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This commit is contained in:
Gael Guennebaud 2013-07-17 13:21:35 +02:00
commit 2f593ee67c
245 changed files with 8361 additions and 2515 deletions
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7
CMakeLists.txt
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@ -1,6 +1,6 @@
project(Eigen)
cmake_minimum_required(VERSION 2.6.2)
cmake_minimum_required(VERSION 2.8.2)
# guard against in-source builds
@ -150,11 +150,14 @@ if(NOT MSVC)
ei_add_cxx_compiler_flag("-fno-check-new")
ei_add_cxx_compiler_flag("-fno-common")
ei_add_cxx_compiler_flag("-fstrict-aliasing")
ei_add_cxx_compiler_flag("-wd981") # disbale ICC's "operands are evaluated in unspecified order" remark
ei_add_cxx_compiler_flag("-wd981") # disable ICC's "operands are evaluated in unspecified order" remark
ei_add_cxx_compiler_flag("-wd2304") # disbale ICC's "warning #2304: non-explicit constructor with single argument may cause implicit type conversion" produced by -Wnon-virtual-dtor
# The -ansi flag must be added last, otherwise it is also used as a linker flag by check_cxx_compiler_flag making it fails
# Moreover we should not set both -strict-ansi and -ansi
check_cxx_compiler_flag("-strict-ansi" COMPILER_SUPPORT_STRICTANSI)
ei_add_cxx_compiler_flag("-Qunused-arguments") # disable clang warning: argument unused during compilation: '-ansi'
if(COMPILER_SUPPORT_STRICTANSI)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -strict-ansi")
else()

5
CTestCustom.cmake.in
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@ -1,4 +1,3 @@
## A tribute to Dynamic!
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "33331")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "33331")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_WARNINGS "2000")
set(CTEST_CUSTOM_MAXIMUM_NUMBER_OF_ERRORS "2000")

8
Eigen/Core
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@ -40,6 +40,12 @@
// defined e.g. EIGEN_DONT_ALIGN) so it needs to be done before we do anything with vectorization.
#include "src/Core/util/Macros.h"
// Disable the ipa-cp-clone optimization flag with MinGW 6.x or newer (enabled by default with -O3)
// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
#if defined(__MINGW32__) && EIGEN_GNUC_AT_LEAST(4,6)
#pragma GCC optimize ("-fno-ipa-cp-clone")
#endif
#include <complex>
// this include file manages BLAS and MKL related macros
@ -266,8 +272,8 @@ using std::ptrdiff_t;
#include "src/Core/util/Constants.h"
#include "src/Core/util/ForwardDeclarations.h"
#include "src/Core/util/Meta.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/StaticAssert.h"
#include "src/Core/util/XprHelper.h"
#include "src/Core/util/Memory.h"
#include "src/Core/NumTraits.h"

2
Eigen/SPQRSupport
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@ -26,4 +26,4 @@
#include "src/CholmodSupport/CholmodSupport.h"
#include "src/SPQRSupport/SuiteSparseQRSupport.h"
#endif
#endif

14
Eigen/Sparse
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@ -1,15 +1,15 @@
#ifndef EIGEN_SPARSE_MODULE_H
#define EIGEN_SPARSE_MODULE_H
/** defgroup Sparse_modules Sparse modules
/** \defgroup Sparse_Module Sparse meta-module
*
* Meta-module including all related modules:
* - SparseCore
* - OrderingMethods
* - SparseCholesky
* - SparseLU
* - SparseQR
* - IterativeLinearSolvers
* - \ref SparseCore_Module
* - \ref OrderingMethods_Module
* - \ref SparseCholesky_Module
* - \ref SparseLU_Module
* - \ref SparseQR_Module
* - \ref IterativeLinearSolvers_Module
*
* \code
* #include <Eigen/Sparse>

11
Eigen/SparseCholesky
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@ -1,7 +1,17 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
#define EIGEN_SPARSECHOLESKY_MODULE_H
#include "SparseCore"
#include "OrderingMethods"
#include "src/Core/util/DisableStupidWarnings.h"
@ -26,7 +36,6 @@
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "src/SparseCholesky/SimplicialCholesky.h"
#ifndef EIGEN_MPL2_ONLY

3
Eigen/SparseLU
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@ -20,6 +20,9 @@
* Please, see the documentation of the SparseLU class for more details.
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
// Ordering interface
#include "OrderingMethods"

4
Eigen/SparseQR
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@ -20,6 +20,10 @@
*
*
*/
#include "src/misc/Solve.h"
#include "src/misc/SparseSolve.h"
#include "OrderingMethods"
#include "src/SparseCore/SparseColEtree.h"
#include "src/SparseQR/SparseQR.h"

37
Eigen/src/Cholesky/LDLT.h
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@ -259,7 +259,7 @@ template<> struct ldlt_inplace<Lower>
{
transpositions.setIdentity();
if(sign)
*sign = real(mat.coeff(0,0))>0 ? 1:-1;
*sign = numext::real(mat.coeff(0,0))>0 ? 1:-1;
return true;
}
@ -278,22 +278,13 @@ template<> struct ldlt_inplace<Lower>
// are compared; if any diagonal is negligible compared
// to the largest overall, the algorithm bails.
cutoff = abs(NumTraits<Scalar>::epsilon() * biggest_in_corner);
if(sign)
*sign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0 ? 1 : -1;
}
else if(sign)
{
// LDLT is not guaranteed to work for indefinite matrices, but let's try to get the sign right
int newSign = real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0;
if(newSign != *sign)
*sign = 0;
}
// Finish early if the matrix is not full rank.
if(biggest_in_corner < cutoff)
{
for(Index i = k; i < size; i++) transpositions.coeffRef(i) = i;
if(sign) *sign = 0;
break;
}
@ -309,11 +300,11 @@ template<> struct ldlt_inplace<Lower>
for(int i=k+1;i<index_of_biggest_in_corner;++i)
{
Scalar tmp = mat.coeffRef(i,k);
mat.coeffRef(i,k) = conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = conj(tmp);
mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
}
if(NumTraits<Scalar>::IsComplex)
mat.coeffRef(index_of_biggest_in_corner,k) = conj(mat.coeff(index_of_biggest_in_corner,k));
mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
}
// partition the matrix:
@ -334,6 +325,16 @@ template<> struct ldlt_inplace<Lower>
}
if((rs>0) && (abs(mat.coeffRef(k,k)) > cutoff))
A21 /= mat.coeffRef(k,k);
if(sign)
{
// LDLT is not guaranteed to work for indefinite matrices, but let's try to get the sign right
int newSign = numext::real(mat.diagonal().coeff(index_of_biggest_in_corner)) > 0;
if(k == 0)
*sign = newSign;
else if(*sign != newSign)
*sign = 0;
}
}
return true;
@ -349,7 +350,7 @@ template<> struct ldlt_inplace<Lower>
template<typename MatrixType, typename WDerived>
static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
{
using internal::isfinite;
using numext::isfinite;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
@ -367,9 +368,9 @@ template<> struct ldlt_inplace<Lower>
break;
// Update the diagonal terms
RealScalar dj = real(mat.coeff(j,j));
RealScalar dj = numext::real(mat.coeff(j,j));
Scalar wj = w.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*alpha + swj2;
mat.coeffRef(j,j) += swj2/alpha;
@ -380,7 +381,7 @@ template<> struct ldlt_inplace<Lower>
Index rs = size-j-1;
w.tail(rs) -= wj * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) += (sigma*conj(wj)/gamma)*w.tail(rs);
mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
}
return true;
}

10
Eigen/src/Cholesky/LLT.h
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@ -232,10 +232,10 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
RealScalar beta = 1;
for(Index j=0; j<n; ++j)
{
RealScalar Ljj = real(mat.coeff(j,j));
RealScalar dj = abs2(Ljj);
RealScalar Ljj = numext::real(mat.coeff(j,j));
RealScalar dj = numext::abs2(Ljj);
Scalar wj = temp.coeff(j);
RealScalar swj2 = sigma*abs2(wj);
RealScalar swj2 = sigma*numext::abs2(wj);
RealScalar gamma = dj*beta + swj2;
RealScalar x = dj + swj2/beta;
@ -251,7 +251,7 @@ static typename MatrixType::Index llt_rank_update_lower(MatrixType& mat, const V
{
temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
if(gamma != 0)
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*conj(wj)/gamma)*temp.tail(rs);
mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
}
}
}
@ -277,7 +277,7 @@ template<typename Scalar> struct llt_inplace<Scalar, Lower>
Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
RealScalar x = real(mat.coeff(k,k));
RealScalar x = numext::real(mat.coeff(k,k));
if (k>0) x -= A10.squaredNorm();
if (x<=RealScalar(0))
return k;

8
Eigen/src/CholmodSupport/CholmodSupport.h
View File

@ -126,7 +126,7 @@ cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
res.ncol = mat.cols();
res.nzmax = res.nrow * res.ncol;
res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
res.x = mat.derived().data();
res.x = (void*)(mat.derived().data());
res.z = 0;
internal::cholmod_configure_matrix<Scalar>(res);
@ -295,7 +295,8 @@ class CholmodBase : internal::noncopyable
eigen_assert(size==b.rows());
// note: cd stands for Cholmod Dense
cholmod_dense b_cd = viewAsCholmod(b.const_cast_derived());
Rhs& b_ref(b.const_cast_derived());
cholmod_dense b_cd = viewAsCholmod(b_ref);
cholmod_dense* x_cd = cholmod_solve(CHOLMOD_A, m_cholmodFactor, &b_cd, &m_cholmod);
if(!x_cd)
{
@ -312,6 +313,7 @@ class CholmodBase : internal::noncopyable
{
eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
const Index size = m_cholmodFactor->n;
EIGEN_UNUSED_VARIABLE(size);
eigen_assert(size==b.rows());
// note: cs stands for Cholmod Sparse
@ -344,7 +346,7 @@ class CholmodBase : internal::noncopyable
}
template<typename Stream>
void dumpMemory(Stream& s)
void dumpMemory(Stream& /*s*/)
{}
protected:

2
Eigen/src/Core/Array.h
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@ -111,7 +111,7 @@ class Array
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Array() : Base()
EIGEN_STRONG_INLINE Array() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED

4
Eigen/src/Core/ArrayWrapper.h
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@ -55,7 +55,7 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index rowId, Index colId) const
@ -175,7 +175,7 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
inline Index outerStride() const { return m_expression.outerStride(); }
inline Index innerStride() const { return m_expression.innerStride(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
inline ScalarWithConstIfNotLvalue* data() { return m_expression.const_cast_derived().data(); }
inline const Scalar* data() const { return m_expression.data(); }
inline CoeffReturnType coeff(Index rowId, Index colId) const

24
Eigen/src/Core/Assign.h
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@ -519,20 +519,20 @@ EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
namespace internal {
template<typename Derived, typename OtherDerived,
bool EvalBeforeAssigning = (int(OtherDerived::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = Derived::IsVectorAtCompileTime
&& OtherDerived::IsVectorAtCompileTime
&& ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
bool EvalBeforeAssigning = (int(internal::traits<OtherDerived>::Flags) & EvalBeforeAssigningBit) != 0,
bool NeedToTranspose = ((int(Derived::RowsAtCompileTime) == 1 && int(OtherDerived::ColsAtCompileTime) == 1)
| // FIXME | instead of || to please GCC 4.4.0 stupid warning "suggest parentheses around &&".
// revert to || as soon as not needed anymore.
(int(Derived::ColsAtCompileTime) == 1 && int(OtherDerived::RowsAtCompileTime) == 1))
&& int(Derived::SizeAtCompileTime) != 1>
struct assign_selector;
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,false> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.derived()); }
template<typename ActualDerived, typename ActualOtherDerived>
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { other.evalTo(dst); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,false> {
@ -543,6 +543,8 @@ template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,false,true> {
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE Derived& run(Derived& dst, const OtherDerived& other) { return dst.lazyAssign(other.transpose()); }
template<typename ActualDerived, typename ActualOtherDerived>
static EIGEN_STRONG_INLINE Derived& evalTo(ActualDerived& dst, const ActualOtherDerived& other) { Transpose<ActualDerived> dstTrans(dst); other.evalTo(dstTrans); return dst; }
};
template<typename Derived, typename OtherDerived>
struct assign_selector<Derived,OtherDerived,true,true> {
@ -582,16 +584,14 @@ template<typename Derived>
template <typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
{
other.derived().evalTo(derived());
return derived();
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
}
template<typename Derived>
template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
other.evalTo(derived());
return derived();
return internal::assign_selector<Derived,OtherDerived,false>::evalTo(derived(), other.derived());
}
} // end namespace Eigen

2
Eigen/src/Core/CommaInitializer.h
View File

@ -124,6 +124,8 @@ struct CommaInitializer
*
* Example: \include MatrixBase_set.cpp
* Output: \verbinclude MatrixBase_set.out
*
* \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
*
* \sa CommaInitializer::finished(), class CommaInitializer
*/

4
Eigen/src/Core/CwiseUnaryView.h
View File

@ -56,8 +56,7 @@ template<typename ViewOp, typename MatrixType, typename StorageKind>
class CwiseUnaryViewImpl;
template<typename ViewOp, typename MatrixType>
class CwiseUnaryView : internal::no_assignment_operator,
public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, typename internal::traits<MatrixType>::StorageKind>
{
public:
@ -99,6 +98,7 @@ class CwiseUnaryViewImpl<ViewOp,MatrixType,Dense>
typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType> >::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
inline Scalar* data() { return &coeffRef(0); }
inline const Scalar* data() const { return &coeff(0); }

12
Eigen/src/Core/DenseBase.h
View File

@ -13,6 +13,16 @@
namespace Eigen {
namespace internal {
// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
// This dummy function simply aims at checking that at compile time.
static inline void check_DenseIndex_is_signed() {
EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
}
} // end namespace internal
/** \class DenseBase
* \ingroup Core_Module
*
@ -286,7 +296,7 @@ template<typename Derived> class DenseBase
EIGEN_DEVICE_FUNC
Eigen::Transpose<Derived> transpose();
typedef const Transpose<const Derived> ConstTransposeReturnType;
typedef typename internal::add_const<Transpose<const Derived> >::type ConstTransposeReturnType;
EIGEN_DEVICE_FUNC
ConstTransposeReturnType transpose() const;
EIGEN_DEVICE_FUNC

16
Eigen/src/Core/DenseStorage.h
View File

@ -118,7 +118,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
{
internal::plain_array<T,Size,_Options> m_data;
public:
EIGEN_DEVICE_FUNC inline explicit DenseStorage() {}
EIGEN_DEVICE_FUNC inline DenseStorage() {}
EIGEN_DEVICE_FUNC inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()) {}
EIGEN_DEVICE_FUNC inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
@ -135,7 +135,7 @@ template<typename T, int Size, int _Rows, int _Cols, int _Options> class DenseSt
template<typename T, int _Rows, int _Cols, int _Options> class DenseStorage<T, 0, _Rows, _Cols, _Options>
{
public:
inline explicit DenseStorage() {}
inline DenseStorage() {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) {}
inline DenseStorage(DenseIndex,DenseIndex,DenseIndex) {}
inline void swap(DenseStorage& ) {}
@ -164,7 +164,7 @@ template<typename T, int Size, int _Options> class DenseStorage<T, Size, Dynamic
DenseIndex m_rows;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage() : m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex nbCols) : m_rows(nbRows), m_cols(nbCols) {}
@ -184,7 +184,7 @@ template<typename T, int Size, int _Cols, int _Options> class DenseStorage<T, Si
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_rows(0) {}
inline DenseStorage() : m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
inline DenseStorage(DenseIndex, DenseIndex nbRows, DenseIndex) : m_rows(nbRows) {}
@ -203,7 +203,7 @@ template<typename T, int Size, int _Rows, int _Options> class DenseStorage<T, Si
internal::plain_array<T,Size,_Options> m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_cols(0) {}
inline DenseStorage() : m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
inline DenseStorage(DenseIndex, DenseIndex, DenseIndex nbCols) : m_cols(nbCols) {}
@ -223,7 +223,7 @@ template<typename T, int _Options> class DenseStorage<T, Dynamic, Dynamic, Dynam
DenseIndex m_rows;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert)
: m_data(0), m_rows(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex nbCols)
@ -264,7 +264,7 @@ template<typename T, int _Rows, int _Options> class DenseStorage<T, Dynamic, _Ro
T *m_data;
DenseIndex m_cols;
public:
inline explicit DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage() : m_data(0), m_cols(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
inline DenseStorage(DenseIndex size, DenseIndex, DenseIndex nbCols) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_cols(nbCols)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }
@ -300,7 +300,7 @@ template<typename T, int _Cols, int _Options> class DenseStorage<T, Dynamic, Dyn
T *m_data;
DenseIndex m_rows;
public:
inline explicit DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage() : m_data(0), m_rows(0) {}
inline DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
inline DenseStorage(DenseIndex size, DenseIndex nbRows, DenseIndex) : m_data(internal::conditional_aligned_new_auto<T,(_Options&DontAlign)==0>(size)), m_rows(nbRows)
{ EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN }

4
Eigen/src/Core/Diagonal.h
View File

@ -75,7 +75,7 @@ template<typename MatrixType, int _DiagIndex> class Diagonal
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
inline Index rows() const
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{ return m_index.value()<0 ? (std::min<Index>)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min<Index>)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
inline Index cols() const { return 1; }
@ -172,7 +172,7 @@ MatrixBase<Derived>::diagonal()
/** This is the const version of diagonal(). */
template<typename Derived>
inline const typename MatrixBase<Derived>::ConstDiagonalReturnType
inline typename MatrixBase<Derived>::ConstDiagonalReturnType
MatrixBase<Derived>::diagonal() const
{
return ConstDiagonalReturnType(derived());

