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Updated Eigen to the latest version.

This commit is contained in:
Paolo Cignoni 2013-02-27 21:07:14 +00:00
parent d3d50e6858
commit 17f61b2774
30 changed files with 172 additions and 118 deletions
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14
eigenlib/Eigen/src/Core/ArrayWrapper.h
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@ -121,6 +121,13 @@ class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
protected:
NestedExpressionType m_expression;
};
@ -231,6 +238,13 @@ class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
return m_expression;
}
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index) */
void resize(Index newSize) { m_expression.const_cast_derived().resize(newSize); }
/** Forwards the resizing request to the nested expression
* \sa DenseBase::resize(Index,Index)*/
void resize(Index nbRows, Index nbCols) { m_expression.const_cast_derived().resize(nbRows,nbCols); }
protected:
NestedExpressionType m_expression;
};

2
eigenlib/Eigen/src/Core/CommaInitializer.h
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@ -65,6 +65,8 @@ struct CommaInitializer
template<typename OtherDerived>
CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
{
if(other.cols()==0 || other.rows()==0)
return *this;
if (m_col==m_xpr.cols())
{
m_row+=m_currentBlockRows;

12
eigenlib/Eigen/src/Core/DiagonalMatrix.h
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@ -20,6 +20,7 @@ class DiagonalBase : public EigenBase<Derived>
public:
typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
typedef typename DiagonalVectorType::Scalar Scalar;
typedef typename DiagonalVectorType::RealScalar RealScalar;
typedef typename internal::traits<Derived>::StorageKind StorageKind;
typedef typename internal::traits<Derived>::Index Index;
@ -65,6 +66,17 @@ class DiagonalBase : public EigenBase<Derived>
return diagonal().cwiseInverse();
}
inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
operator*(const Scalar& scalar) const
{
return diagonal() * scalar;
}
friend inline const DiagonalWrapper<const CwiseUnaryOp<internal::scalar_multiple_op<Scalar>, const DiagonalVectorType> >
operator*(const Scalar& scalar, const DiagonalBase& other)
{
return other.diagonal() * scalar;
}
#ifdef EIGEN2_SUPPORT
template<typename OtherDerived>
bool isApprox(const DiagonalBase<OtherDerived>& other, typename NumTraits<Scalar>::Real precision = NumTraits<Scalar>::dummy_precision()) const

44
eigenlib/Eigen/src/Core/Functors.h
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@ -447,7 +447,7 @@ struct functor_traits<scalar_log_op<Scalar> >
* indeed it seems better to declare m_other as a Packet and do the pset1() once
* in the constructor. However, in practice:
* - GCC does not like m_other as a Packet and generate a load every time it needs it
* - on the other hand GCC is able to moves the pset1() away the loop :)
* - on the other hand GCC is able to moves the pset1() outside the loop :)
* - simpler code ;)
* (ICC and gcc 4.4 seems to perform well in both cases, the issue is visible with y = a*x + b*y)
*/
@ -478,33 +478,6 @@ template<typename Scalar1,typename Scalar2>
struct functor_traits<scalar_multiple2_op<Scalar1,Scalar2> >
{ enum { Cost = NumTraits<Scalar1>::MulCost, PacketAccess = false }; };
template<typename Scalar, bool IsInteger>
struct scalar_quotient1_impl {
typedef typename packet_traits<Scalar>::type Packet;
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(static_cast<Scalar>(1) / other) {}
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a * m_other; }
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pmul(a, pset1<Packet>(m_other)); }
const Scalar m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_impl<Scalar,false> >
{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
template<typename Scalar>
struct scalar_quotient1_impl<Scalar,true> {
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_quotient1_impl(const scalar_quotient1_impl& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_quotient1_impl(const Scalar& other) : m_other(other) {}
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_impl<Scalar,true> >
{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = false }; };
/** \internal
* \brief Template functor to divide a scalar by a fixed other one
*
@ -514,14 +487,19 @@ struct functor_traits<scalar_quotient1_impl<Scalar,true> >
* \sa class CwiseUnaryOp, MatrixBase::operator/
*/
template<typename Scalar>
struct scalar_quotient1_op : scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger > {
EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other)
: scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger >(other) {}
struct scalar_quotient1_op {
typedef typename packet_traits<Scalar>::type Packet;
// FIXME default copy constructors seems bugged with std::complex<>
EIGEN_STRONG_INLINE scalar_quotient1_op(const scalar_quotient1_op& other) : m_other(other.m_other) { }
EIGEN_STRONG_INLINE scalar_quotient1_op(const Scalar& other) : m_other(other) {}
EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const { return a / m_other; }
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
{ return internal::pdiv(a, pset1<Packet>(m_other)); }
typename add_const_on_value_type<typename NumTraits<Scalar>::Nested>::type m_other;
};
template<typename Scalar>
struct functor_traits<scalar_quotient1_op<Scalar> >
: functor_traits<scalar_quotient1_impl<Scalar, NumTraits<Scalar>::IsInteger> >
{};
{ enum { Cost = 2 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasDiv }; };
// nullary functors

