizable fixed-size */
/** Align the matrix itself if it is vectorizable fixed-size */
AutoAlign = 0,
/** \internal Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
/** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
DontAlign = 0x2
};

2
Eigen/src/SparseCore/SparseMatrix.h
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@ -1121,7 +1121,7 @@ void set_from_triplets(const InputIterator& begin, const InputIterator& end, Spa
}
/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \b.
/** Fill the matrix \c *this with the list of \em triplets defined by the iterator range \a begin - \a end.
*
* A \em triplet is a tuple (i,j,value) defining a non-zero element.
* The input list of triplets does not have to be sorted, and can contains duplicated elements.

8
Eigen/src/SparseLU/SparseLU.h
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@ -38,7 +38,7 @@ namespace Eigen {
* An important parameter of this class is the ordering method. It is used to reorder the columns
* (and eventually the rows) of the matrix to reduce the number of new elements that are created during
* numerical factorization. The cheapest method available is COLAMD.
* See \link Ordering_Modules the Ordering module \endlink for the list of
* See \link OrderingMethods_Module the OrderingMethods module \endlink for the list of
* built-in and external ordering methods.
*
* Simple example with key steps
@ -55,10 +55,10 @@ namespace Eigen {
* x = solver.solve(b);
* \endcode
*
* \WARNING The input matrix A should be in a \b compressed and \b column-major form.
* \warning The input matrix A should be in a \b compressed and \b column-major form.
* Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
*
* \NOTE Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix.
* \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix.
* For badly scaled matrices, this step can be useful to reduce the pivoting during factorization.
* If this is the case for your matrices, you can try the basic scaling method at
* "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
@ -68,7 +68,7 @@ namespace Eigen {
*
*
* \sa \ref TutorialSparseDirectSolvers
* \sa \ref Ordering_Modules
* \sa \ref OrderingMethods_Module
*/
template <typename _MatrixType, typename _OrderingType>
class SparseLU

6
Eigen/src/SparseLU/SparseLU_Memory.h
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@ -44,10 +44,10 @@
/**
* Expand the existing storage to accomodate more fill-ins
* \param vec Valid pointer to the vector to allocate or expand
* \param [in,out]length At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
* \param [in]nbElts Current number of elements in the factors
* \param[in,out] length At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
* \param[in] nbElts Current number of elements in the factors
* \param keep_prev 1: use length and do not expand the vector; 0: compute new_len and expand
* \param [in,out]num_expansions Number of times the memory has been expanded
* \param[in,out] num_expansions Number of times the memory has been expanded
*/
template <typename Scalar, typename Index>
template <typename VectorType>

2
Eigen/src/SparseLU/SparseLU_kernel_bmod.h
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@ -15,7 +15,7 @@
* \brief Performs numeric block updates from a given supernode to a single column
*
* \param segsize Size of the segment (and blocks ) to use for updates
* \param [in,out]dense Packed values of the original matrix
* \param[in,out] dense Packed values of the original matrix
* \param tempv temporary vector to use for updates
* \param lusup array containing the supernodes
* \param lda Leading dimension in the supernode

24
Eigen/src/SparseLU/SparseLU_panel_dfs.h
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@ -169,18 +169,18 @@ void SparseLUBase<Scalar,Index>::LU_dfs_kernel(const int jj, IndexVector& perm_r
* marker[i] == jj, if i was visited during dfs of current column jj;
* marker1[i] >= jcol, if i was visited by earlier columns in this panel;
*
* \param [in]m number of rows in the matrix
* \param [in]w Panel size
* \param [in]jcol Starting column of the panel
* \param [in]A Input matrix in column-major storage
* \param [in]perm_r Row permutation
* \param [out]nseg Number of U segments
* \param [out]dense Accumulate the column vectors of the panel
* \param [out]panel_lsub Subscripts of the row in the panel
* \param [out]segrep Segment representative i.e first nonzero row of each segment
* \param [out]repfnz First nonzero location in each row
* \param [out]xprune
* \param [out]marker
* \param[in] m number of rows in the matrix
* \param[in] w Panel size
* \param[in] jcol Starting column of the panel
* \param[in] A Input matrix in column-major storage
* \param[in] perm_r Row permutation
* \param[out] nseg Number of U segments
* \param[out] dense Accumulate the column vectors of the panel
* \param[out] panel_lsub Subscripts of the row in the panel
* \param[out] segrep Segment representative i.e first nonzero row of each segment
* \param[out] repfnz First nonzero location in each row
* \param[out] xprune
* \param[out] marker
*
*
*/

6
Eigen/src/SparseLU/SparseLU_pivotL.h
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@ -45,9 +45,9 @@
*
* \param jcol The current column of L
* \param u diagonal pivoting threshold
* \param [in,out]perm_r Row permutation (threshold pivoting)
* \param [in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
* \param [out]pivrow The pivot row
* \param[in,out] perm_r Row permutation (threshold pivoting)
* \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
* \param[out] pivrow The pivot row
* \param glu Global LU data
* \return 0 if success, i > 0 if U(i,i) is exactly zero
*

6
Eigen/src/SparseLU/SparseLU_pruneL.h
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@ -37,12 +37,12 @@
*
*
* \param jcol The current column of L
* \param [in]perm_r Row permutation
* \param [out]pivrow The pivot row
* \param[in] perm_r Row permutation
* \param[out] pivrow The pivot row
* \param nseg Number of segments
* \param segrep
* \param repfnz
* \param [out]xprune
* \param[out] xprune
* \param glu Global LU data
*
*/