37
Eigen/src/Core/DiagonalProduct.h
View File

@ -26,14 +26,15 @@ struct traits<DiagonalProduct<MatrixType, DiagonalType, ProductOrder> >
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
_StorageOrder = MatrixType::Flags & RowMajorBit ? RowMajor : ColMajor,
_PacketOnDiag = !((int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)),
_ScalarAccessOnDiag = !((int(_StorageOrder) == ColMajor && int(ProductOrder) == OnTheLeft)
||(int(_StorageOrder) == RowMajor && int(ProductOrder) == OnTheRight)),
_SameTypes = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
// FIXME currently we need same types, but in the future the next rule should be the one
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && ((!_PacketOnDiag) || (bool(int(DiagonalType::Flags)&PacketAccessBit))),
//_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && ((!_PacketOnDiag) || (_SameTypes && bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_Vectorizable = bool(int(MatrixType::Flags)&PacketAccessBit) && _SameTypes && (_ScalarAccessOnDiag || (bool(int(DiagonalType::DiagonalVectorType::Flags)&PacketAccessBit))),
_LinearAccessMask = (RowsAtCompileTime==1 || ColsAtCompileTime==1) ? LinearAccessBit : 0,
Flags = (HereditaryBits & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0),
Flags = ((HereditaryBits|_LinearAccessMask) & (unsigned int)(MatrixType::Flags)) | (_Vectorizable ? PacketAccessBit : 0) | AlignedBit,//(int(MatrixType::Flags)&int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit),
CoeffReadCost = NumTraits<Scalar>::MulCost + MatrixType::CoeffReadCost + DiagonalType::DiagonalVectorType::CoeffReadCost
};
};
@ -54,13 +55,21 @@ class DiagonalProduct : internal::no_assignment_operator,
eigen_assert(diagonal.diagonal().size() == (ProductOrder == OnTheLeft ? matrix.rows() : matrix.cols()));
}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
EIGEN_STRONG_INLINE Index rows() const { return m_matrix.rows(); }
EIGEN_STRONG_INLINE Index cols() const { return m_matrix.cols(); }
const Scalar coeff(Index row, Index col) const
EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
{
return m_diagonal.diagonal().coeff(ProductOrder == OnTheLeft ? row : col) * m_matrix.coeff(row, col);
}
EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return coeff(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index row, Index col) const
@ -69,11 +78,19 @@ class DiagonalProduct : internal::no_assignment_operator,
StorageOrder = Flags & RowMajorBit ? RowMajor : ColMajor
};
const Index indexInDiagonalVector = ProductOrder == OnTheLeft ? row : col;
return packet_impl<LoadMode>(row,col,indexInDiagonalVector,typename internal::conditional<
((int(StorageOrder) == RowMajor && int(ProductOrder) == OnTheLeft)
||(int(StorageOrder) == ColMajor && int(ProductOrder) == OnTheRight)), internal::true_type, internal::false_type>::type());
}
template<int LoadMode>
EIGEN_STRONG_INLINE PacketScalar packet(Index idx) const
{
enum {
StorageOrder = int(MatrixType::Flags) & RowMajorBit ? RowMajor : ColMajor
};
return packet<LoadMode>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
}
protected:
template<int LoadMode>
@ -88,7 +105,7 @@ class DiagonalProduct : internal::no_assignment_operator,
{
enum {
InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && ((InnerSize%16) == 0)) ? Aligned : Unaligned
DiagonalVectorPacketLoadMode = (LoadMode == Aligned && (((InnerSize%16) == 0) || (int(DiagonalType::DiagonalVectorType::Flags)&AlignedBit)==AlignedBit) ? Aligned : Unaligned)
};
return internal::pmul(m_matrix.template packet<LoadMode>(row, col),
m_diagonal.diagonal().template packet<DiagonalVectorPacketLoadMode>(id));

5
Eigen/src/Core/Dot.h
View File

@ -115,7 +115,7 @@ MatrixBase<Derived>::eigen2_dot(const MatrixBase<OtherDerived>& other) const
template<typename Derived>
EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return numext::real((*this).cwiseAbs2().sum());
}
/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
@ -170,6 +170,7 @@ struct lpNorm_selector
EIGEN_DEVICE_FUNC
static inline RealScalar run(const MatrixBase<Derived>& m)
{
using std::pow;
return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
}
};
@ -235,7 +236,7 @@ bool MatrixBase<Derived>::isOrthogonal
{
typename internal::nested<Derived,2>::type nested(derived());
typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
return internal::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
}
/** \returns true if *this is approximately an unitary matrix,

24
Eigen/src/Core/Functors.h
View File

@ -171,7 +171,8 @@ struct functor_traits<scalar_hypot_op<Scalar> > {
*/
template<typename Scalar, typename OtherScalar> struct scalar_binary_pow_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_binary_pow_op)
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return internal::pow(a, b); }
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a, const OtherScalar& b) const { return numext::pow(a, b); }
};
template<typename Scalar, typename OtherScalar>
struct functor_traits<scalar_binary_pow_op<Scalar,OtherScalar> > {
@ -310,7 +311,8 @@ struct functor_traits<scalar_abs_op<Scalar> >
template<typename Scalar> struct scalar_abs2_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_abs2_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return internal::abs2(a); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a,a); }
@ -326,7 +328,8 @@ struct functor_traits<scalar_abs2_op<Scalar> >
*/
template<typename Scalar> struct scalar_conjugate_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_conjugate_op)
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using internal::conj; return conj(a); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { using numext::conj; return conj(a); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
};
@ -363,7 +366,8 @@ template<typename Scalar>
struct scalar_real_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::real(a); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); }
};
template<typename Scalar>
struct functor_traits<scalar_real_op<Scalar> >
@ -378,7 +382,8 @@ template<typename Scalar>
struct scalar_imag_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return internal::imag(a); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_op<Scalar> >
@ -393,7 +398,8 @@ template<typename Scalar>
struct scalar_real_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_real_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::real_ref(*const_cast<Scalar*>(&a)); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_real_ref_op<Scalar> >
@ -408,7 +414,8 @@ template<typename Scalar>
struct scalar_imag_ref_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_imag_ref_op)
typedef typename NumTraits<Scalar>::Real result_type;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return internal::imag_ref(*const_cast<Scalar*>(&a)); }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); }
};
template<typename Scalar>
struct functor_traits<scalar_imag_ref_op<Scalar> >
@ -804,7 +811,8 @@ struct scalar_pow_op {
// FIXME default copy constructors seems bugged with std::complex<>
inline scalar_pow_op(const scalar_pow_op& other) : m_exponent(other.m_exponent) { }
inline scalar_pow_op(const Scalar& exponent) : m_exponent(exponent) {}
EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return internal::pow(a, m_exponent); }
EIGEN_DEVICE_FUNC
inline Scalar operator() (const Scalar& a) const { return numext::pow(a, m_exponent); }
const Scalar m_exponent;
};
template<typename Scalar>

4
Eigen/src/Core/Fuzzy.h
View File

@ -45,7 +45,7 @@ struct isMuchSmallerThan_object_selector
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec) * y.cwiseAbs2().sum();
return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
}
};
@ -65,7 +65,7 @@ struct isMuchSmallerThan_scalar_selector
EIGEN_DEVICE_FUNC
static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
{
return x.cwiseAbs2().sum() <= abs2(prec * y);
return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
}
};

2
Eigen/src/Core/GeneralProduct.h
View File

@ -435,7 +435,7 @@ template<> struct gemv_selector<OnTheRight,ColMajor,true>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);

3
Eigen/src/Core/GenericPacketMath.h
View File

@ -105,8 +105,9 @@ template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pnegate(const Packet& a) { return -a; }
/** \internal \returns conj(a) (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
pconj(const Packet& a) { return conj(a); }
pconj(const Packet& a) { return numext::conj(a); }
/** \internal \returns a * b (coeff-wise) */
template<typename Packet> EIGEN_DEVICE_FUNC inline Packet

3
Eigen/src/Core/GlobalFunctions.h
View File

@ -39,6 +39,7 @@ namespace Eigen
{
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op)
EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op)
@ -86,6 +87,6 @@ namespace Eigen
}
}
// TODO: cleanly disable those functions that are not supported on Array (internal::real_ref, internal::random, internal::isApprox...)
// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
#endif // EIGEN_GLOBAL_FUNCTIONS_H

2
Eigen/src/Core/IO.h
View File

@ -55,7 +55,7 @@ struct IOFormat
const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
const std::string& _matPrefix="", const std::string& _matSuffix="")
: matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
rowSpacer(""), coeffSeparator(_coeffSeparator), precision(_precision), flags(_flags)
{
int i = int(matSuffix.length())-1;
while (i>=0 && matSuffix[i]!='\n')

249
Eigen/src/Core/MathFunctions.h
View File

@ -51,16 +51,15 @@ struct global_math_functions_filtering_base
typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
};
#define EIGEN_MATHFUNC_IMPL(func, scalar) func##_impl<typename global_math_functions_filtering_base<scalar>::type>
#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename func##_retval<typename global_math_functions_filtering_base<scalar>::type>::type
#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
/****************************************************************************
* Implementation of real *
****************************************************************************/
template<typename Scalar>
struct real_impl
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct real_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
@ -70,36 +69,32 @@ struct real_impl
}
};
template<typename RealScalar>
struct real_impl<std::complex<RealScalar> >
template<typename Scalar>
struct real_default_impl<Scalar,true>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const std::complex<RealScalar>& x)
static inline RealScalar run(const Scalar& x)
{
using std::real;
return real(x);
}
};
template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
template<typename Scalar>
struct real_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
}
/****************************************************************************
* Implementation of imag *
****************************************************************************/
template<typename Scalar>
struct imag_impl
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct imag_default_impl
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
@ -109,30 +104,26 @@ struct imag_impl
}
};
template<typename RealScalar>
struct imag_impl<std::complex<RealScalar> >
template<typename Scalar>
struct imag_default_impl<Scalar,true>
{
typedef typename NumTraits<Scalar>::Real RealScalar;
EIGEN_DEVICE_FUNC
static inline RealScalar run(const std::complex<RealScalar>& x)
static inline RealScalar run(const Scalar& x)
{
using std::imag;
return imag(x);
}
};
template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
template<typename Scalar>
struct imag_retval
{
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
}
/****************************************************************************
* Implementation of real_ref *
****************************************************************************/
@ -159,20 +150,6 @@ struct real_ref_retval
typedef typename NumTraits<Scalar>::Real & type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
{
return real_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
}
/****************************************************************************
* Implementation of imag_ref *
****************************************************************************/
@ -217,25 +194,11 @@ struct imag_ref_retval
typedef typename NumTraits<Scalar>::Real & type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
{
return imag_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
}
/****************************************************************************
* Implementation of conj *
****************************************************************************/
template<typename Scalar>
template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
struct conj_impl
{
EIGEN_DEVICE_FUNC
@ -245,11 +208,11 @@ struct conj_impl
}
};
template<typename RealScalar>
struct conj_impl<std::complex<RealScalar> >
template<typename Scalar>
struct conj_impl<Scalar,true>
{
EIGEN_DEVICE_FUNC
static inline std::complex<RealScalar> run(const std::complex<RealScalar>& x)
static inline Scalar run(const Scalar& x)
{
using std::conj;
return conj(x);
@ -262,13 +225,6 @@ struct conj_retval
typedef Scalar type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
}
/****************************************************************************
* Implementation of abs2 *
****************************************************************************/
@ -300,13 +256,6 @@ struct abs2_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
}
/****************************************************************************
* Implementation of norm1 *
****************************************************************************/
@ -343,13 +292,6 @@ struct norm1_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
}
/****************************************************************************
* Implementation of hypot *
****************************************************************************/
@ -367,6 +309,7 @@ struct hypot_impl
RealScalar _x = abs(x);
RealScalar _y = abs(y);
RealScalar p = (max)(_x, _y);
if(p==RealScalar(0)) return 0;
RealScalar q = (min)(_x, _y);
RealScalar qp = q/p;
return p * sqrt(RealScalar(1) + qp*qp);
@ -379,12 +322,6 @@ struct hypot_retval
typedef typename NumTraits<Scalar>::Real type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of cast *
****************************************************************************/
@ -447,12 +384,6 @@ struct atanh2_retval
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of pow *
****************************************************************************/
@ -496,12 +427,6 @@ struct pow_retval
typedef Scalar type;
};
template<typename Scalar>
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
}
/****************************************************************************
* Implementation of random *
****************************************************************************/
@ -600,11 +525,10 @@ struct random_default_impl<Scalar, false, true>
#else
enum { rand_bits = floor_log2<(unsigned int)(RAND_MAX)+1>::value,
scalar_bits = sizeof(Scalar) * CHAR_BIT,
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits))
shift = EIGEN_PLAIN_ENUM_MAX(0, int(rand_bits) - int(scalar_bits)),
offset = NumTraits<Scalar>::IsSigned ? (1 << (EIGEN_PLAIN_ENUM_MIN(rand_bits,scalar_bits)-1)) : 0
};
Scalar x = Scalar(std::rand() >> shift);
Scalar offset = NumTraits<Scalar>::IsSigned ? Scalar(1 << (rand_bits-1)) : Scalar(0);
return x - offset;
return Scalar((std::rand() >> shift) - offset);
#endif
}
};
@ -636,6 +560,111 @@ inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
}
} // end namespace internal
/****************************************************************************
* Generic math function *
****************************************************************************/
namespace numext {
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) >::type real_ref(const Scalar& x)
{
return internal::real_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline typename internal::add_const_on_value_type< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) >::type imag_ref(const Scalar& x)
{
return internal::imag_ref_impl<Scalar>::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
{
return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(atanh2, Scalar) atanh2(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(atanh2, Scalar)::run(x, y);
}
template<typename Scalar>
EIGEN_DEVICE_FUNC
inline EIGEN_MATHFUNC_RETVAL(pow, Scalar) pow(const Scalar& x, const Scalar& y)
{
return EIGEN_MATHFUNC_IMPL(pow, Scalar)::run(x, y);
}
// std::isfinite is non standard, so let's define our own version,
// even though it is not very efficient.
template<typename T>
EIGEN_DEVICE_FUNC
bool (isfinite)(const T& x)
{
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
}
} // end namespace numext
namespace internal {
/****************************************************************************
* Implementation of fuzzy comparisons *
****************************************************************************/
@ -697,12 +726,12 @@ struct scalar_fuzzy_default_impl<Scalar, true, false>
template<typename OtherScalar>
static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
{
return abs2(x) <= abs2(y) * prec * prec;
return numext::abs2(x) <= numext::abs2(y) * prec * prec;
}
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
EIGEN_USING_STD_MATH(min);
return abs2(x - y) <= (min)(abs2(x), abs2(y)) * prec * prec;
return numext::abs2(x - y) <= (min)(numext::abs2(x), numext::abs2(y)) * prec * prec;
}
};
@ -766,17 +795,7 @@ template<> struct scalar_fuzzy_impl<bool>
};
/****************************************************************************
* Special functions *
****************************************************************************/
// std::isfinite is non standard, so let's define our own version,
// even though it is not very efficient.
template<typename T> bool (isfinite)(const T& x)
{
return x<NumTraits<T>::highest() && x>NumTraits<T>::lowest();
}
} // end namespace internal
} // end namespace Eigen

2
Eigen/src/Core/Matrix.h
View File

@ -205,7 +205,7 @@ class Matrix
* \sa resize(Index,Index)
*/
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit Matrix() : Base()
EIGEN_STRONG_INLINE Matrix() : Base()
{
Base::_check_template_params();
EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED

4
Eigen/src/Core/MatrixBase.h
View File

@ -232,8 +232,8 @@ template<typename Derived> class MatrixBase
typedef Diagonal<Derived> DiagonalReturnType;
DiagonalReturnType diagonal();
typedef const Diagonal<const Derived> ConstDiagonalReturnType;
const ConstDiagonalReturnType diagonal() const;
typedef typename internal::add_const<Diagonal<const Derived> >::type ConstDiagonalReturnType;
ConstDiagonalReturnType diagonal() const;
template<int Index> struct DiagonalIndexReturnType { typedef Diagonal<Derived,Index> Type; };
template<int Index> struct ConstDiagonalIndexReturnType { typedef const Diagonal<const Derived,Index> Type; };

4
Eigen/src/Core/NoAlias.h
View File

@ -86,6 +86,10 @@ class NoAlias
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE ExpressionType& operator-=(const CoeffBasedProduct<Lhs,Rhs,NestingFlags>& other)
{ return m_expression.derived() -= CoeffBasedProduct<Lhs,Rhs,NestByRefBit>(other.lhs(), other.rhs()); }
template<typename OtherDerived>
ExpressionType& operator=(const ReturnByValue<OtherDerived>& func)
{ return m_expression = func; }
#endif
EIGEN_DEVICE_FUNC

2
Eigen/src/Core/PlainObjectBase.h
View File

@ -437,7 +437,7 @@ class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE explicit PlainObjectBase() : m_storage()
EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
{
// _check_template_params();
// EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED

2
Eigen/src/Core/ReturnByValue.h
View File

@ -48,7 +48,7 @@ struct nested<ReturnByValue<Derived>, n, PlainObject>
} // end namespace internal
template<typename Derived> class ReturnByValue
: public internal::dense_xpr_base< ReturnByValue<Derived> >::type
: internal::no_assignment_operator, public internal::dense_xpr_base< ReturnByValue<Derived> >::type
{
public:
typedef typename internal::traits<Derived>::ReturnType ReturnType;

12
Eigen/src/Core/SelfAdjointView.h
View File

@ -214,9 +214,9 @@ struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), U
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Upper), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row < col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
@ -239,9 +239,9 @@ struct triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), U
triangular_assignment_selector<Derived1, Derived2, (SelfAdjoint|Lower), UnrollCount-1, ClearOpposite>::run(dst, src);
if(row == col)
dst.coeffRef(row, col) = real(src.coeff(row, col));
dst.coeffRef(row, col) = numext::real(src.coeff(row, col));
else if(row > col)
dst.coeffRef(col, row) = conj(dst.coeffRef(row, col) = src.coeff(row, col));
dst.coeffRef(col, row) = numext::conj(dst.coeffRef(row, col) = src.coeff(row, col));
}
};
@ -262,7 +262,7 @@ struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Upper, Dyn
for(Index i = 0; i < j; ++i)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(j, j, src);
}
@ -280,7 +280,7 @@ struct triangular_assignment_selector<Derived1, Derived2, SelfAdjoint|Lower, Dyn
for(Index j = 0; j < i; ++j)
{
dst.copyCoeff(i, j, src);
dst.coeffRef(j,i) = conj(dst.coeff(i,j));
dst.coeffRef(j,i) = numext::conj(dst.coeff(i,j));
}
dst.copyCoeff(i, i, src);
}