2
eigenlib/Eigen/src/Core/MatrixBase.h
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@ -237,7 +237,7 @@ template<typename Derived> class MatrixBase
// huuuge hack. make Eigen2's matrix.part<Diagonal>() work in eigen3. Problem: Diagonal is now a class template instead
// of an integer constant. Solution: overload the part() method template wrt template parameters list.
template<template<typename T, int n> class U>
template<template<typename T, int N> class U>
const DiagonalWrapper<ConstDiagonalReturnType> part() const
{ return diagonal().asDiagonal(); }
#endif // EIGEN2_SUPPORT

1
eigenlib/Eigen/src/Core/StableNorm.h
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@ -131,7 +131,6 @@ MatrixBase<Derived>::blueNorm() const
abig = internal::sqrt(abig);
if(abig > overfl)
{
eigen_assert(false && "overflow");
return rbig;
}
if(amed > RealScalar(0))

3
eigenlib/Eigen/src/Core/TriangularMatrix.h
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@ -511,6 +511,7 @@ template<typename Derived1, typename Derived2, bool ClearOpposite>
struct triangular_assignment_selector<Derived1, Derived2, StrictlyUpper, Dynamic, ClearOpposite>
{
typedef typename Derived1::Index Index;
typedef typename Derived1::Scalar Scalar;
static inline void run(Derived1 &dst, const Derived2 &src)
{
for(Index j = 0; j < dst.cols(); ++j)
@ -520,7 +521,7 @@ struct triangular_assignment_selector<Derived1, Derived2, StrictlyUpper, Dynamic
dst.copyCoeff(i, j, src);
if (ClearOpposite)
for(Index i = maxi; i < dst.rows(); ++i)
dst.coeffRef(i, j) = 0;
dst.coeffRef(i, j) = Scalar(0);
}
}
};