2
Eigen/src/UmfPackSupport/UmfPackSupport.h
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@ -114,7 +114,7 @@ inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *N
* using the UmfPack library. The sparse matrix A must be squared and full rank.
* The vectors or matrices X and B can be either dense or sparse.
*
* \WARNING The input matrix A should be in a \b compressed and \b column-major form.
* \warning The input matrix A should be in a \b compressed and \b column-major form.
* Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
* \tparam _MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
*

14
Eigen/src/plugins/ArrayCwiseBinaryOps.h
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@ -35,7 +35,12 @@ EIGEN_MAKE_CWISE_BINARY_OP(min,internal::scalar_min_op)
*/
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
(min)(const Scalar &other) const
#ifdef EIGEN_PARSED_BY_DOXYGEN
min
#else
(min)
#endif
(const Scalar &other) const
{
return (min)(Derived::PlainObject::Constant(rows(), cols(), other));
}
@ -55,7 +60,12 @@ EIGEN_MAKE_CWISE_BINARY_OP(max,internal::scalar_max_op)
*/
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
(max)(const Scalar &other) const
#ifdef EIGEN_PARSED_BY_DOXYGEN
max
#else
(max)
#endif
(const Scalar &other) const
{
return (max)(Derived::PlainObject::Constant(rows(), cols(), other));
}

10
Eigen/src/plugins/BlockMethods.h
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@ -584,7 +584,7 @@ inline ConstRowXpr row(Index i) const
* \only_for_vectors
*
* \param start the first coefficient in the segment
* \param size the number of coefficients in the segment
* \param vecSize the number of coefficients in the segment
*
* Example: \include MatrixBase_segment_int_int.cpp
* Output: \verbinclude MatrixBase_segment_int_int.out
@ -613,7 +613,7 @@ inline ConstSegmentReturnType segment(Index start, Index vecSize) const
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
* \param vecSize the number of coefficients in the block
*
* Example: \include MatrixBase_start_int.cpp
* Output: \verbinclude MatrixBase_start_int.out
@ -624,10 +624,10 @@ inline ConstSegmentReturnType segment(Index start, Index vecSize) const
*
* \sa class Block, block(Index,Index)
*/
inline SegmentReturnType head(Index vecsize)
inline SegmentReturnType head(Index vecSize)
{
EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
return SegmentReturnType(derived(), 0, vecsize);
return SegmentReturnType(derived(), 0, vecSize);
}
/** This is the const version of head(Index).*/
@ -642,7 +642,7 @@ inline ConstSegmentReturnType
*
* \only_for_vectors
*
* \param size the number of coefficients in the block
* \param vecSize the number of coefficients in the block
*
* Example: \include MatrixBase_end_int.cpp
* Output: \verbinclude MatrixBase_end_int.out

6
doc/C03_TutorialArrayClass.dox
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@ -122,7 +122,7 @@ arrays can be multiplied if and only if they have the same dimensions.
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 @@ The following example shows how to use array operations on a Matrix object by em
* 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">

4
doc/C09_TutorialSparse.dox
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@ -25,10 +25,10 @@ Manipulating and solving sparse problems involves various modules which are summ
<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

4
doc/C10_TutorialMapClass.dox
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@ -5,7 +5,7 @@ namespace Eigen {
\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 @@ for (int i = 0; i < n_matrices; i++)
}
\endcode
\li \b Next: \ref TODO
\li \b Next:
*/

7
doc/Doxyfile.in
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@ -1214,7 +1214,9 @@ PREDEFINED = EIGEN_EMPTY_STRUCT \
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 @@ PREDEFINED = EIGEN_EMPTY_STRUCT \
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

4
doc/I14_PreprocessorDirectives.dox
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@ -29,8 +29,8 @@ are doing.
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

6
doc/I17_SparseLinearSystems.dox
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@ -39,7 +39,7 @@ Eigen provides a limited set of methods to reorder the matrix in this step, eith
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 @@ x1 = solver.solve(b1);
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">

8
doc/QuickReference.dox
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@ -31,7 +31,7 @@ The Eigen library is divided in a Core module and several additional modules. Ea
<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 @@ MatrixWrapper<Array44f> a1m(a1);
\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 @@ mat = 2 7 8
\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]

2
doc/UsingIntelMKL.dox
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@ -61,7 +61,7 @@ In addition you can coarsely select choose which parts will be substituted by de
<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

2
unsupported/Eigen/src/Eigenvalues/ArpackSelfAdjointEigenSolver.h
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@ -213,7 +213,7 @@ public:
* this object was used to solve the eigenproblem for the selfadjoint
* matrix \f$ A \f$, then the matrix returned by this function is the
* matrix \f$ V \f$ in the eigendecomposition \f$ A V = D V \f$.
* For the generalized eigenproblem, the matrix returned is the solution \f$A V = D B V
* For the generalized eigenproblem, the matrix returned is the solution \f$ A V = D B V \f$
*
* Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
* Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out

2
unsupported/Eigen/src/Splines/Spline.h
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@ -16,7 +16,7 @@ namespace Eigen
{
/**
* \ingroup Splines_Module
* \class Spline class
* \class Spline
* \brief A class representing multi-dimensional spline curves.
*
* The class represents B-splines with non-uniform knot vectors. Each control