10
Eigen/src/Core/StableNorm.h
View File

@ -20,7 +20,7 @@ inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& sc
Scalar max = bl.cwiseAbs().maxCoeff();
if (max>scale)
{
ssq = ssq * abs2(scale/max);
ssq = ssq * numext::abs2(scale/max);
scale = max;
invScale = Scalar(1)/scale;
}
@ -84,9 +84,9 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
for(typename Derived::InnerIterator it(vec, 0); it; ++it)
{
RealScalar ax = abs(it.value());
if(ax > ab2) abig += internal::abs2(ax*s2m);
else if(ax < b1) asml += internal::abs2(ax*s1m);
else amed += internal::abs2(ax);
if(ax > ab2) abig += numext::abs2(ax*s2m);
else if(ax < b1) asml += numext::abs2(ax*s1m);
else amed += numext::abs2(ax);
}
if(abig > RealScalar(0))
{
@ -120,7 +120,7 @@ blueNorm_impl(const EigenBase<Derived>& _vec)
if(asml <= abig*relerr)
return abig;
else
return abig * sqrt(RealScalar(1) + internal::abs2(asml/abig));
return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
}
} // end namespace internal

9
Eigen/src/Core/Transpose.h
View File

@ -107,6 +107,7 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
EIGEN_DEVICE_FUNC inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
EIGEN_DEVICE_FUNC inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
@ -215,7 +216,7 @@ DenseBase<Derived>::transpose()
*
* \sa transposeInPlace(), adjoint() */
template<typename Derived>
inline const typename DenseBase<Derived>::ConstTransposeReturnType
inline typename DenseBase<Derived>::ConstTransposeReturnType
DenseBase<Derived>::transpose() const
{
return ConstTransposeReturnType(derived());
@ -261,7 +262,7 @@ struct inplace_transpose_selector;
template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,true> { // square matrix
static void run(MatrixType& m) {
m.template triangularView<StrictlyUpper>().swap(m.transpose());
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
}
};
@ -269,7 +270,7 @@ template<typename MatrixType>
struct inplace_transpose_selector<MatrixType,false> { // non square matrix
static void run(MatrixType& m) {
if (m.rows()==m.cols())
m.template triangularView<StrictlyUpper>().swap(m.transpose());
m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose());
else
m = m.transpose().eval();
}
@ -396,7 +397,7 @@ struct checkTransposeAliasing_impl
eigen_assert((!check_transpose_aliasing_run_time_selector
<typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived>
::run(extract_data(dst), other))
&& "aliasing detected during tranposition, use transposeInPlace() "
&& "aliasing detected during transposition, use transposeInPlace() "
"or evaluate the rhs into a temporary using .eval()");
}

3
Eigen/src/Core/arch/AltiVec/PacketMath.h
View File

@ -173,6 +173,9 @@ template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return psub<Packet4f>(p4f_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return psub<Packet4i>(p4i_ZERO, a); }
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_madd(a,b,p4f_ZERO); }
/* Commented out: it's actually slower than processing it scalar
*

10
Eigen/src/Core/arch/NEON/Complex.h
View File

@ -68,7 +68,6 @@ template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
{
Packet4f v1, v2;
float32x2_t a_lo, a_hi;
// Get the real values of a | a1_re | a1_re | a2_re | a2_re |
v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
@ -81,9 +80,7 @@ template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, con
// Conjugate v2
v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR));
// Swap real/imag elements in v2.
a_lo = vrev64_f32(vget_low_f32(v2));
a_hi = vrev64_f32(vget_high_f32(v2));
v2 = vcombine_f32(a_lo, a_hi);
v2 = vrev64q_f32(v2);
// Add and return the result
return Packet2cf(vaddq_f32(v1, v2));
}
@ -241,13 +238,10 @@ template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, con
// TODO optimize it for AltiVec
Packet2cf res = conj_helper<Packet2cf,Packet2cf,false,true>().pmul(a,b);
Packet4f s, rev_s;
float32x2_t a_lo, a_hi;
// this computes the norm
s = vmulq_f32(b.v, b.v);
a_lo = vrev64_f32(vget_low_f32(s));
a_hi = vrev64_f32(vget_high_f32(s));
rev_s = vcombine_f32(a_lo, a_hi);
rev_s = vrev64q_f32(s);
return Packet2cf(pdiv(res.v, vaddq_f32(s,rev_s)));
}

11
Eigen/src/Core/arch/NEON/PacketMath.h
View File

@ -115,6 +115,9 @@ template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const
template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); }
template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); }
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); }
@ -188,15 +191,15 @@ template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from) { EI
template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
{
float32x2_t lo, hi;
lo = vdup_n_f32(*from);
hi = vdup_n_f32(*(from+1));
lo = vld1_dup_f32(from);
hi = vld1_dup_f32(from+1);
return vcombine_f32(lo, hi);
}
template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int* from)
{
int32x2_t lo, hi;
lo = vdup_n_s32(*from);
hi = vdup_n_s32(*(from+1));
lo = vld1_dup_s32(from);
hi = vld1_dup_s32(from+1);
return vcombine_s32(lo, hi);
}

8
Eigen/src/Core/arch/SSE/Complex.h
View File

@ -81,8 +81,8 @@ template<> EIGEN_STRONG_INLINE Packet2cf por <Packet2cf>(const Packet2cf& a,
template<> EIGEN_STRONG_INLINE Packet2cf pxor <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(a.v,b.v)); }
template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); }
template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
{
@ -104,8 +104,8 @@ template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<flo
template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> * to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr) { _mm_prefetch((const char*)(addr), _MM_HINT_T0); }

2
Eigen/src/Core/arch/SSE/MathFunctions.h
View File

@ -450,7 +450,7 @@ Packet4f psqrt<Packet4f>(const Packet4f& _x)
Packet4f half = pmul(_x, pset1<Packet4f>(.5f));
/* select only the inverse sqrt of non-zero inputs */
Packet4f non_zero_mask = _mm_cmpgt_ps(_x, pset1<Packet4f>(std::numeric_limits<float>::epsilon()));
Packet4f non_zero_mask = _mm_cmpge_ps(_x, pset1<Packet4f>((std::numeric_limits<float>::min)()));
Packet4f x = _mm_and_ps(non_zero_mask, _mm_rsqrt_ps(_x));
x = pmul(x, psub(pset1<Packet4f>(1.5f), pmul(half, pmul(x,x))));

4
Eigen/src/Core/arch/SSE/PacketMath.h
View File

@ -141,6 +141,10 @@ template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
return psub(_mm_setr_epi32(0,0,0,0), a);
}
template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); }
template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); }
template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)

2
Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -86,7 +86,7 @@ EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,ColMajor,Co
conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
if(ConjugateRhs)
alpha = conj(alpha);
alpha = numext::conj(alpha);
enum { AllAligned = 0, EvenAligned, FirstAligned, NoneAligned };
const Index columnsAtOnce = 4;

34
Eigen/src/Core/products/SelfadjointMatrixMatrix.h
View File

@ -30,9 +30,9 @@ struct symm_pack_lhs
for(Index k=i; k<i+BlockRows; k++)
{
for(Index w=0; w<h; w++)
blockA[count++] = conj(lhs(k, i+w)); // transposed
blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
blockA[count++] = real(lhs(k,k)); // real (diagonal)
blockA[count++] = numext::real(lhs(k,k)); // real (diagonal)
for(Index w=h+1; w<BlockRows; w++)
blockA[count++] = lhs(i+w, k); // normal
@ -41,7 +41,7 @@ struct symm_pack_lhs
// transposed copy
for(Index k=i+BlockRows; k<cols; k++)
for(Index w=0; w<BlockRows; w++)
blockA[count++] = conj(lhs(k, i+w)); // transposed
blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
}
void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
{
@ -65,10 +65,10 @@ struct symm_pack_lhs
for(Index k=0; k<i; k++)
blockA[count++] = lhs(i, k); // normal
blockA[count++] = real(lhs(i, i)); // real (diagonal)
blockA[count++] = numext::real(lhs(i, i)); // real (diagonal)
for(Index k=i+1; k<cols; k++)
blockA[count++] = conj(lhs(k, i)); // transposed
blockA[count++] = numext::conj(lhs(k, i)); // transposed
}
}
};
@ -107,12 +107,12 @@ struct symm_pack_rhs
// transpose
for(Index k=k2; k<j2; k++)
{
blockB[count+0] = conj(rhs(j2+0,k));
blockB[count+1] = conj(rhs(j2+1,k));
blockB[count+0] = numext::conj(rhs(j2+0,k));
blockB[count+1] = numext::conj(rhs(j2+1,k));
if (nr==4)
{
blockB[count+2] = conj(rhs(j2+2,k));
blockB[count+3] = conj(rhs(j2+3,k));
blockB[count+2] = numext::conj(rhs(j2+2,k));
blockB[count+3] = numext::conj(rhs(j2+3,k));
}
count += nr;
}
@ -124,11 +124,11 @@ struct symm_pack_rhs
for (Index w=0 ; w<h; ++w)
blockB[count+w] = rhs(k,j2+w);
blockB[count+h] = real(rhs(k,k));
blockB[count+h] = numext::real(rhs(k,k));
// transpose
for (Index w=h+1 ; w<nr; ++w)
blockB[count+w] = conj(rhs(j2+w,k));
blockB[count+w] = numext::conj(rhs(j2+w,k));
count += nr;
++h;
}
@ -151,12 +151,12 @@ struct symm_pack_rhs
{
for(Index k=k2; k<end_k; k++)
{
blockB[count+0] = conj(rhs(j2+0,k));
blockB[count+1] = conj(rhs(j2+1,k));
blockB[count+0] = numext::conj(rhs(j2+0,k));
blockB[count+1] = numext::conj(rhs(j2+1,k));
if (nr==4)
{
blockB[count+2] = conj(rhs(j2+2,k));
blockB[count+3] = conj(rhs(j2+3,k));
blockB[count+2] = numext::conj(rhs(j2+2,k));
blockB[count+3] = numext::conj(rhs(j2+3,k));
}
count += nr;
}
@ -169,13 +169,13 @@ struct symm_pack_rhs
Index half = (std::min)(end_k,j2);
for(Index k=k2; k<half; k++)
{
blockB[count] = conj(rhs(j2,k));
blockB[count] = numext::conj(rhs(j2,k));
count += 1;
}
if(half==j2 && half<k2+rows)
{
blockB[count] = real(rhs(j2,j2));
blockB[count] = numext::real(rhs(j2,j2));
count += 1;
}
else

12
Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -59,7 +59,7 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, IsRowMajor), ConjugateRhs> pcj0;
conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && EIGEN_LOGICAL_XOR(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;
Scalar cjAlpha = ConjugateRhs ? conj(alpha) : alpha;
Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
// FIXME this copy is now handled outside product_selfadjoint_vector, so it could probably be removed.
// if the rhs is not sequentially stored in memory we copy it to a temporary buffer,
@ -98,8 +98,8 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
size_t alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed
res[j] += cjd.pmul(internal::real(A0[j]), t0);
res[j+1] += cjd.pmul(internal::real(A1[j+1]), t1);
res[j] += cjd.pmul(numext::real(A0[j]), t0);
res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);
if(FirstTriangular)
{
res[j] += cj0.pmul(A1[j], t1);
@ -114,8 +114,8 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
for (size_t i=starti; i<alignedStart; ++i)
{
res[i] += t0 * A0[i] + t1 * A1[i];
t2 += conj(A0[i]) * rhs[i];
t3 += conj(A1[i]) * rhs[i];
t2 += numext::conj(A0[i]) * rhs[i];
t3 += numext::conj(A1[i]) * rhs[i];
}
// Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
// gcc 4.2 does this optimization automatically.
@ -152,7 +152,7 @@ EIGEN_DONT_INLINE void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrd
Scalar t1 = cjAlpha * rhs[j];
Scalar t2(0);
// TODO make sure this product is a real * complex and that the rhs is properly conjugated if needed
res[j] += cjd.pmul(internal::real(A0[j]), t1);
res[j] += cjd.pmul(numext::real(A0[j]), t1);
for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)
{
res[i] += cj0.pmul(A0[i], t1);

12
Eigen/src/Core/products/SelfadjointRank2Update.h
View File

@ -30,8 +30,8 @@ struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
for (Index i=0; i<size; ++i)
{
Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) +=
(conj(alpha) * conj(u.coeff(i))) * v.tail(size-i)
+ (alpha * conj(v.coeff(i))) * u.tail(size-i);
(numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i)
+ (alpha * numext::conj(v.coeff(i))) * u.tail(size-i);
}
}
};
@ -44,8 +44,8 @@ struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper>
const Index size = u.size();
for (Index i=0; i<size; ++i)
Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) +=
(conj(alpha) * conj(u.coeff(i))) * v.head(i+1)
+ (alpha * conj(v.coeff(i))) * u.head(i+1);
(numext::conj(alpha) * numext::conj(u.coeff(i))) * v.head(i+1)
+ (alpha * numext::conj(v.coeff(i))) * u.head(i+1);
}
};
@ -75,9 +75,9 @@ SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived())
* internal::conj(VBlasTraits::extractScalarFactor(v.derived()));
* numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
if (IsRowMajor)
actualAlpha = internal::conj(actualAlpha);
actualAlpha = numext::conj(actualAlpha);
internal::selfadjoint_rank2_update_selector<Scalar, Index,
typename internal::remove_all<typename internal::conj_expr_if<IsRowMajor ^ UBlasTraits::NeedToConjugate,_ActualUType>::type>::type,

2
Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -245,7 +245,7 @@ template<> struct trmv_selector<ColMajor>
gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
bool alphaIsCompatible = (!ComplexByReal) || (imag(actualAlpha)==RealScalar(0));
bool alphaIsCompatible = (!ComplexByReal) || (numext::imag(actualAlpha)==RealScalar(0));
bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);

11
Eigen/src/Core/util/BlasUtil.h
View File

@ -42,7 +42,7 @@ template<bool Conjugate> struct conj_if;
template<> struct conj_if<true> {
template<typename T>
inline T operator()(const T& x) { return conj(x); }
inline T operator()(const T& x) { return numext::conj(x); }
template<typename T>
inline T pconj(const T& x) { return internal::pconj(x); }
};
@ -67,7 +67,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) + imag(x)*imag(y), imag(x)*real(y) - real(x)*imag(y)); }
{ return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::imag(x)*numext::real(y) - numext::real(x)*numext::imag(y)); }
};
template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,false>
@ -77,7 +77,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) + imag(x)*imag(y), real(x)*imag(y) - imag(x)*real(y)); }
{ return Scalar(numext::real(x)*numext::real(y) + numext::imag(x)*numext::imag(y), numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); }
};
template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::complex<RealScalar>, true,true>
@ -87,7 +87,7 @@ template<typename RealScalar> struct conj_helper<std::complex<RealScalar>, std::
{ return c + pmul(x,y); }
EIGEN_STRONG_INLINE Scalar pmul(const Scalar& x, const Scalar& y) const
{ return Scalar(real(x)*real(y) - imag(x)*imag(y), - real(x)*imag(y) - imag(x)*real(y)); }
{ return Scalar(numext::real(x)*numext::real(y) - numext::imag(x)*numext::imag(y), - numext::real(x)*numext::imag(y) - numext::imag(x)*numext::real(y)); }
};
template<typename RealScalar,bool Conj> struct conj_helper<std::complex<RealScalar>, RealScalar, Conj,false>
@ -113,7 +113,8 @@ template<typename From,typename To> struct get_factor {
};
template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return real(x); }
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); }
};
// Lightweight helper class to access matrix coefficients.

14
Eigen/src/Core/util/Macros.h
View File

@ -12,8 +12,8 @@
#define EIGEN_MACROS_H
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 1
#define EIGEN_MINOR_VERSION 91
#define EIGEN_MAJOR_VERSION 2
#define EIGEN_MINOR_VERSION 90
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
@ -240,10 +240,12 @@
// Suppresses 'unused variable' warnings.
#define EIGEN_UNUSED_VARIABLE(var) (void)var;
#if !defined(EIGEN_ASM_COMMENT) && (defined __GNUC__)
#define EIGEN_ASM_COMMENT(X) asm("#" X)
#else
#define EIGEN_ASM_COMMENT(X)
#if !defined(EIGEN_ASM_COMMENT)
#if (defined __GNUC__) && ( defined(__i386__) || defined(__x86_64__) )
#define EIGEN_ASM_COMMENT(X) asm("#" X)
#else
#define EIGEN_ASM_COMMENT(X)
#endif
#endif
/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.