22
eigenlib/Eigen/src/Core/products/GeneralMatrixVector.h
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@ -81,14 +81,13 @@ EIGEN_DONT_INLINE static void run(
const Index peels = 2;
const Index LhsPacketAlignedMask = LhsPacketSize-1;
const Index ResPacketAlignedMask = ResPacketSize-1;
const Index PeelAlignedMask = ResPacketSize*peels-1;
const Index size = rows;
// How many coeffs of the result do we have to skip to be aligned.
// Here we assume data are at least aligned on the base scalar type.
Index alignedStart = internal::first_aligned(res,size);
Index alignedSize = ResPacketSize>1 ? alignedStart + ((size-alignedStart) & ~ResPacketAlignedMask) : 0;
const Index peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart;
const Index peeledSize = alignedSize - RhsPacketSize*peels - RhsPacketSize + 1;
const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0;
Index alignmentPattern = alignmentStep==0 ? AllAligned
@ -177,6 +176,8 @@ EIGEN_DONT_INLINE static void run(
_EIGEN_ACCUMULATE_PACKETS(d,du,d);
break;
case FirstAligned:
{
Index j = alignedStart;
if(peels>1)
{
LhsPacket A00, A01, A02, A03, A10, A11, A12, A13;
@ -186,7 +187,7 @@ EIGEN_DONT_INLINE static void run(
A02 = pload<LhsPacket>(&lhs2[alignedStart-2]);
A03 = pload<LhsPacket>(&lhs3[alignedStart-3]);
for (Index j = alignedStart; j<peeledSize; j+=peels*ResPacketSize)
for (; j<peeledSize; j+=peels*ResPacketSize)
{
A11 = pload<LhsPacket>(&lhs1[j-1+LhsPacketSize]); palign<1>(A01,A11);
A12 = pload<LhsPacket>(&lhs2[j-2+LhsPacketSize]); palign<2>(A02,A12);
@ -210,9 +211,10 @@ EIGEN_DONT_INLINE static void run(
pstore(&res[j+ResPacketSize],T1);
}
}
for (Index j = peeledSize; j<alignedSize; j+=ResPacketSize)
for (; j<alignedSize; j+=ResPacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,du);
break;
}
default:
for (Index j = alignedStart; j<alignedSize; j+=ResPacketSize)
_EIGEN_ACCUMULATE_PACKETS(du,du,du);
@ -332,7 +334,6 @@ EIGEN_DONT_INLINE static void run(
const Index peels = 2;
const Index RhsPacketAlignedMask = RhsPacketSize-1;
const Index LhsPacketAlignedMask = LhsPacketSize-1;
const Index PeelAlignedMask = RhsPacketSize*peels-1;
const Index depth = cols;
// How many coeffs of the result do we have to skip to be aligned.
@ -340,7 +341,7 @@ EIGEN_DONT_INLINE static void run(
// if that's not the case then vectorization is discarded, see below.
Index alignedStart = internal::first_aligned(rhs, depth);
Index alignedSize = RhsPacketSize>1 ? alignedStart + ((depth-alignedStart) & ~RhsPacketAlignedMask) : 0;
const Index peeledSize = peels>1 ? alignedStart + ((alignedSize-alignedStart) & ~PeelAlignedMask) : alignedStart;
const Index peeledSize = alignedSize - RhsPacketSize*peels - RhsPacketSize + 1;
const Index alignmentStep = LhsPacketSize>1 ? (LhsPacketSize - lhsStride % LhsPacketSize) & LhsPacketAlignedMask : 0;
Index alignmentPattern = alignmentStep==0 ? AllAligned
@ -430,10 +431,12 @@ EIGEN_DONT_INLINE static void run(
_EIGEN_ACCUMULATE_PACKETS(d,du,d);
break;
case FirstAligned:
{
Index j = alignedStart;
if (peels>1)
{
/* Here we proccess 4 rows with with two peeled iterations to hide
* tghe overhead of unaligned loads. Moreover unaligned loads are handled
* the overhead of unaligned loads. Moreover unaligned loads are handled
* using special shift/move operations between the two aligned packets
* overlaping the desired unaligned packet. This is *much* more efficient
* than basic unaligned loads.
@ -443,7 +446,7 @@ EIGEN_DONT_INLINE static void run(
A02 = pload<LhsPacket>(&lhs2[alignedStart-2]);
A03 = pload<LhsPacket>(&lhs3[alignedStart-3]);
for (Index j = alignedStart; j<peeledSize; j+=peels*RhsPacketSize)
for (; j<peeledSize; j+=peels*RhsPacketSize)
{
RhsPacket b = pload<RhsPacket>(&rhs[j]);
A11 = pload<LhsPacket>(&lhs1[j-1+LhsPacketSize]); palign<1>(A01,A11);
@ -465,9 +468,10 @@ EIGEN_DONT_INLINE static void run(
ptmp3 = pcj.pmadd(A13, b, ptmp3);
}
}
for (Index j = peeledSize; j<alignedSize; j+=RhsPacketSize)
for (; j<alignedSize; j+=RhsPacketSize)
_EIGEN_ACCUMULATE_PACKETS(d,du,du);
break;
}
default:
for (Index j = alignedStart; j<alignedSize; j+=RhsPacketSize)
_EIGEN_ACCUMULATE_PACKETS(du,du,du);