17
Eigen/src/Core/util/Memory.h
View File

@ -58,10 +58,17 @@
#endif
#if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) \
&& (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0)
#define EIGEN_HAS_POSIX_MEMALIGN 1
#else
// See bug 554 (http://eigen.tuxfamily.org/bz/show_bug.cgi?id=554)
// It seems to be unsafe to check _POSIX_ADVISORY_INFO without including unistd.h first.
// Currently, let's include it only on unix systems:
#if defined(__unix__) || defined(__unix)
#include <unistd.h>
#if ((defined __QNXNTO__) || (defined _GNU_SOURCE) || ((defined _XOPEN_SOURCE) && (_XOPEN_SOURCE >= 600))) && (defined _POSIX_ADVISORY_INFO) && (_POSIX_ADVISORY_INFO > 0)
#define EIGEN_HAS_POSIX_MEMALIGN 1
#endif
#endif
#ifndef EIGEN_HAS_POSIX_MEMALIGN
#define EIGEN_HAS_POSIX_MEMALIGN 0
#endif
@ -215,7 +222,7 @@ inline void* aligned_malloc(size_t size)
if(posix_memalign(&result, 16, size)) result = 0;
#elif EIGEN_HAS_MM_MALLOC
result = _mm_malloc(size, 16);
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
result = _aligned_malloc(size, 16);
#else
result = handmade_aligned_malloc(size);

3
Eigen/src/Core/util/XprHelper.h
View File

@ -91,7 +91,8 @@ template<typename T> struct functor_traits
enum
{
Cost = 10,
PacketAccess = false
PacketAccess = false,
IsRepeatable = false
};
};

2
Eigen/src/Eigen2Support/Geometry/AlignedBox.h
View File

@ -34,7 +34,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
/** Default constructor initializing a null box. */
inline explicit AlignedBox()
inline AlignedBox()
{ if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
/** Constructs a null box with \a _dim the dimension of the ambient space. */

2
Eigen/src/Eigen2Support/Geometry/Hyperplane.h
View File

@ -44,7 +44,7 @@ public:
typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
/** Default constructor without initialization */
inline explicit Hyperplane() {}
inline Hyperplane() {}
/** Constructs a dynamic-size hyperplane with \a _dim the dimension
* of the ambient space */

2
Eigen/src/Eigen2Support/Geometry/ParametrizedLine.h
View File

@ -36,7 +36,7 @@ public:
typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
/** Default constructor without initialization */
inline explicit ParametrizedLine() {}
inline ParametrizedLine() {}
/** Constructs a dynamic-size line with \a _dim the dimension
* of the ambient space */

10
Eigen/src/Eigen2Support/MathFunctions.h
View File

@ -12,18 +12,18 @@
namespace Eigen {
template<typename T> inline typename NumTraits<T>::Real ei_real(const T& x) { return internal::real(x); }
template<typename T> inline typename NumTraits<T>::Real ei_imag(const T& x) { return internal::imag(x); }
template<typename T> inline T ei_conj(const T& x) { return internal::conj(x); }
template<typename T> inline typename NumTraits<T>::Real ei_real(const T& x) { return numext::real(x); }
template<typename T> inline typename NumTraits<T>::Real ei_imag(const T& x) { return numext::imag(x); }
template<typename T> inline T ei_conj(const T& x) { return numext::conj(x); }
template<typename T> inline typename NumTraits<T>::Real ei_abs (const T& x) { using std::abs; return abs(x); }
template<typename T> inline typename NumTraits<T>::Real ei_abs2(const T& x) { return internal::abs2(x); }
template<typename T> inline typename NumTraits<T>::Real ei_abs2(const T& x) { return numext::abs2(x); }
template<typename T> inline T ei_sqrt(const T& x) { using std::sqrt; return sqrt(x); }
template<typename T> inline T ei_exp (const T& x) { using std::exp; return exp(x); }
template<typename T> inline T ei_log (const T& x) { using std::log; return log(x); }
template<typename T> inline T ei_sin (const T& x) { using std::sin; return sin(x); }
template<typename T> inline T ei_cos (const T& x) { using std::cos; return cos(x); }
template<typename T> inline T ei_atan2(const T& x,const T& y) { using std::atan2; return atan2(x,y); }
template<typename T> inline T ei_pow (const T& x,const T& y) { return internal::pow(x,y); }
template<typename T> inline T ei_pow (const T& x,const T& y) { return numext::pow(x,y); }
template<typename T> inline T ei_random () { return internal::random<T>(); }
template<typename T> inline T ei_random (const T& x, const T& y) { return internal::random(x, y); }

8
Eigen/src/Eigen2Support/SVD.h
View File

@ -315,7 +315,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
e[p-2] = 0.0;
for (j = p-2; j >= k; --j)
{
Scalar t(internal::hypot(m_sigma[j],f));
Scalar t(numext::hypot(m_sigma[j],f));
Scalar cs(m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
@ -344,7 +344,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
e[k-1] = 0.0;
for (j = k; j < p; ++j)
{
Scalar t(internal::hypot(m_sigma[j],f));
Scalar t(numext::hypot(m_sigma[j],f));
Scalar cs( m_sigma[j]/t);
Scalar sn(f/t);
m_sigma[j] = t;
@ -392,7 +392,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
for (j = k; j < p-1; ++j)
{
Scalar t = internal::hypot(f,g);
Scalar t = numext::hypot(f,g);
Scalar cs = f/t;
Scalar sn = g/t;
if (j != k)
@ -410,7 +410,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
m_matV(i,j) = t;
}
}
t = internal::hypot(f,g);
t = numext::hypot(f,g);
cs = f/t;
sn = g/t;
m_sigma[j] = t;

2
Eigen/src/Eigenvalues/ComplexEigenSolver.h
View File

@ -294,7 +294,7 @@ void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(const RealScalar& mat
{
// If the i-th and k-th eigenvalue are equal, then z equals 0.
// Use a small value instead, to prevent division by zero.
internal::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
}
m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
}

10
Eigen/src/Eigenvalues/ComplexSchur.h
View File

@ -263,8 +263,8 @@ template<typename _MatrixType> class ComplexSchur
template<typename MatrixType>
inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i)
{
RealScalar d = internal::norm1(m_matT.coeff(i,i)) + internal::norm1(m_matT.coeff(i+1,i+1));
RealScalar sd = internal::norm1(m_matT.coeff(i+1,i));
RealScalar d = numext::norm1(m_matT.coeff(i,i)) + numext::norm1(m_matT.coeff(i+1,i+1));
RealScalar sd = numext::norm1(m_matT.coeff(i+1,i));
if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon()))
{
m_matT.coeffRef(i+1,i) = ComplexScalar(0);
@ -282,7 +282,7 @@ typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::compu
if (iter == 10 || iter == 20)
{
// exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
return abs(internal::real(m_matT.coeff(iu,iu-1))) + abs(internal::real(m_matT.coeff(iu-1,iu-2)));
return abs(numext::real(m_matT.coeff(iu,iu-1))) + abs(numext::real(m_matT.coeff(iu-1,iu-2)));
}
// compute the shift as one of the eigenvalues of t, the 2x2
@ -299,13 +299,13 @@ typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::compu
ComplexScalar eival1 = (trace + disc) / RealScalar(2);
ComplexScalar eival2 = (trace - disc) / RealScalar(2);
if(internal::norm1(eival1) > internal::norm1(eival2))
if(numext::norm1(eival1) > numext::norm1(eival2))
eival2 = det / eival1;
else
eival1 = det / eival2;
// choose the eigenvalue closest to the bottom entry of the diagonal
if(internal::norm1(eival1-t.coeff(1,1)) < internal::norm1(eival2-t.coeff(1,1)))
if(numext::norm1(eival1-t.coeff(1,1)) < numext::norm1(eival2-t.coeff(1,1)))
return normt * eival1;
else
return normt * eival2;

26
Eigen/src/Eigenvalues/EigenSolver.h
View File

@ -317,12 +317,12 @@ MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
MatrixType matD = MatrixType::Zero(n,n);
for (Index i=0; i<n; ++i)
{
if (internal::isMuchSmallerThan(internal::imag(m_eivalues.coeff(i)), internal::real(m_eivalues.coeff(i))))
matD.coeffRef(i,i) = internal::real(m_eivalues.coeff(i));
if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i))))
matD.coeffRef(i,i) = numext::real(m_eivalues.coeff(i));
else
{
matD.template block<2,2>(i,i) << internal::real(m_eivalues.coeff(i)), internal::imag(m_eivalues.coeff(i)),
-internal::imag(m_eivalues.coeff(i)), internal::real(m_eivalues.coeff(i));
matD.template block<2,2>(i,i) << numext::real(m_eivalues.coeff(i)), numext::imag(m_eivalues.coeff(i)),
-numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i));
++i;
}
}
@ -338,7 +338,7 @@ typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eige
EigenvectorsType matV(n,n);
for (Index j=0; j<n; ++j)
{
if (internal::isMuchSmallerThan(internal::imag(m_eivalues.coeff(j)), internal::real(m_eivalues.coeff(j))) || j+1==n)
if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j))) || j+1==n)
{
// we have a real eigen value
matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
@ -515,8 +515,8 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
else
{
std::complex<Scalar> cc = cdiv<Scalar>(0.0,-m_matT.coeff(n-1,n),m_matT.coeff(n-1,n-1)-p,q);
m_matT.coeffRef(n-1,n-1) = internal::real(cc);
m_matT.coeffRef(n-1,n) = internal::imag(cc);
m_matT.coeffRef(n-1,n-1) = numext::real(cc);
m_matT.coeffRef(n-1,n) = numext::imag(cc);
}
m_matT.coeffRef(n,n-1) = 0.0;
m_matT.coeffRef(n,n) = 1.0;
@ -538,8 +538,8 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
if (m_eivalues.coeff(i).imag() == RealScalar(0))
{
std::complex<Scalar> cc = cdiv(-ra,-sa,w,q);
m_matT.coeffRef(i,n-1) = internal::real(cc);
m_matT.coeffRef(i,n) = internal::imag(cc);
m_matT.coeffRef(i,n-1) = numext::real(cc);
m_matT.coeffRef(i,n) = numext::imag(cc);
}
else
{
@ -552,8 +552,8 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw));
std::complex<Scalar> cc = cdiv(x*lastra-lastw*ra+q*sa,x*lastsa-lastw*sa-q*ra,vr,vi);
m_matT.coeffRef(i,n-1) = internal::real(cc);
m_matT.coeffRef(i,n) = internal::imag(cc);
m_matT.coeffRef(i,n-1) = numext::real(cc);
m_matT.coeffRef(i,n) = numext::imag(cc);
if (abs(x) > (abs(lastw) + abs(q)))
{
m_matT.coeffRef(i+1,n-1) = (-ra - w * m_matT.coeff(i,n-1) + q * m_matT.coeff(i,n)) / x;
@ -562,8 +562,8 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
else
{
cc = cdiv(-lastra-y*m_matT.coeff(i,n-1),-lastsa-y*m_matT.coeff(i,n),lastw,q);
m_matT.coeffRef(i+1,n-1) = internal::real(cc);
m_matT.coeffRef(i+1,n) = internal::imag(cc);
m_matT.coeffRef(i+1,n-1) = numext::real(cc);
m_matT.coeffRef(i+1,n) = numext::imag(cc);
}
}

4
Eigen/src/Eigenvalues/HessenbergDecomposition.h
View File

@ -82,7 +82,7 @@ template<typename _MatrixType> class HessenbergDecomposition
typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
/** \brief Return type of matrixQ() */
typedef typename HouseholderSequence<MatrixType,CoeffVectorType>::ConjugateReturnType HouseholderSequenceType;
typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType;
@ -313,7 +313,7 @@ void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVector
// A = A H'
matA.rightCols(remainingSize)
.applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1).conjugate(), internal::conj(h), &temp.coeffRef(0));
.applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1).conjugate(), numext::conj(h), &temp.coeffRef(0));
}
}

16
Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
View File

@ -395,7 +395,7 @@ SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
if(n==1)
{
m_eivalues.coeffRef(0,0) = internal::real(matrix.coeff(0,0));
m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0));
if(computeEigenvectors)
m_eivec.setOnes(n,n);
m_info = Success;
@ -669,7 +669,7 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,2
static inline void computeRoots(const MatrixType& m, VectorType& roots)
{
using std::sqrt;
const Scalar t0 = Scalar(0.5) * sqrt( abs2(m(0,0)-m(1,1)) + Scalar(4)*m(1,0)*m(1,0));
const Scalar t0 = Scalar(0.5) * sqrt( numext::abs2(m(0,0)-m(1,1)) + Scalar(4)*m(1,0)*m(1,0));
const Scalar t1 = Scalar(0.5) * (m(0,0) + m(1,1));
roots(0) = t1 - t0;
roots(1) = t1 + t0;
@ -699,9 +699,9 @@ template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,2
if(computeEigenvectors)
{
scaledMat.diagonal().array () -= eivals(1);
Scalar a2 = abs2(scaledMat(0,0));
Scalar c2 = abs2(scaledMat(1,1));
Scalar b2 = abs2(scaledMat(1,0));
Scalar a2 = numext::abs2(scaledMat(0,0));
Scalar c2 = numext::abs2(scaledMat(1,1));
Scalar b2 = numext::abs2(scaledMat(1,0));
if(a2>c2)
{
eivecs.col(1) << -scaledMat(1,0), scaledMat(0,0);
@ -744,7 +744,7 @@ static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index sta
RealScalar e = subdiag[end-1];
// Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
// underflow thus leading to inf/NaN values when using the following commented code:
// RealScalar e2 = abs2(subdiag[end-1]);
// RealScalar e2 = numext::abs2(subdiag[end-1]);
// RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
// This explain the following, somewhat more complicated, version:
RealScalar mu = diag[end];
@ -752,8 +752,8 @@ static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index sta
mu -= abs(e);
else
{
RealScalar e2 = abs2(subdiag[end-1]);
RealScalar h = hypot(td,e);
RealScalar e2 = numext::abs2(subdiag[end-1]);
RealScalar h = numext::hypot(td,e);
if(e2==0) mu -= (e / (td + (td>0 ? 1 : -1))) * (e / h);
else mu -= e2 / (td + (td>0 ? h : -h));
}

2
Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h
View File

@ -56,7 +56,7 @@ SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(c
\
if(n==1) \
{ \
m_eivalues.coeffRef(0,0) = internal::real(matrix.coeff(0,0)); \
m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0)); \
if(computeEigenvectors) m_eivec.setOnes(n,n); \
m_info = Success; \
m_isInitialized = true; \

10
Eigen/src/Eigenvalues/Tridiagonalization.h
View File

@ -96,7 +96,7 @@ template<typename _MatrixType> class Tridiagonalization
>::type SubDiagonalReturnType;
/** \brief Return type of matrixQ() */
typedef typename HouseholderSequence<MatrixType,CoeffVectorType>::ConjugateReturnType HouseholderSequenceType;
typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename CoeffVectorType::ConjugateReturnType>::type> HouseholderSequenceType;
/** \brief Default constructor.
*
@ -345,7 +345,7 @@ namespace internal {
template<typename MatrixType, typename CoeffVectorType>
void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
{
using internal::conj;
using numext::conj;
typedef typename MatrixType::Index Index;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
@ -468,7 +468,7 @@ struct tridiagonalization_inplace_selector<MatrixType,3,false>
{
using std::sqrt;
diag[0] = mat(0,0);
RealScalar v1norm2 = abs2(mat(2,0));
RealScalar v1norm2 = numext::abs2(mat(2,0));
if(v1norm2 == RealScalar(0))
{
diag[1] = mat(1,1);
@ -480,7 +480,7 @@ struct tridiagonalization_inplace_selector<MatrixType,3,false>
}
else
{
RealScalar beta = sqrt(abs2(mat(1,0)) + v1norm2);
RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2);
RealScalar invBeta = RealScalar(1)/beta;
Scalar m01 = mat(1,0) * invBeta;
Scalar m02 = mat(2,0) * invBeta;
@ -510,7 +510,7 @@ struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex>
template<typename DiagonalType, typename SubDiagonalType>
static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, bool extractQ)
{
diag(0,0) = real(mat(0,0));
diag(0,0) = numext::real(mat(0,0));
if(extractQ)
mat(0,0) = Scalar(1);
}

2
Eigen/src/Geometry/AlignedBox.h
View File

@ -56,7 +56,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
/** Default constructor initializing a null box. */
inline explicit AlignedBox()
inline AlignedBox()
{ if (AmbientDimAtCompileTime!=Dynamic) setEmpty(); }
/** Constructs a null box with \a _dim the dimension of the ambient space. */

59
Eigen/src/Geometry/EulerAngles.h
View File

@ -27,56 +27,75 @@ namespace Eigen {
* * AngleAxisf(ea[1], Vector3f::UnitX())
* * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
* This corresponds to the right-multiply conventions (with right hand side frames).
*
* The returned angles are in the ranges [0:pi]x[0:pi]x[-pi:pi].
*
* \sa class AngleAxis
*/
template<typename Derived>
inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
MatrixBase<Derived>::eulerAngles(Index a0, Index a1, Index a2) const
{
using std::atan2;
using std::sin;
using std::cos;
/* Implemented from Graphics Gems IV */
EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived,3,3)
Matrix<Scalar,3,1> res;
typedef Matrix<typename Derived::Scalar,2,1> Vector2;
const Scalar epsilon = NumTraits<Scalar>::dummy_precision();
const Index odd = ((a0+1)%3 == a1) ? 0 : 1;
const Index i = a0;
const Index j = (a0 + 1 + odd)%3;
const Index k = (a0 + 2 - odd)%3;
if (a0==a2)
{
Scalar s = Vector2(coeff(j,i) , coeff(k,i)).norm();
res[1] = atan2(s, coeff(i,i));
if (s > epsilon)
res[0] = atan2(coeff(j,i), coeff(k,i));
if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0)))
{
res[0] = atan2(coeff(j,i), coeff(k,i));
res[2] = atan2(coeff(i,j),-coeff(i,k));
res[0] = (res[0] > Scalar(0)) ? res[0] - Scalar(M_PI) : res[0] + Scalar(M_PI);
Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
res[1] = -atan2(s2, coeff(i,i));
}
else
{
res[0] = Scalar(0);
res[2] = (coeff(i,i)>0?1:-1)*atan2(-coeff(k,j), coeff(j,j));
Scalar s2 = Vector2(coeff(j,i), coeff(k,i)).norm();
res[1] = atan2(s2, coeff(i,i));
}
}
// With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
// we can compute their respective rotation, and apply its inverse to M. Since the result must
// be a rotation around x, we have:
//
// c2 s1.s2 c1.s2 1 0 0
// 0 c1 -s1 * M = 0 c3 s3
// -s2 s1.c2 c1.c2 0 -s3 c3
//
// Thus: m11.c1 - m21.s1 = c3 & m12.c1 - m22.s1 = s3
Scalar s1 = sin(res[0]);
Scalar c1 = cos(res[0]);
res[2] = atan2(c1*coeff(j,k)-s1*coeff(k,k), c1*coeff(j,j) - s1 * coeff(k,j));
}
else
{
Scalar c = Vector2(coeff(i,i) , coeff(i,j)).norm();
res[1] = atan2(-coeff(i,k), c);
if (c > epsilon)
{
res[0] = atan2(coeff(j,k), coeff(k,k));
res[2] = atan2(coeff(i,j), coeff(i,i));
res[0] = atan2(coeff(j,k), coeff(k,k));
Scalar c2 = Vector2(coeff(i,i), coeff(i,j)).norm();
if((odd && res[0]<Scalar(0)) || ((!odd) && res[0]>Scalar(0))) {
res[0] = (res[0] > Scalar(0)) ? res[0] - Scalar(M_PI) : res[0] + Scalar(M_PI);
res[1] = atan2(-coeff(i,k), -c2);
}
else
{
res[0] = Scalar(0);
res[2] = (coeff(i,k)>0?1:-1)*atan2(-coeff(k,j), coeff(j,j));
}
res[1] = atan2(-coeff(i,k), c2);
Scalar s1 = sin(res[0]);
Scalar c1 = cos(res[0]);
res[2] = atan2(s1*coeff(k,i)-c1*coeff(j,i), c1*coeff(j,j) - s1 * coeff(k,j));
}
if (!odd)
res = -res;
return res;
}

2
Eigen/src/Geometry/Homogeneous.h
View File

@ -59,7 +59,7 @@ template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl
} // end namespace internal
template<typename MatrixType,int _Direction> class Homogeneous
: public MatrixBase<Homogeneous<MatrixType,_Direction> >
: internal::no_assignment_operator, public MatrixBase<Homogeneous<MatrixType,_Direction> >
{
public:

2
Eigen/src/Geometry/Hyperplane.h
View File

@ -50,7 +50,7 @@ public:
typedef const Block<const Coefficients,AmbientDimAtCompileTime,1> ConstNormalReturnType;
/** Default constructor without initialization */
inline explicit Hyperplane() {}
inline Hyperplane() {}
template<int OtherOptions>
Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)

26
Eigen/src/Geometry/OrthoMethods.h
View File

@ -33,9 +33,9 @@ MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
typename internal::nested<Derived,2>::type lhs(derived());
typename internal::nested<OtherDerived,2>::type rhs(other.derived());
return typename cross_product_return_type<OtherDerived>::type(
internal::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
internal::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
internal::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
);
}
@ -49,9 +49,9 @@ struct cross3_impl {
run(const VectorLhs& lhs, const VectorRhs& rhs)
{
return typename internal::plain_matrix_type<VectorLhs>::type(
internal::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
internal::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
internal::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
0
);
}
@ -141,8 +141,8 @@ struct unitOrthogonal_selector
if (maxi==0)
sndi = 1;
RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm();
perp.coeffRef(maxi) = -conj(src.coeff(sndi)) * invnm;
perp.coeffRef(sndi) = conj(src.coeff(maxi)) * invnm;
perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
perp.coeffRef(sndi) = numext::conj(src.coeff(maxi)) * invnm;
return perp;
}
@ -168,8 +168,8 @@ struct unitOrthogonal_selector<Derived,3>
|| (!isMuchSmallerThan(src.y(), src.z())))
{
RealScalar invnm = RealScalar(1)/src.template head<2>().norm();
perp.coeffRef(0) = -conj(src.y())*invnm;
perp.coeffRef(1) = conj(src.x())*invnm;
perp.coeffRef(0) = -numext::conj(src.y())*invnm;
perp.coeffRef(1) = numext::conj(src.x())*invnm;
perp.coeffRef(2) = 0;
}
/* if both x and y are close to zero, then the vector is close
@ -180,8 +180,8 @@ struct unitOrthogonal_selector<Derived,3>
{
RealScalar invnm = RealScalar(1)/src.template tail<2>().norm();
perp.coeffRef(0) = 0;
perp.coeffRef(1) = -conj(src.z())*invnm;
perp.coeffRef(2) = conj(src.y())*invnm;
perp.coeffRef(1) = -numext::conj(src.z())*invnm;
perp.coeffRef(2) = numext::conj(src.y())*invnm;
}
return perp;
@ -193,7 +193,7 @@ struct unitOrthogonal_selector<Derived,2>
{
typedef typename plain_matrix_type<Derived>::type VectorType;
static inline VectorType run(const Derived& src)
{ return VectorType(-conj(src.y()), conj(src.x())).normalized(); }
{ return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); }
};
} // end namespace internal

2
Eigen/src/Geometry/ParametrizedLine.h
View File

@ -41,7 +41,7 @@ public:
typedef Matrix<Scalar,AmbientDimAtCompileTime,1,Options> VectorType;
/** Default constructor without initialization */
inline explicit ParametrizedLine() {}
inline ParametrizedLine() {}
template<int OtherOptions>
ParametrizedLine(const ParametrizedLine<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)

10
Eigen/src/Householder/Householder.h
View File

@ -68,7 +68,7 @@ void MatrixBase<Derived>::makeHouseholder(
RealScalar& beta) const
{
using std::sqrt;
using internal::conj;
using numext::conj;
EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size()-1);
@ -76,16 +76,16 @@ void MatrixBase<Derived>::makeHouseholder(
RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
Scalar c0 = coeff(0);
if(tailSqNorm == RealScalar(0) && internal::imag(c0)==RealScalar(0))
if(tailSqNorm == RealScalar(0) && numext::imag(c0)==RealScalar(0))
{
tau = RealScalar(0);
beta = internal::real(c0);
beta = numext::real(c0);
essential.setZero();
}
else
{
beta = sqrt(internal::abs2(c0) + tailSqNorm);
if (internal::real(c0)>=RealScalar(0))
beta = sqrt(numext::abs2(c0) + tailSqNorm);
if (numext::real(c0)>=RealScalar(0))
beta = -beta;
essential = tail / (c0 - beta);
tau = conj((beta - c0) / beta);

14
Eigen/src/Householder/HouseholderSequence.h
View File

@ -112,6 +112,9 @@ template<typename OtherScalarType, typename MatrixType> struct matrix_type_times
template<typename VectorsType, typename CoeffsType, int Side> class HouseholderSequence
: public EigenBase<HouseholderSequence<VectorsType,CoeffsType,Side> >
{
typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType EssentialVectorType;
public:
enum {
RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime,
ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime,
@ -121,13 +124,10 @@ template<typename VectorsType, typename CoeffsType, int Side> class HouseholderS
typedef typename internal::traits<HouseholderSequence>::Scalar Scalar;
typedef typename VectorsType::Index Index;
typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType
EssentialVectorType;
public:
typedef HouseholderSequence<
VectorsType,
typename internal::conditional<NumTraits<Scalar>::IsComplex,
typename internal::remove_all<typename VectorsType::ConjugateReturnType>::type,
VectorsType>::type,
typename internal::conditional<NumTraits<Scalar>::IsComplex,
typename internal::remove_all<typename CoeffsType::ConjugateReturnType>::type,
CoeffsType>::type,
@ -208,7 +208,7 @@ template<typename VectorsType, typename CoeffsType, int Side> class HouseholderS
/** \brief Complex conjugate of the Householder sequence. */
ConjugateReturnType conjugate() const
{
return ConjugateReturnType(m_vectors, m_coeffs.conjugate())
return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate())
.setTrans(m_trans)
.setLength(m_length)
.setShift(m_shift);

26
Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
View File

@ -43,8 +43,9 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
VectorType r = rhs - mat * x;
VectorType r0 = r;
RealScalar r0_sqnorm = rhs.squaredNorm();
if(r0_sqnorm == 0)
RealScalar r0_sqnorm = r0.squaredNorm();
RealScalar rhs_sqnorm = rhs.squaredNorm();
if(rhs_sqnorm == 0)
{
x.setZero();
return true;
@ -61,13 +62,22 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
RealScalar tol2 = tol*tol;
int i = 0;
int restarts = 0;
while ( r.squaredNorm()/r0_sqnorm > tol2 && i<maxIters )
while ( r.squaredNorm()/rhs_sqnorm > tol2 && i<maxIters )
{
Scalar rho_old = rho;
rho = r0.dot(r);
if (rho == Scalar(0)) return false; /* New search directions cannot be found */
if (internal::isMuchSmallerThan(rho,r0_sqnorm))
{
// The new residual vector became too orthogonal to the arbitrarily choosen direction r0
// Let's restart with a new r0:
r0 = r;
rho = r0_sqnorm = r.squaredNorm();
if(restarts++ == 0)
i = 0;
}
Scalar beta = (rho/rho_old) * (alpha / w);
p = r + beta * (p - w * v);
@ -81,12 +91,16 @@ bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
z = precond.solve(s);
t.noalias() = mat * z;
w = t.dot(s) / t.squaredNorm();
RealScalar tmp = t.squaredNorm();
if(tmp>RealScalar(0))
w = t.dot(s) / tmp;
else
w = Scalar(0);
x += alpha * y + w * z;
r = s - w * t;
++i;
}
tol_error = sqrt(r.squaredNorm()/r0_sqnorm);
tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
iters = i;
return true;
}

4
Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
View File

@ -63,7 +63,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
p = precond.solve(residual); //initial search direction
VectorType z(n), tmp(n);
RealScalar absNew = internal::real(residual.dot(p)); // the square of the absolute value of r scaled by invM
RealScalar absNew = numext::real(residual.dot(p)); // the square of the absolute value of r scaled by invM
int i = 0;
while(i < maxIters)
{
@ -80,7 +80,7 @@ void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
z = precond.solve(residual); // approximately solve for "A z = residual"
RealScalar absOld = absNew;
absNew = internal::real(residual.dot(z)); // update the absolute value of r
absNew = numext::real(residual.dot(z)); // update the absolute value of r
RealScalar beta = absNew / absOld; // calculate the Gram-Schmidt value used to create the new search direction
p = z + beta * p; // update search direction
i++;

5
Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
View File

@ -150,8 +150,7 @@ class IncompleteLUT : internal::noncopyable
{
analyzePattern(amat);
factorize(amat);
eigen_assert(m_factorizationIsOk == true);
m_isInitialized = true;
m_isInitialized = m_factorizationIsOk;
return *this;
}
@ -310,7 +309,7 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
jr(k) = jpos;
++sizeu;
}
rownorm += internal::abs2(j_it.value());
rownorm += numext::abs2(j_it.value());
}
// 2 - detect possible zero row

50
Eigen/src/Jacobi/Jacobi.h
View File

@ -50,16 +50,16 @@ template<typename Scalar> class JacobiRotation
/** Concatenates two planar rotation */
JacobiRotation operator*(const JacobiRotation& other)
{
using internal::conj;
using numext::conj;
return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s,
conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c)));
}
/** Returns the transposed transformation */
JacobiRotation transpose() const { using internal::conj; return JacobiRotation(m_c, -conj(m_s)); }
JacobiRotation transpose() const { using numext::conj; return JacobiRotation(m_c, -conj(m_s)); }
/** Returns the adjoint transformation */
JacobiRotation adjoint() const { using internal::conj; return JacobiRotation(conj(m_c), -m_s); }
JacobiRotation adjoint() const { using numext::conj; return JacobiRotation(conj(m_c), -m_s); }
template<typename Derived>
bool makeJacobi(const MatrixBase<Derived>&, typename Derived::Index p, typename Derived::Index q);
@ -94,7 +94,7 @@ bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, co
else
{
RealScalar tau = (x-z)/(RealScalar(2)*abs(y));
RealScalar w = sqrt(internal::abs2(tau) + RealScalar(1));
RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1));
RealScalar t;
if(tau>RealScalar(0))
{
@ -105,8 +105,8 @@ bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, co
t = RealScalar(1) / (tau - w);
}
RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
RealScalar n = RealScalar(1) / sqrt(internal::abs2(t)+RealScalar(1));
m_s = - sign_t * (internal::conj(y) / abs(y)) * abs(t) * n;
RealScalar n = RealScalar(1) / sqrt(numext::abs2(t)+RealScalar(1));
m_s = - sign_t * (numext::conj(y) / abs(y)) * abs(t) * n;
m_c = n;
return true;
}
@ -125,7 +125,7 @@ template<typename Scalar>
template<typename Derived>
inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, typename Derived::Index p, typename Derived::Index q)
{
return makeJacobi(internal::real(m.coeff(p,p)), m.coeff(p,q), internal::real(m.coeff(q,q)));
return makeJacobi(numext::real(m.coeff(p,p)), m.coeff(p,q), numext::real(m.coeff(q,q)));
}
/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector
@ -157,11 +157,11 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
{
using std::sqrt;
using std::abs;
using internal::conj;
using numext::conj;
if(q==Scalar(0))
{
m_c = internal::real(p)<0 ? Scalar(-1) : Scalar(1);
m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1);
m_s = 0;
if(r) *r = m_c * p;
}
@ -173,17 +173,17 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
}
else
{
RealScalar p1 = internal::norm1(p);
RealScalar q1 = internal::norm1(q);
RealScalar p1 = numext::norm1(p);
RealScalar q1 = numext::norm1(q);
if(p1>=q1)
{
Scalar ps = p / p1;
RealScalar p2 = internal::abs2(ps);
RealScalar p2 = numext::abs2(ps);
Scalar qs = q / p1;
RealScalar q2 = internal::abs2(qs);
RealScalar q2 = numext::abs2(qs);
RealScalar u = sqrt(RealScalar(1) + q2/p2);
if(internal::real(p)<RealScalar(0))
if(numext::real(p)<RealScalar(0))
u = -u;
m_c = Scalar(1)/u;
@ -193,12 +193,12 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
else
{
Scalar ps = p / q1;
RealScalar p2 = internal::abs2(ps);
RealScalar p2 = numext::abs2(ps);
Scalar qs = q / q1;
RealScalar q2 = internal::abs2(qs);
RealScalar q2 = numext::abs2(qs);
RealScalar u = q1 * sqrt(p2 + q2);
if(internal::real(p)<RealScalar(0))
if(numext::real(p)<RealScalar(0))
u = -u;
p1 = abs(p);
@ -231,7 +231,7 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
else if(abs(p) > abs(q))
{
Scalar t = q/p;
Scalar u = sqrt(Scalar(1) + internal::abs2(t));
Scalar u = sqrt(Scalar(1) + numext::abs2(t));
if(p<Scalar(0))
u = -u;
m_c = Scalar(1)/u;
@ -241,7 +241,7 @@ void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar
else
{
Scalar t = p/q;
Scalar u = sqrt(Scalar(1) + internal::abs2(t));
Scalar u = sqrt(Scalar(1) + numext::abs2(t));
if(q<Scalar(0))
u = -u;
m_s = -Scalar(1)/u;
@ -337,8 +337,8 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(VectorX& _x, VectorY& _y,
{
Scalar xi = x[i];
Scalar yi = y[i];
x[i] = c * xi + conj(s) * yi;
y[i] = -s * xi + conj(c) * yi;
x[i] = c * xi + numext::conj(s) * yi;
y[i] = -s * xi + numext::conj(c) * yi;
}
Scalar* EIGEN_RESTRICT px = x + alignedStart;
@ -385,8 +385,8 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(VectorX& _x, VectorY& _y,
{
Scalar xi = x[i];
Scalar yi = y[i];
x[i] = c * xi + conj(s) * yi;
y[i] = -s * xi + conj(c) * yi;
x[i] = c * xi + numext::conj(s) * yi;
y[i] = -s * xi + numext::conj(c) * yi;
}
}
@ -418,8 +418,8 @@ void /*EIGEN_DONT_INLINE*/ apply_rotation_in_the_plane(VectorX& _x, VectorY& _y,
{
Scalar xi = *x;
Scalar yi = *y;
*x = c * xi + conj(s) * yi;
*y = -s * xi + conj(c) * yi;
*x = c * xi + numext::conj(s) * yi;
*y = -s * xi + numext::conj(c) * yi;
x += incrx;
y += incry;
}

2
Eigen/src/LU/Inverse.h
View File

@ -348,7 +348,7 @@ inline const internal::inverse_impl<Derived> MatrixBase<Derived>::inverse() cons
* This is only for fixed-size square matrices of size up to 4x4.
*
* \param inverse Reference to the matrix in which to store the inverse.
* \param determinant Reference to the variable in which to store the inverse.
* \param determinant Reference to the variable in which to store the determinant.
* \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
* \param absDeterminantThreshold Optional parameter controlling the invertibility check.
* The matrix will be declared invertible if the absolute value of its

2
Eigen/src/MetisSupport/MetisSupport.h
View File

@ -134,4 +134,4 @@ public:
};
}// end namespace eigen
#endif
#endif

32
Eigen/src/QR/ColPivHouseholderQR.h
View File

@ -55,7 +55,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
typedef typename internal::plain_row_type<MatrixType, Index>::type IntRowVectorType;
typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
typedef typename HouseholderSequence<MatrixType,HCoeffsType>::ConjugateReturnType HouseholderSequenceType;
typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
private:
@ -94,6 +94,18 @@ template<typename _MatrixType> class ColPivHouseholderQR
m_isInitialized(false),
m_usePrescribedThreshold(false) {}
/** \brief Constructs a QR factorization from a given matrix
*
* This constructor computes the QR factorization of the matrix \a matrix by calling
* the method compute(). It is a short cut for:
*
* \code
* ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
* qr.compute(matrix);
* \endcode
*
* \sa compute()
*/
ColPivHouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
@ -163,6 +175,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
ColPivHouseholderQR& compute(const MatrixType& matrix);
/** \returns a const reference to the column permutation matrix */
const PermutationType& colsPermutation() const
{
eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
@ -281,6 +294,11 @@ template<typename _MatrixType> class ColPivHouseholderQR
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
/** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
*
* For advanced uses only.
*/
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
/** Allows to prescribe a threshold to be used by certain methods, such as rank(),
@ -394,6 +412,12 @@ typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDetermina
return m_qr.diagonal().cwiseAbs().array().log().sum();
}
/** Performs the QR factorization of the given matrix \a matrix. The result of
* the factorization is stored into \c *this, and a reference to \c *this
* is returned.
*
* \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
@ -417,7 +441,7 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
for(Index k = 0; k < cols; ++k)
m_colSqNorms.coeffRef(k) = m_qr.col(k).squaredNorm();
RealScalar threshold_helper = m_colSqNorms.maxCoeff() * internal::abs2(NumTraits<Scalar>::epsilon()) / RealScalar(rows);
RealScalar threshold_helper = m_colSqNorms.maxCoeff() * numext::abs2(NumTraits<Scalar>::epsilon()) / RealScalar(rows);
m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
m_maxpivot = RealScalar(0);
@ -501,8 +525,8 @@ struct solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs>
{
eigen_assert(rhs().rows() == dec().rows());
const int cols = dec().cols(),
nonzero_pivots = dec().nonzeroPivots();
const Index cols = dec().cols(),
nonzero_pivots = dec().nonzeroPivots();
if(nonzero_pivots == 0)
{

27
Eigen/src/QR/FullPivHouseholderQR.h
View File

@ -100,6 +100,18 @@ template<typename _MatrixType> class FullPivHouseholderQR
m_isInitialized(false),
m_usePrescribedThreshold(false) {}
/** \brief Constructs a QR factorization from a given matrix
*
* This constructor computes the QR factorization of the matrix \a matrix by calling
* the method compute(). It is a short cut for:
*
* \code
* FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
* qr.compute(matrix);
* \endcode
*
* \sa compute()
*/
FullPivHouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
@ -152,12 +164,14 @@ template<typename _MatrixType> class FullPivHouseholderQR
FullPivHouseholderQR& compute(const MatrixType& matrix);
/** \returns a const reference to the column permutation matrix */
const PermutationType& colsPermutation() const
{
eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
return m_cols_permutation;
}
/** \returns a const reference to the vector of indices representing the rows transpositions */
const IntColVectorType& rowsTranspositions() const
{
eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
@ -275,6 +289,11 @@ template<typename _MatrixType> class FullPivHouseholderQR
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
/** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
*
* For advanced uses only.
*/
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
/** Allows to prescribe a threshold to be used by certain methods, such as rank(),
@ -377,6 +396,12 @@ typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDetermin
return m_qr.diagonal().cwiseAbs().array().log().sum();
}
/** Performs the QR factorization of the given matrix \a matrix. The result of
* the factorization is stored into \c *this, and a reference to \c *this
* is returned.
*
* \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
@ -547,7 +572,7 @@ public:
template <typename ResultType>
void evalTo(ResultType& result, WorkVectorType& workspace) const
{
using internal::conj;
using numext::conj;
// compute the product H'_0 H'_1 ... H'_n-1,
// where H_k is the k-th Householder transformation I - h_k v_k v_k'
// and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]

35
Eigen/src/QR/HouseholderQR.h
View File

@ -57,14 +57,14 @@ template<typename _MatrixType> class HouseholderQR
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
typedef typename HouseholderSequence<MatrixType,HCoeffsType>::ConjugateReturnType HouseholderSequenceType;
typedef HouseholderSequence<MatrixType,typename internal::remove_all<typename HCoeffsType::ConjugateReturnType>::type> HouseholderSequenceType;
/**
* \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via HouseholderQR::compute(const MatrixType&).
*/
* \brief Default Constructor.
*
* The default constructor is useful in cases in which the user intends to
* perform decompositions via HouseholderQR::compute(const MatrixType&).
*/
HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {}
/** \brief Default Constructor with memory preallocation
@ -79,6 +79,18 @@ template<typename _MatrixType> class HouseholderQR
m_temp(cols),
m_isInitialized(false) {}
/** \brief Constructs a QR factorization from a given matrix
*
* This constructor computes the QR factorization of the matrix \a matrix by calling
* the method compute(). It is a short cut for:
*
* \code
* HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
* qr.compute(matrix);
* \endcode
*
* \sa compute()
*/
HouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
@ -169,6 +181,11 @@ template<typename _MatrixType> class HouseholderQR
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
/** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
*
* For advanced uses only.
*/
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
protected:
@ -317,6 +334,12 @@ struct solve_retval<HouseholderQR<_MatrixType>, Rhs>
} // end namespace internal
/** Performs the QR factorization of the given matrix \a matrix. The result of
* the factorization is stored into \c *this, and a reference to \c *this
* is returned.
*
* \sa class HouseholderQR, HouseholderQR(const MatrixType&)
*/
template<typename MatrixType>
HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{

14
Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
View File

@ -83,10 +83,12 @@ class SPQR
~SPQR()
{
// Calls SuiteSparseQR_free()
cholmod_free_sparse(&m_H, &m_cc);
cholmod_free_dense(&m_HTau, &m_cc);
delete[] m_E;
delete[] m_HPinv;
cholmod_l_free_sparse(&m_H, &m_cc);
cholmod_l_free_sparse(&m_cR, &m_cc);
cholmod_l_free_dense(&m_HTau, &m_cc);
std::free(m_E);
std::free(m_HPinv);
cholmod_l_finish(&m_cc);
}
void compute(const _MatrixType& matrix)
{
@ -244,7 +246,7 @@ struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> >
y_cd = viewAsCholmod(m_other.const_cast_derived());
x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc);
res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
cholmod_free_dense(&x_cd, cc);
cholmod_l_free_dense(&x_cd, cc);
}
const SPQRType& m_spqr;
const Derived& m_other;
@ -301,4 +303,4 @@ struct solve_retval<SPQR<_MatrixType>, Rhs>
} // end namespace internal
}// End namespace Eigen
#endif
#endif

23
Eigen/src/SVD/JacobiSVD.h
View File

@ -374,7 +374,7 @@ struct svd_precondition_2x2_block_to_be_real<MatrixType, QRPreconditioner, true>
using std::sqrt;
Scalar z;
JacobiRotation<Scalar> rot;
RealScalar n = sqrt(abs2(work_matrix.coeff(p,p)) + abs2(work_matrix.coeff(q,p)));
RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
if(n==0)
{
z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
@ -413,8 +413,8 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
{
using std::sqrt;
Matrix<RealScalar,2,2> m;
m << real(matrix.coeff(p,p)), real(matrix.coeff(p,q)),
real(matrix.coeff(q,p)), real(matrix.coeff(q,q));
m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
JacobiRotation<RealScalar> rot1;
RealScalar t = m.coeff(0,0) + m.coeff(1,1);
RealScalar d = m.coeff(1,0) - m.coeff(0,1);
@ -426,7 +426,7 @@ void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
else
{
RealScalar u = d / t;
rot1.c() = RealScalar(1) / sqrt(RealScalar(1) + abs2(u));
rot1.c() = RealScalar(1) / sqrt(RealScalar(1) + numext::abs2(u));
rot1.s() = rot1.c() * u;
}
m.applyOnTheLeft(0,1,rot1);
@ -850,17 +850,12 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
// A = U S V^*
// So A^{-1} = V S^{-1} U^*
Index diagSize = (std::min)(dec().rows(), dec().cols());
typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
Matrix<Scalar, Dynamic, Rhs::ColsAtCompileTime, 0, _MatrixType::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime> tmp;
Index nonzeroSingVals = dec().nonzeroSingularValues();
invertedSingVals.head(nonzeroSingVals) = dec().singularValues().head(nonzeroSingVals).array().inverse();
invertedSingVals.tail(diagSize - nonzeroSingVals).setZero();
dst = dec().matrixV().leftCols(diagSize)
* invertedSingVals.asDiagonal()
* dec().matrixU().leftCols(diagSize).adjoint()
* rhs();
tmp.noalias() = dec().matrixU().leftCols(nonzeroSingVals).adjoint() * rhs();
tmp = dec().singularValues().head(nonzeroSingVals).asDiagonal().inverse() * tmp;
dst = dec().matrixV().leftCols(nonzeroSingVals) * tmp;
}
};
} // end namespace internal

4
Eigen/src/SVD/UpperBidiagonalization.h
View File

@ -39,7 +39,7 @@ template<typename _MatrixType> class UpperBidiagonalization
CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Diagonal<const MatrixType,0> >
> HouseholderUSequenceType;
typedef HouseholderSequence<
const MatrixType,
const typename internal::remove_all<typename MatrixType::ConjugateReturnType>::type,
Diagonal<const MatrixType,1>,
OnTheRight
> HouseholderVSequenceType;
@ -74,7 +74,7 @@ template<typename _MatrixType> class UpperBidiagonalization
const HouseholderVSequenceType householderV() // const here gives nasty errors and i'm lazy
{
eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
return HouseholderVSequenceType(m_householder, m_householder.const_derived().template diagonal<1>())
return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>())
.setLength(m_householder.cols()-1)
.setShift(1);
}

4
Eigen/src/SparseCholesky/SimplicialCholesky.h
View File

@ -364,7 +364,7 @@ public:
Scalar determinant() const
{
Scalar detL = Base::m_matrix.diagonal().prod();
return internal::abs2(detL);
return numext::abs2(detL);
}
};
@ -599,7 +599,7 @@ public:
else
{
Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
return internal::abs2(detL);
return numext::abs2(detL);
}
}

8
Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
View File

@ -131,7 +131,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
Index i = it.index();
if(i <= k)
{
y[i] += internal::conj(it.value()); /* scatter A(i,k) into Y (sum duplicates) */
y[i] += numext::conj(it.value()); /* scatter A(i,k) into Y (sum duplicates) */
Index len;
for(len = 0; tags[i] != k; i = m_parent[i])
{
@ -145,7 +145,7 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
/* compute numerical values kth row of L (a sparse triangular solve) */
RealScalar d = internal::real(y[k]) * m_shiftScale + m_shiftOffset; // get D(k,k), apply the shift function, and clear Y(k)
RealScalar d = numext::real(y[k]) * m_shiftScale + m_shiftOffset; // get D(k,k), apply the shift function, and clear Y(k)
y[k] = 0.0;
for(; top < size; ++top)
{
@ -163,8 +163,8 @@ void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType&
Index p2 = Lp[i] + m_nonZerosPerCol[i];
Index p;
for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
y[Li[p]] -= internal::conj(Lx[p]) * yi;
d -= internal::real(l_ki * internal::conj(yi));
y[Li[p]] -= numext::conj(Lx[p]) * yi;
d -= numext::real(l_ki * numext::conj(yi));
Li[p] = k; /* store L(k,i) in column form of L */
Lx[p] = l_ki;
++m_nonZerosPerCol[i]; /* increment count of nonzeros in col i */

2
Eigen/src/SparseCore/SparseBlock.h
View File

@ -27,6 +27,7 @@ public:
class InnerIterator: public XprType::InnerIterator
{
typedef typename BlockImpl::Index Index;
public:
inline InnerIterator(const BlockType& xpr, Index outer)
: XprType::InnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)
@ -38,6 +39,7 @@ public:
};
class ReverseInnerIterator: public XprType::ReverseInnerIterator
{
typedef typename BlockImpl::Index Index;
public:
inline ReverseInnerIterator(const BlockType& xpr, Index outer)
: XprType::ReverseInnerIterator(xpr.m_matrix, xpr.m_outerStart + outer), m_outer(outer)

6
Eigen/src/SparseCore/SparseCwiseBinaryOp.h
View File

@ -73,7 +73,7 @@ class CwiseBinaryOpImpl<BinaryOp,Lhs,Rhs,Sparse>::InnerIterator
typedef internal::sparse_cwise_binary_op_inner_iterator_selector<
BinaryOp,Lhs,Rhs, InnerIterator> Base;
EIGEN_STRONG_INLINE InnerIterator(const CwiseBinaryOpImpl& binOp, typename CwiseBinaryOpImpl::Index outer)
EIGEN_STRONG_INLINE InnerIterator(const CwiseBinaryOpImpl& binOp, Index outer)
: Base(binOp.derived(),outer)
{}
};
@ -300,7 +300,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
{
return *this = derived() - other.derived();
return derived() = derived() - other.derived();
}
template<typename Derived>
@ -308,7 +308,7 @@ template<typename OtherDerived>
EIGEN_STRONG_INLINE Derived &
SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
{
return *this = derived() + other.derived();
return derived() = derived() + other.derived();
}
template<typename Derived>

1
Eigen/src/SparseCore/SparseDenseProduct.h
View File

@ -111,6 +111,7 @@ template<typename Lhs, typename Rhs, bool Transpose>
class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNested::InnerIterator
{
typedef typename _LhsNested::InnerIterator Base;
typedef typename SparseDenseOuterProduct::Index Index;
public:
EIGEN_STRONG_INLINE InnerIterator(const SparseDenseOuterProduct& prod, Index outer)
: Base(prod.lhs(), 0), m_outer(outer), m_factor(prod.rhs().coeff(outer))

22
Eigen/src/SparseCore/SparseDiagonalProduct.h
View File

@ -78,7 +78,11 @@ class SparseDiagonalProduct
EIGEN_SPARSE_PUBLIC_INTERFACE(SparseDiagonalProduct)
typedef internal::sparse_diagonal_product_inner_iterator_selector
<_LhsNested,_RhsNested,SparseDiagonalProduct,LhsMode,RhsMode> InnerIterator;
<_LhsNested,_RhsNested,SparseDiagonalProduct,LhsMode,RhsMode> InnerIterator;
// We do not want ReverseInnerIterator for diagonal-sparse products,
// but this dummy declaration is needed to make diag * sparse * diag compile.
class ReverseInnerIterator;
EIGEN_STRONG_INLINE SparseDiagonalProduct(const Lhs& lhs, const Rhs& rhs)
: m_lhs(lhs), m_rhs(rhs)
@ -118,13 +122,13 @@ class sparse_diagonal_product_inner_iterator_selector
<Lhs,Rhs,SparseDiagonalProductType,SDP_IsDiagonal,SDP_IsSparseColMajor>
: public CwiseBinaryOp<
scalar_product_op<typename Lhs::Scalar>,
typename Rhs::ConstInnerVectorReturnType,
typename Lhs::DiagonalVectorType>::InnerIterator
const typename Rhs::ConstInnerVectorReturnType,
const typename Lhs::DiagonalVectorType>::InnerIterator
{
typedef typename CwiseBinaryOp<
scalar_product_op<typename Lhs::Scalar>,
typename Rhs::ConstInnerVectorReturnType,
typename Lhs::DiagonalVectorType>::InnerIterator Base;
const typename Rhs::ConstInnerVectorReturnType,
const typename Lhs::DiagonalVectorType>::InnerIterator Base;
typedef typename Lhs::Index Index;
Index m_outer;
public:
@ -156,13 +160,13 @@ class sparse_diagonal_product_inner_iterator_selector
<Lhs,Rhs,SparseDiagonalProductType,SDP_IsSparseRowMajor,SDP_IsDiagonal>
: public CwiseBinaryOp<
scalar_product_op<typename Rhs::Scalar>,
typename Lhs::ConstInnerVectorReturnType,
Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator
const typename Lhs::ConstInnerVectorReturnType,
const Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator
{
typedef typename CwiseBinaryOp<
scalar_product_op<typename Rhs::Scalar>,
typename Lhs::ConstInnerVectorReturnType,
Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base;
const typename Lhs::ConstInnerVectorReturnType,
const Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base;
typedef typename Lhs::Index Index;
Index m_outer;
public:

6
Eigen/src/SparseCore/SparseDot.h
View File

@ -30,7 +30,7 @@ SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
Scalar res(0);
while (i)
{
res += internal::conj(i.value()) * other.coeff(i.index());
res += numext::conj(i.value()) * other.coeff(i.index());
++i;
}
return res;
@ -64,7 +64,7 @@ SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) cons
{
if (i.index()==j.index())
{
res += internal::conj(i.value()) * j.value();
res += numext::conj(i.value()) * j.value();
++i; ++j;
}
else if (i.index()<j.index())
@ -79,7 +79,7 @@ template<typename Derived>
inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
SparseMatrixBase<Derived>::squaredNorm() const
{
return internal::real((*this).cwiseAbs2().sum());
return numext::real((*this).cwiseAbs2().sum());
}
template<typename Derived>

165
Eigen/src/SparseCore/SparseMatrix.h
View File

@ -31,7 +31,7 @@ namespace Eigen {
*
* \tparam _Scalar the scalar type, i.e. the type of the coefficients
* \tparam _Options Union of bit flags controlling the storage scheme. Currently the only possibility
* is RowMajor. The default is 0 which means column-major.
* is ColMajor or RowMajor. The default is 0 which means column-major.
* \tparam _Index the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
*
* This class can be extended with the help of the plugin mechanism described on the page
@ -170,6 +170,8 @@ class SparseMatrix
* This function returns Scalar(0) if the element is an explicit \em zero */
inline Scalar coeff(Index row, Index col) const
{
eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
@ -186,6 +188,8 @@ class SparseMatrix
*/
inline Scalar& coeffRef(Index row, Index col)
{
eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
@ -215,6 +219,8 @@ class SparseMatrix
*/
Scalar& insert(Index row, Index col)
{
eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
if(isCompressed())
{
reserve(VectorXi::Constant(outerSize(), 2));
@ -281,7 +287,6 @@ class SparseMatrix
template<class SizesType>
inline void reserveInnerVectors(const SizesType& reserveSizes)
{
if(isCompressed())
{
std::size_t totalReserveSize = 0;
@ -526,59 +531,63 @@ class SparseMatrix
*/
void conservativeResize(Index rows, Index cols)
{
// No change
if (this->rows() == rows && this->cols() == cols) return;
// No change
if (this->rows() == rows && this->cols() == cols) return;
// If one dimension is null, then there is nothing to be preserved
if(rows==0 || cols==0) return resize(rows,cols);
Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows();
Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols();
Index newInnerSize = IsRowMajor ? cols : rows;
Index innerChange = IsRowMajor ? cols - this->cols() : rows - this->rows();
Index outerChange = IsRowMajor ? rows - this->rows() : cols - this->cols();
Index newInnerSize = IsRowMajor ? cols : rows;
// Deals with inner non zeros
if (m_innerNonZeros)
// Deals with inner non zeros
if (m_innerNonZeros)
{
// Resize m_innerNonZeros
Index *newInnerNonZeros = static_cast<Index*>(std::realloc(m_innerNonZeros, (m_outerSize + outerChange) * sizeof(Index)));
if (!newInnerNonZeros) internal::throw_std_bad_alloc();
m_innerNonZeros = newInnerNonZeros;
for(Index i=m_outerSize; i<m_outerSize+outerChange; i++)
m_innerNonZeros[i] = 0;
}
else if (innerChange < 0)
{
// Inner size decreased: allocate a new m_innerNonZeros
m_innerNonZeros = static_cast<Index*>(std::malloc((m_outerSize+outerChange+1) * sizeof(Index)));
if (!m_innerNonZeros) internal::throw_std_bad_alloc();
for(Index i = 0; i < m_outerSize; i++)
m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
}
// Change the m_innerNonZeros in case of a decrease of inner size
if (m_innerNonZeros && innerChange < 0)
{
for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
{
// Resize m_innerNonZeros
Index *newInnerNonZeros = static_cast<Index*>(std::realloc(m_innerNonZeros, (m_outerSize + outerChange) * sizeof(Index)));
if (!newInnerNonZeros) internal::throw_std_bad_alloc();
m_innerNonZeros = newInnerNonZeros;
Index &n = m_innerNonZeros[i];
Index start = m_outerIndex[i];
while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n;
}
}
m_innerSize = newInnerSize;
// Re-allocate outer index structure if necessary
if (outerChange == 0)
return;
for(Index i=m_outerSize; i<m_outerSize+outerChange; i++)
m_innerNonZeros[i] = 0;
}
else if (innerChange < 0)
{
// Inner size decreased: allocate a new m_innerNonZeros
m_innerNonZeros = static_cast<Index*>(std::malloc((m_outerSize+outerChange+1) * sizeof(Index)));
if (!m_innerNonZeros) internal::throw_std_bad_alloc();
for(Index i = 0; i < m_outerSize; i++)
m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
}
// Change the m_innerNonZeros in case of a decrease of inner size
if (m_innerNonZeros && innerChange < 0) {
for(Index i = 0; i < m_outerSize + (std::min)(outerChange, Index(0)); i++)
{
Index &n = m_innerNonZeros[i];
Index start = m_outerIndex[i];
while (n > 0 && m_data.index(start+n-1) >= newInnerSize) --n;
}
}
m_innerSize = newInnerSize;
// Re-allocate outer index structure if necessary
if (outerChange == 0)
return;
Index *newOuterIndex = static_cast<Index*>(std::realloc(m_outerIndex, (m_outerSize + outerChange + 1) * sizeof(Index)));
if (!newOuterIndex) internal::throw_std_bad_alloc();
m_outerIndex = newOuterIndex;
if (outerChange > 0) {
Index last = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize];
for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++)
m_outerIndex[i] = last;
}
m_outerSize += outerChange;
Index *newOuterIndex = static_cast<Index*>(std::realloc(m_outerIndex, (m_outerSize + outerChange + 1) * sizeof(Index)));
if (!newOuterIndex) internal::throw_std_bad_alloc();
m_outerIndex = newOuterIndex;
if (outerChange > 0)
{
Index last = m_outerSize == 0 ? 0 : m_outerIndex[m_outerSize];
for(Index i=m_outerSize; i<m_outerSize+outerChange+1; i++)
m_outerIndex[i] = last;
}
m_outerSize += outerChange;
}
/** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
@ -637,9 +646,20 @@ class SparseMatrix
inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
: m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
{
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
check_template_parameters();
*this = other.derived();
}
/** Constructs a sparse matrix from the sparse selfadjoint view \a other */
template<typename OtherDerived, unsigned int UpLo>
inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other)
: m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
{
check_template_parameters();
*this = other;
}
/** Copy constructor (it performs a deep copy) */
inline SparseMatrix(const SparseMatrix& other)
@ -671,13 +691,22 @@ class SparseMatrix
m_data.swap(other.m_data);
}
/** Sets *this to the identity matrix */
inline void setIdentity()
{
eigen_assert(rows() == cols() && "ONLY FOR SQUARED MATRICES");
this->m_data.resize(rows());
Eigen::Map<Matrix<Index, Dynamic, 1> >(&this->m_data.index(0), rows()).setLinSpaced(0, rows()-1);
Eigen::Map<Matrix<Scalar, Dynamic, 1> >(&this->m_data.value(0), rows()).setOnes();
Eigen::Map<Matrix<Index, Dynamic, 1> >(this->m_outerIndex, rows()+1).setLinSpaced(0, rows());
}
inline SparseMatrix& operator=(const SparseMatrix& other)
{
if (other.isRValue())
{
swap(other.const_cast_derived());
}
else
else if(this!=&other)
{
initAssignment(other);
if(other.isCompressed())
@ -822,6 +851,7 @@ private:
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT(NumTraits<Index>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
}
struct default_prunning_func {
@ -911,19 +941,25 @@ void set_from_triplets(const InputIterator& begin, const InputIterator& end, Spa
typedef typename SparseMatrixType::Scalar Scalar;
SparseMatrix<Scalar,IsRowMajor?ColMajor:RowMajor> trMat(mat.rows(),mat.cols());
// pass 1: count the nnz per inner-vector
VectorXi wi(trMat.outerSize());
wi.setZero();
for(InputIterator it(begin); it!=end; ++it)
wi(IsRowMajor ? it->col() : it->row())++;
if(begin<end)
{
// pass 1: count the nnz per inner-vector
VectorXi wi(trMat.outerSize());
wi.setZero();
for(InputIterator it(begin); it!=end; ++it)
{
eigen_assert(it->row()>=0 && it->row()<mat.rows() && it->col()>=0 && it->col()<mat.cols());
wi(IsRowMajor ? it->col() : it->row())++;
}
// pass 2: insert all the elements into trMat
trMat.reserve(wi);
for(InputIterator it(begin); it!=end; ++it)
trMat.insertBackUncompressed(it->row(),it->col()) = it->value();
// pass 2: insert all the elements into trMat
trMat.reserve(wi);
for(InputIterator it(begin); it!=end; ++it)
trMat.insertBackUncompressed(it->row(),it->col()) = it->value();
// pass 3:
trMat.sumupDuplicates();
// pass 3:
trMat.sumupDuplicates();
}
// pass 4: transposed copy -> implicit sorting
mat = trMat;
@ -1020,6 +1056,9 @@ template<typename Scalar, int _Options, typename _Index>
template<typename OtherDerived>
EIGEN_DONT_INLINE SparseMatrix<Scalar,_Options,_Index>& SparseMatrix<Scalar,_Options,_Index>::operator=(const SparseMatrixBase<OtherDerived>& other)
{
EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
if (needToTranspose)
{

33
Eigen/src/SparseCore/SparseMatrixBase.h
View File

@ -89,6 +89,9 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
*/
IsRowMajor = Flags&RowMajorBit ? 1 : 0,
InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
: int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
#ifndef EIGEN_PARSED_BY_DOXYGEN
_HasDirectAccess = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
@ -322,8 +325,8 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
typename internal::traits<OtherDerived>::Scalar \
>::ReturnType \
>, \
Derived, \
OtherDerived \
const Derived, \
const OtherDerived \
>
template<typename OtherDerived>
@ -403,20 +406,20 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
Block<Derived,Dynamic,Dynamic,true> innerVectors(Index outerStart, Index outerSize);
const Block<const Derived,Dynamic,Dynamic,true> innerVectors(Index outerStart, Index outerSize) const;
/** \internal use operator= */
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& dst) const
{
dst.setZero();
for (Index j=0; j<outerSize(); ++j)
for (typename Derived::InnerIterator i(derived(),j); i; ++i)
dst.coeffRef(i.row(),i.col()) = i.value();
}
/** \internal use operator= */
template<typename DenseDerived>
void evalTo(MatrixBase<DenseDerived>& dst) const
{
dst.setZero();
for (Index j=0; j<outerSize(); ++j)
for (typename Derived::InnerIterator i(derived(),j); i; ++i)
dst.coeffRef(i.row(),i.col()) = i.value();
}
Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const
{
return derived();
}
Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const
{
return derived();
}
template<typename OtherDerived>
bool isApprox(const SparseMatrixBase<OtherDerived>& other,

35
Eigen/src/SparseCore/SparseSelfAdjointView.h
View File

@ -69,6 +69,30 @@ template<typename MatrixType, unsigned int UpLo> class SparseSelfAdjointView
const _MatrixTypeNested& matrix() const { return m_matrix; }
_MatrixTypeNested& matrix() { return m_matrix.const_cast_derived(); }
/** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs.
*
* Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
* Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
*/
template<typename OtherDerived>
SparseSparseProduct<SparseMatrix<Scalar, ((internal::traits<OtherDerived>::Flags&RowMajorBit) ? RowMajor : ColMajor),Index>, OtherDerived>
operator*(const SparseMatrixBase<OtherDerived>& rhs) const
{
return SparseSparseProduct<SparseMatrix<Scalar, (internal::traits<OtherDerived>::Flags&RowMajorBit) ? RowMajor : ColMajor, Index>, OtherDerived>(*this, rhs.derived());
}
/** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a rhs.
*
* Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
* Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
*/
template<typename OtherDerived> friend
SparseSparseProduct<OtherDerived, SparseMatrix<Scalar, ((internal::traits<OtherDerived>::Flags&RowMajorBit) ? RowMajor : ColMajor),Index> >
operator*(const SparseMatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
{
return SparseSparseProduct< OtherDerived, SparseMatrix<Scalar, (internal::traits<OtherDerived>::Flags&RowMajorBit) ? RowMajor : ColMajor, Index> >(lhs.derived(), rhs.derived());
}
/** Efficient sparse self-adjoint matrix times dense vector/matrix product */
template<typename OtherDerived>
SparseSelfAdjointTimeDenseProduct<MatrixType,OtherDerived,UpLo>
@ -240,7 +264,7 @@ class SparseSelfAdjointTimeDenseProduct
Index b = LhsIsRowMajor ? i.index() : j;
typename Lhs::Scalar v = i.value();
dest.row(a) += (v) * m_rhs.row(b);
dest.row(b) += internal::conj(v) * m_rhs.row(a);
dest.row(b) += numext::conj(v) * m_rhs.row(a);
}
if (ProcessFirstHalf && i && (i.index()==j))
dest.row(j) += i.value() * m_rhs.row(j);
@ -367,7 +391,7 @@ void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename Matri
dest.valuePtr()[k] = it.value();
k = count[ip]++;
dest.innerIndexPtr()[k] = jp;
dest.valuePtr()[k] = internal::conj(it.value());
dest.valuePtr()[k] = numext::conj(it.value());
}
}
}
@ -428,7 +452,7 @@ void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixTyp
if(!StorageOrderMatch) std::swap(ip,jp);
if( ((int(DstUpLo)==int(Lower) && ip<jp) || (int(DstUpLo)==int(Upper) && ip>jp)))
dest.valuePtr()[k] = conj(it.value());
dest.valuePtr()[k] = numext::conj(it.value());
else
dest.valuePtr()[k] = it.value();
}
@ -461,7 +485,10 @@ class SparseSymmetricPermutationProduct
template<typename DestScalar, int Options, typename DstIndex>
void evalTo(SparseMatrix<DestScalar,Options,DstIndex>& _dest) const
{
internal::permute_symm_to_fullsymm<UpLo>(m_matrix,_dest,m_perm.indices().data());
// internal::permute_symm_to_fullsymm<UpLo>(m_matrix,_dest,m_perm.indices().data());
SparseMatrix<DestScalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
internal::permute_symm_to_fullsymm<UpLo>(m_matrix,tmp,m_perm.indices().data());
_dest = tmp;
}
template<typename DestType,unsigned int DestUpLo> void evalTo(SparseSelfAdjointView<DestType,DestUpLo>& dest) const

10
Eigen/src/SparseCore/SparseTranspose.h
View File

@ -34,26 +34,28 @@ template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::InnerItera
: public _MatrixTypeNested::InnerIterator
{
typedef typename _MatrixTypeNested::InnerIterator Base;
typedef typename TransposeImpl::Index Index;
public:
EIGEN_STRONG_INLINE InnerIterator(const TransposeImpl& trans, typename TransposeImpl<MatrixType,Sparse>::Index outer)
: Base(trans.derived().nestedExpression(), outer)
{}
inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col(); }
inline typename TransposeImpl<MatrixType,Sparse>::Index col() const { return Base::row(); }
Index row() const { return Base::col(); }
Index col() const { return Base::row(); }
};
template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>::ReverseInnerIterator
: public _MatrixTypeNested::ReverseInnerIterator
{
typedef typename _MatrixTypeNested::ReverseInnerIterator Base;
typedef typename TransposeImpl::Index Index;
public:
EIGEN_STRONG_INLINE ReverseInnerIterator(const TransposeImpl& xpr, typename TransposeImpl<MatrixType,Sparse>::Index outer)
: Base(xpr.derived().nestedExpression(), outer)
{}
inline typename TransposeImpl<MatrixType,Sparse>::Index row() const { return Base::col(); }
inline typename TransposeImpl<MatrixType,Sparse>::Index col() const { return Base::row(); }
Index row() const { return Base::col(); }
Index col() const { return Base::row(); }
};
} // end namespace Eigen

31
Eigen/src/SparseCore/SparseTriangularView.h
View File

@ -2,6 +2,7 @@
// for linear algebra.
//
// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
@ -27,6 +28,7 @@ template<typename MatrixType, int Mode> class SparseTriangularView
enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit))
|| ((Mode&Upper) && (MatrixType::Flags&RowMajorBit)),
SkipLast = !SkipFirst,
SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
};
@ -64,6 +66,7 @@ template<typename MatrixType, int Mode>
class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNestedCleaned::InnerIterator
{
typedef typename MatrixTypeNestedCleaned::InnerIterator Base;
typedef typename SparseTriangularView::Index Index;
public:
EIGEN_STRONG_INLINE InnerIterator(const SparseTriangularView& view, Index outer)
@ -71,7 +74,7 @@ class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNe
{
if(SkipFirst)
{
while((*this) && (HasUnitDiag ? this->index()<=outer : this->index()<outer))
while((*this) && ((HasUnitDiag||SkipDiag) ? this->index()<=outer : this->index()<outer))
Base::operator++();
if(HasUnitDiag)
m_returnOne = true;
@ -101,8 +104,8 @@ class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNe
return *this;
}
inline Index row() const { return Base::row(); }
inline Index col() const { return Base::col(); }
inline Index row() const { return (MatrixType::Flags&RowMajorBit ? Base::outer() : this->index()); }
inline Index col() const { return (MatrixType::Flags&RowMajorBit ? this->index() : Base::outer()); }
inline Index index() const
{
if(HasUnitDiag && m_returnOne) return Base::outer();
@ -118,7 +121,12 @@ class SparseTriangularView<MatrixType,Mode>::InnerIterator : public MatrixTypeNe
{
if(HasUnitDiag && m_returnOne)
return true;
return (SkipFirst ? Base::operator bool() : (Base::operator bool() && this->index() <= this->outer()));
if(SkipFirst) return Base::operator bool();
else
{
if (SkipDiag) return (Base::operator bool() && this->index() < this->outer());
else return (Base::operator bool() && this->index() <= this->outer());
}
}
protected:
bool m_returnOne;
@ -128,18 +136,20 @@ template<typename MatrixType, int Mode>
class SparseTriangularView<MatrixType,Mode>::ReverseInnerIterator : public MatrixTypeNestedCleaned::ReverseInnerIterator
{
typedef typename MatrixTypeNestedCleaned::ReverseInnerIterator Base;
typedef typename SparseTriangularView::Index Index;
public:
EIGEN_STRONG_INLINE ReverseInnerIterator(const SparseTriangularView& view, Index outer)
: Base(view.nestedExpression(), outer)
{
eigen_assert((!HasUnitDiag) && "ReverseInnerIterator does not support yet triangular views with a unit diagonal");
if(SkipLast)
while((*this) && this->index()>outer)
if(SkipLast) {
while((*this) && (SkipDiag ? this->index()>=outer : this->index()>outer))
--(*this);
}
}
EIGEN_STRONG_INLINE InnerIterator& operator--()
EIGEN_STRONG_INLINE ReverseInnerIterator& operator--()
{ Base::operator--(); return *this; }
inline Index row() const { return Base::row(); }
@ -147,7 +157,12 @@ class SparseTriangularView<MatrixType,Mode>::ReverseInnerIterator : public Matri
EIGEN_STRONG_INLINE operator bool() const
{
return SkipLast ? Base::operator bool() : (Base::operator bool() && this->index() >= this->outer());
if (SkipLast) return Base::operator bool() ;
else
{
if(SkipDiag) return (Base::operator bool() && this->index() > this->outer());
else return (Base::operator bool() && this->index() >= this->outer());
}
}
};

16
Eigen/src/SparseCore/SparseUtil.h
View File

@ -98,16 +98,16 @@ template<typename T> struct eval<T,Sparse>
template<typename T,int Cols> struct sparse_eval<T,1,Cols> {
typedef typename traits<T>::Scalar _Scalar;
enum { _Flags = traits<T>::Flags| RowMajorBit };
typedef typename traits<T>::Index _Index;
public:
typedef SparseVector<_Scalar, _Flags> type;
typedef SparseVector<_Scalar, RowMajor, _Index> type;
};
template<typename T,int Rows> struct sparse_eval<T,Rows,1> {
typedef typename traits<T>::Scalar _Scalar;
enum { _Flags = traits<T>::Flags & (~RowMajorBit) };
typedef typename traits<T>::Index _Index;
public:
typedef SparseVector<_Scalar, _Flags> type;
typedef SparseVector<_Scalar, ColMajor, _Index> type;
};
template<typename T,int Rows,int Cols> struct sparse_eval {
@ -127,12 +127,10 @@ template<typename T> struct sparse_eval<T,1,1> {
template<typename T> struct plain_matrix_type<T,Sparse>
{
typedef typename traits<T>::Scalar _Scalar;
enum {
_Flags = traits<T>::Flags
};
typedef typename traits<T>::Index _Index;
enum { _Options = ((traits<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
public:
typedef SparseMatrix<_Scalar, _Flags> type;
typedef SparseMatrix<_Scalar, _Options, _Index> type;
};
} // end namespace internal

103
Eigen/src/SparseCore/SparseVector.h
View File

@ -45,6 +45,20 @@ struct traits<SparseVector<_Scalar, _Options, _Index> >
SupportedAccessPatterns = InnerRandomAccessPattern
};
};
// Sparse-Vector-Assignment kinds:
enum {
SVA_RuntimeSwitch,
SVA_Inner,
SVA_Outer
};
template< typename Dest, typename Src,
int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
: Src::InnerSizeAtCompileTime==1 ? SVA_Outer
: SVA_Inner>
struct sparse_vector_assign_selector;
}
template<typename _Scalar, int _Options, typename _Index>
@ -83,14 +97,18 @@ class SparseVector
inline Scalar coeff(Index row, Index col) const
{
eigen_assert((IsColVector ? col : row)==0);
eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
return coeff(IsColVector ? row : col);
}
inline Scalar coeff(Index i) const { return m_data.at(i); }
inline Scalar coeff(Index i) const
{
eigen_assert(i>=0 && i<m_size);
return m_data.at(i);
}
inline Scalar& coeffRef(Index row, Index col)
{
eigen_assert((IsColVector ? col : row)==0);
eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
return coeff(IsColVector ? row : col);
}
@ -102,6 +120,7 @@ class SparseVector
*/
inline Scalar& coeffRef(Index i)
{
eigen_assert(i>=0 && i<m_size);
return m_data.atWithInsertion(i);
}
@ -135,6 +154,8 @@ class SparseVector
inline Scalar& insert(Index row, Index col)
{
eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
Index inner = IsColVector ? row : col;
Index outer = IsColVector ? col : row;
eigen_assert(outer==0);
@ -142,6 +163,8 @@ class SparseVector
}
Scalar& insert(Index i)
{
eigen_assert(i>=0 && i<m_size);
Index startId = 0;
Index p = Index(m_data.size()) - 1;
// TODO smart realloc
@ -184,22 +207,24 @@ class SparseVector
void resizeNonZeros(Index size) { m_data.resize(size); }
inline SparseVector() : m_size(0) { resize(0); }
inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); }
inline SparseVector(Index size) : m_size(0) { resize(size); }
inline SparseVector(Index size) : m_size(0) { check_template_parameters(); resize(size); }
inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows,cols); }
inline SparseVector(Index rows, Index cols) : m_size(0) { check_template_parameters(); resize(rows,cols); }
template<typename OtherDerived>
inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
: m_size(0)
{
check_template_parameters();
*this = other.derived();
}
inline SparseVector(const SparseVector& other)
: SparseBase(other), m_size(0)
{
check_template_parameters();
*this = other.derived();
}
@ -230,11 +255,10 @@ class SparseVector
template<typename OtherDerived>
inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
{
if ( (bool(OtherDerived::IsVectorAtCompileTime) && int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
|| ((!bool(OtherDerived::IsVectorAtCompileTime)) && ( bool(IsColVector) ? other.cols()>1 : other.rows()>1 )))
return assign(other.transpose());
else
return assign(other);
SparseVector tmp(other.size());
internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
this->swap(tmp);
return *this;
}
#ifndef EIGEN_PARSED_BY_DOXYGEN
@ -309,8 +333,12 @@ class SparseVector
# endif
protected:
template<typename OtherDerived>
EIGEN_DONT_INLINE SparseVector& assign(const SparseMatrixBase<OtherDerived>& _other);
static void check_template_parameters()
{
EIGEN_STATIC_ASSERT(NumTraits<Index>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE);
EIGEN_STATIC_ASSERT((_Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS);
}
Storage m_data;
Index m_size;
@ -380,33 +408,40 @@ class SparseVector<Scalar,_Options,_Index>::ReverseInnerIterator
const Index m_start;
};
template<typename Scalar, int _Options, typename _Index>
template<typename OtherDerived>
EIGEN_DONT_INLINE SparseVector<Scalar,_Options,_Index>& SparseVector<Scalar,_Options,_Index>::assign(const SparseMatrixBase<OtherDerived>& _other)
{
const OtherDerived& other(_other.derived());
const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
if(needToTranspose)
{
Index size = other.size();
Index nnz = other.nonZeros();
resize(size);
reserve(nnz);
for(Index i=0; i<size; ++i)
namespace internal {
template< typename Dest, typename Src>
struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
static void run(Dest& dst, const Src& src) {
eigen_internal_assert(src.innerSize()==src.size());
for(typename Src::InnerIterator it(src, 0); it; ++it)
dst.insert(it.index()) = it.value();
}
};
template< typename Dest, typename Src>
struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
static void run(Dest& dst, const Src& src) {
eigen_internal_assert(src.outerSize()==src.size());
for(typename Dest::Index i=0; i<src.size(); ++i)
{
typename OtherDerived::InnerIterator it(other, i);
typename Src::InnerIterator it(src, i);
if(it)
insert(i) = it.value();
dst.insert(i) = it.value();
}
return *this;
}
else
{
// there is no special optimization
return Base::operator=(other);
};
template< typename Dest, typename Src>
struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
static void run(Dest& dst, const Src& src) {
if(src.outerSize()==1) sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
else sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
}
};
}
} // end namespace Eigen
#endif // EIGEN_SPARSEVECTOR_H

3
Eigen/src/SparseCore/SparseView.h
View File

@ -18,7 +18,7 @@ namespace internal {
template<typename MatrixType>
struct traits<SparseView<MatrixType> > : traits<MatrixType>
{
typedef int Index;
typedef typename MatrixType::Index Index;
typedef Sparse StorageKind;
enum {
Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
@ -56,6 +56,7 @@ protected:
template<typename MatrixType>
class SparseView<MatrixType>::InnerIterator : public _MatrixTypeNested::InnerIterator
{
typedef typename SparseView::Index Index;
public:
typedef typename _MatrixTypeNested::InnerIterator IterBase;
InnerIterator(const SparseView& view, Index outer) :

340
Eigen/src/SparseLU/SparseLU.h
View File

@ -14,8 +14,10 @@
namespace Eigen {
template <typename _MatrixType, typename _OrderingType> class SparseLU;
template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
template <typename _MatrixType, typename _OrderingType = COLAMDOrdering<typename _MatrixType::Index> > class SparseLU;
template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
/** \ingroup SparseLU_Module
* \class SparseLU
*
@ -61,7 +63,7 @@ template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
* "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
*
* \tparam _MatrixType The type of the sparse matrix. It must be a column-major SparseMatrix<>
* \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS
* \tparam _OrderingType The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
*
*
* \sa \ref TutorialSparseDirectSolvers
@ -84,11 +86,11 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
typedef internal::SparseLUImpl<Scalar, Index> Base;
public:
SparseLU():m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0)
SparseLU():m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
{
initperfvalues();
}
SparseLU(const MatrixType& matrix):m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0)
SparseLU(const MatrixType& matrix):m_isInitialized(true),m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
{
initperfvalues();
compute(matrix);
@ -104,9 +106,9 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
void simplicialfactorize(const MatrixType& matrix);
/**
* Compute the symbolic and numeric factorization of the input sparse matrix.
* The input matrix should be in column-major storage.
*/
* Compute the symbolic and numeric factorization of the input sparse matrix.
* The input matrix should be in column-major storage.
*/
void compute (const MatrixType& matrix)
{
// Analyze
@ -123,16 +125,43 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
m_symmetricmode = sym;
}
/** Returns an expression of the matrix L, internally stored as supernodes
* For a triangular solve with this matrix, use
* \code
* y = b; matrixL().solveInPlace(y);
* \endcode
*/
/** \returns an expression of the matrix L, internally stored as supernodes
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixL().solveInPlace(y);
* \endcode
*/
SparseLUMatrixLReturnType<SCMatrix> matrixL() const
{
return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore);
}
/** \returns an expression of the matrix U,
* The only operation available with this expression is the triangular solve
* \code
* y = b; matrixU().solveInPlace(y);
* \endcode
*/
SparseLUMatrixUReturnType<SCMatrix,MappedSparseMatrix<Scalar,ColMajor,Index> > matrixU() const
{
return SparseLUMatrixUReturnType<SCMatrix, MappedSparseMatrix<Scalar,ColMajor,Index> >(m_Lstore, m_Ustore);
}
/**
* \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
* \sa colsPermutation()
*/
inline const PermutationType& rowsPermutation() const
{
return m_perm_r;
}
/**
* \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
* \sa rowsPermutation()
*/
inline const PermutationType& colsPermutation() const
{
return m_perm_c;
}
/** Set the threshold used for a diagonal entry to be an acceptable pivot. */
void setPivotThreshold(const RealScalar& thresh)
{
@ -154,7 +183,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
return internal::solve_retval<SparseLU, Rhs>(*this, B.derived());
}
/** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
/** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
*
* \sa compute()
*/
@ -167,7 +196,7 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
return internal::sparse_solve_retval<SparseLU, Rhs>(*this, B.derived());
}
/** \brief Reports whether previous computation was successful.
/** \brief Reports whether previous computation was successful.
*
* \returns \c Success if computation was succesful,
* \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
@ -180,13 +209,15 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
eigen_assert(m_isInitialized && "Decomposition is not initialized.");
return m_info;
}
/**
* \returns A string describing the type of error
*/
* \returns A string describing the type of error
*/
std::string lastErrorMessage() const
{
return m_lastError;
}
template<typename Rhs, typename Dest>
bool _solve(const MatrixBase<Rhs> &B, MatrixBase<Dest> &_X) const
{
@ -195,68 +226,97 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
Index nrhs = B.cols();
Index n = B.rows();
// Permute the right hand side to form X = Pr*B
// on return, X is overwritten by the computed solution
X.resize(n,nrhs);
for(Index j = 0; j < nrhs; ++j)
X.col(j) = m_perm_r * B.col(j);
X.resize(B.rows(),B.cols());
for(Index j = 0; j < B.cols(); ++j)
X.col(j) = rowsPermutation() * B.col(j);
//Forward substitution with L
// m_Lstore.solveInPlace(X);
this->matrixL().solveInPlace(X);
// Backward solve with U
for (Index k = m_Lstore.nsuper(); k >= 0; k--)
{
Index fsupc = m_Lstore.supToCol()[k];
Index lda = m_Lstore.colIndexPtr()[fsupc+1] - m_Lstore.colIndexPtr()[fsupc]; // leading dimension
Index nsupc = m_Lstore.supToCol()[k+1] - fsupc;
Index luptr = m_Lstore.colIndexPtr()[fsupc];
if (nsupc == 1)
{
for (Index j = 0; j < nrhs; j++)
{
X(fsupc, j) /= m_Lstore.valuePtr()[luptr];
}
}
else
{
Map<const Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(m_Lstore.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
Map< Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
U = A.template triangularView<Upper>().solve(U);
}
for (Index j = 0; j < nrhs; ++j)
{
for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
{
typename MappedSparseMatrix<Scalar,ColMajor, Index>::InnerIterator it(m_Ustore, jcol);
for ( ; it; ++it)
{
Index irow = it.index();
X(irow, j) -= X(jcol, j) * it.value();
}
}
}
} // End For U-solve
//Forward substitution with L
this->matrixL().solveInPlace(X);
this->matrixU().solveInPlace(X);
// Permute back the solution
for (Index j = 0; j < nrhs; ++j)
X.col(j) = m_perm_c.inverse() * X.col(j);
for (Index j = 0; j < B.cols(); ++j)
X.col(j) = colsPermutation().inverse() * X.col(j);
return true;
}
/**
* \returns the absolute value of the determinant of the matrix of which
* *this is the QR decomposition.
*
* \warning a determinant can be very big or small, so for matrices
* of large enough dimension, there is a risk of overflow/underflow.
* One way to work around that is to use logAbsDeterminant() instead.
*
* \sa logAbsDeterminant(), signDeterminant()
*/
Scalar absDeterminant()
{
eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
// Initialize with the determinant of the row matrix
Scalar det = Scalar(1.);
//Note that the diagonal blocks of U are stored in supernodes,
// which are available in the L part :)
for (Index j = 0; j < this->cols(); ++j)
{
for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
{
if(it.row() < j) continue;
if(it.row() == j)
{
det *= (std::abs)(it.value());
break;
}
}
}
return det;
}
/** \returns the natural log of the absolute value of the determinant of the matrix
* of which **this is the QR decomposition
*
* \note This method is useful to work around the risk of overflow/underflow that's
* inherent to the determinant computation.
*
* \sa absDeterminant(), signDeterminant()
*/
Scalar logAbsDeterminant() const
{
eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
Scalar det = Scalar(0.);
for (Index j = 0; j < this->cols(); ++j)
{
for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
{
if(it.row() < j) continue;
if(it.row() == j)
{
det += (std::log)((std::abs)(it.value()));
break;
}
}
}
return det;
}
/** \returns A number representing the sign of the determinant
*
* \sa absDeterminant(), logAbsDeterminant()
*/
Scalar signDeterminant()
{
eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
return Scalar(m_detPermR);
}
protected:
// Functions
void initperfvalues()
{
m_perfv.panel_size = 12;
m_perfv.panel_size = 1;
m_perfv.relax = 1;
m_perfv.maxsuper = 128;
m_perfv.rowblk = 16;
@ -285,25 +345,26 @@ class SparseLU : public internal::SparseLUImpl<typename _MatrixType::Scalar, typ
internal::perfvalues<Index> m_perfv;
RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot
Index m_nnzL, m_nnzU; // Nonzeros in L and U factors
Index m_detPermR; // Determinant of the coefficient matrix
private:
// Copy constructor
SparseLU (SparseLU& ) {}
// Disable copy constructor
SparseLU (const SparseLU& );
}; // End class SparseLU
// Functions needed by the anaysis phase
/**
* Compute the column permutation to minimize the fill-in
*
* - Apply this permutation to the input matrix -
*
* - Compute the column elimination tree on the permuted matrix
*
* - Postorder the elimination tree and the column permutation
*
*/
* Compute the column permutation to minimize the fill-in
*
* - Apply this permutation to the input matrix -
*
* - Compute the column elimination tree on the permuted matrix
*
* - Postorder the elimination tree and the column permutation
*
*/
template <typename MatrixType, typename OrderingType>
void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
{
@ -319,11 +380,20 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
if (m_perm_c.size()) {
m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.
//Then, permute only the column pointers
const Index * outerIndexPtr;
if (mat.isCompressed()) outerIndexPtr = mat.outerIndexPtr();
else
{
Index *outerIndexPtr_t = new Index[mat.cols()+1];
for(Index i = 0; i <= mat.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
outerIndexPtr = outerIndexPtr_t;
}
for (Index i = 0; i < mat.cols(); i++)
{
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = mat.outerIndexPtr()[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = mat.outerIndexPtr()[i+1] - mat.outerIndexPtr()[i];
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
}
if(!mat.isCompressed()) delete[] outerIndexPtr;
}
// Compute the column elimination tree of the permuted matrix
IndexVector firstRowElt;
@ -361,23 +431,23 @@ void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
/**
* - Numerical factorization
* - Interleaved with the symbolic factorization
* On exit, info is
*
* = 0: successful factorization
*
* > 0: if info = i, and i is
*
* <= A->ncol: U(i,i) is exactly zero. The factorization has
* been completed, but the factor U is exactly singular,
* and division by zero will occur if it is used to solve a
* system of equations.
*
* > A->ncol: number of bytes allocated when memory allocation
* failure occurred, plus A->ncol. If lwork = -1, it is
* the estimated amount of space needed, plus A->ncol.
*/
* - Numerical factorization
* - Interleaved with the symbolic factorization
* On exit, info is
*
* = 0: successful factorization
*
* > 0: if info = i, and i is
*
* <= A->ncol: U(i,i) is exactly zero. The factorization has
* been completed, but the factor U is exactly singular,
* and division by zero will occur if it is used to solve a
* system of equations.
*
* > A->ncol: number of bytes allocated when memory allocation
* failure occurred, plus A->ncol. If lwork = -1, it is
* the estimated amount of space needed, plus A->ncol.
*/
template <typename MatrixType, typename OrderingType>
void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
{
@ -395,11 +465,20 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
{
m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers.
//Then, permute only the column pointers
const Index * outerIndexPtr;
if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr();
else
{
Index* outerIndexPtr_t = new Index[matrix.cols()+1];
for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
outerIndexPtr = outerIndexPtr_t;
}
for (Index i = 0; i < matrix.cols(); i++)
{
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = matrix.outerIndexPtr()[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = matrix.outerIndexPtr()[i+1] - matrix.outerIndexPtr()[i];
m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
}
if(!matrix.isCompressed()) delete[] outerIndexPtr;
}
else
{ //FIXME This should not be needed if the empty permutation is handled transparently
@ -453,6 +532,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
m_perm_r.resize(m);
m_perm_r.indices().setConstant(-1);
marker.setConstant(-1);
m_detPermR = 1; // Record the determinant of the row permutation
m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0);
m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
@ -540,6 +620,9 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
return;
}
// Update the determinant of the row permutation matrix
if (pivrow != jj) m_detPermR *= -1;
// Prune columns (0:jj-1) using column jj
Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu);
@ -568,7 +651,7 @@ void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
}
template<typename MappedSupernodalType>
struct SparseLUMatrixLReturnType
struct SparseLUMatrixLReturnType : internal::no_assignment_operator
{
typedef typename MappedSupernodalType::Index Index;
typedef typename MappedSupernodalType::Scalar Scalar;
@ -584,6 +667,61 @@ struct SparseLUMatrixLReturnType
const MappedSupernodalType& m_mapL;
};
template<typename MatrixLType, typename MatrixUType>
struct SparseLUMatrixUReturnType : internal::no_assignment_operator
{
typedef typename MatrixLType::Index Index;
typedef typename MatrixLType::Scalar Scalar;
SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU)
: m_mapL(mapL),m_mapU(mapU)
{ }
Index rows() { return m_mapL.rows(); }
Index cols() { return m_mapL.cols(); }
template<typename Dest> void solveInPlace(MatrixBase<Dest> &X) const
{
Index nrhs = X.cols();
Index n = X.rows();
// Backward solve with U
for (Index k = m_mapL.nsuper(); k >= 0; k--)
{
Index fsupc = m_mapL.supToCol()[k];
Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
Index luptr = m_mapL.colIndexPtr()[fsupc];
if (nsupc == 1)
{
for (Index j = 0; j < nrhs; j++)
{
X(fsupc, j) /= m_mapL.valuePtr()[luptr];
}
}
else
{
Map<const Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
Map< Matrix<Scalar,Dynamic,Dynamic>, 0, OuterStride<> > U (&(X(fsupc,0)), nsupc, nrhs, OuterStride<>(n) );
U = A.template triangularView<Upper>().solve(U);
}
for (Index j = 0; j < nrhs; ++j)
{
for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
{
typename MatrixUType::InnerIterator it(m_mapU, jcol);
for ( ; it; ++it)
{
Index irow = it.index();
X(irow, j) -= X(jcol, j) * it.value();
}
}
}
} // End For U-solve
}
const MatrixLType& m_mapL;
const MatrixUType& m_mapU;
};
namespace internal {
template<typename _MatrixType, typename Derived, typename Rhs>

6
Eigen/src/SparseLU/SparseLU_Memory.h
View File

@ -70,7 +70,7 @@ Index SparseLUImpl<Scalar,Index>::expand(VectorType& vec, Index& length, Index
if(num_expansions == 0 || keep_prev)
new_len = length ; // First time allocate requested
else
new_len = alpha * length ;
new_len = Index(alpha * length);
VectorType old_vec; // Temporary vector to hold the previous values
if (nbElts > 0 )
@ -100,7 +100,7 @@ Index SparseLUImpl<Scalar,Index>::expand(VectorType& vec, Index& length, Index
do
{
alpha = (alpha + 1)/2;
new_len = alpha * length ;
new_len = Index(alpha * length);
try
{
vec.resize(new_len);
@ -141,7 +141,7 @@ Index SparseLUImpl<Scalar,Index>::memInit(Index m, Index n, Index annz, Index lw
Index& num_expansions = glu.num_expansions; //No memory expansions so far
num_expansions = 0;
glu.nzumax = glu.nzlumax = (std::max)(fillratio * annz, m*n); // estimated number of nonzeros in U
glu.nzlmax = (std::max)(1., fillratio/4.) * annz; // estimated nnz in L factor
glu.nzlmax = (std::max)(Index(4), fillratio) * annz / 4; // estimated nnz in L factor
// Return the estimated size to the user if necessary
Index tempSpace;

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