20
eigenlib/Eigen/src/Core/products/TriangularMatrixMatrix_MKL.h
View File

@ -57,11 +57,11 @@ template <typename Index, int Mode, \
struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \
LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,Specialized> { \
static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\
const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha) { \
const Scalar* _rhs, Index rhsStride, Scalar* res, Index resStride, Scalar alpha, level3_blocking<Scalar,Scalar>& blocking) { \
product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \
LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \
RhsStorageOrder, ConjugateRhs, ColMajor>::run( \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
} \
};
@ -96,7 +96,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
EIGTYPE* res, Index resStride, \
EIGTYPE alpha) \
EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
{ \
Index diagSize = (std::min)(_rows,_depth); \
Index rows = IsLower ? _rows : diagSize; \
@ -115,16 +115,16 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
/* Most likely no benefit to call TRMM or GEMM from MKL*/ \
product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \
LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
/*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \
} else { \
/* Make sense to call GEMM */ \
Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \
MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \
MKL_INT aStride = aa_tmp.outerStride(); \
gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> blocking(_rows,_cols,_depth); \
gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth); \
general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \
rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, resStride, alpha, blocking, 0); \
rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, resStride, alpha, gemm_blocking, 0); \
\
/*std::cout << "TRMM_L: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \
} \
@ -210,7 +210,7 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsStride, \
EIGTYPE* res, Index resStride, \
EIGTYPE alpha) \
EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
{ \
Index diagSize = (std::min)(_cols,_depth); \
Index rows = _rows; \
@ -229,16 +229,16 @@ struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
/* Most likely no benefit to call TRMM or GEMM from MKL*/ \
product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \
LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, BuiltIn>::run( \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha); \
_rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
/*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \
} else { \
/* Make sense to call GEMM */ \
Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \
MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \
MKL_INT aStride = aa_tmp.outerStride(); \
gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> blocking(_rows,_cols,_depth); \
gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth); \
general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor>::run( \
rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, resStride, alpha, blocking, 0); \
rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, resStride, alpha, gemm_blocking, 0); \
\
/*std::cout << "TRMM_R: A is not square! Go to MKL GEMM implementation! " << nthr<<" \n";*/ \
} \

8
eigenlib/Eigen/src/Core/products/TriangularMatrixVector_MKL.h
View File

@ -82,11 +82,11 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
LowUp = IsLower ? Lower : Upper \
}; \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
{ \
if (ConjLhs || IsZeroDiag) { \
triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha, blocking); \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
return; \
}\
Index size = (std::min)(_rows,_cols); \
@ -167,11 +167,11 @@ struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,
LowUp = IsLower ? Lower : Upper \
}; \
static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
{ \
if (IsZeroDiag) { \
triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha, blocking); \
_rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
return; \
}\
Index size = (std::min)(_rows,_cols); \

2
eigenlib/Eigen/src/Core/util/Macros.h
View File

@ -13,7 +13,7 @@
#define EIGEN_WORLD_VERSION 3
#define EIGEN_MAJOR_VERSION 1
#define EIGEN_MINOR_VERSION 1
#define EIGEN_MINOR_VERSION 2
#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
(EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \

4
eigenlib/Eigen/src/Core/util/Memory.h
View File

@ -204,7 +204,7 @@ 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)
#elif defined(_MSC_VER) && (!defined(_WIN32_WCE))
result = _aligned_malloc(size, 16);
#else
result = handmade_aligned_malloc(size);
@ -745,7 +745,7 @@ public:
__asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id) );
# endif
# elif defined(_MSC_VER)
# if (_MSC_VER > 1500)
# if (_MSC_VER > 1500) && ( defined(_M_IX86) || defined(_M_X64) )
# define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id)
# endif
# endif

4
eigenlib/Eigen/src/Core/util/XprHelper.h
View File

@ -301,9 +301,9 @@ template<typename T, int n=1, typename PlainObject = typename eval<T>::type> str
// it's important that this value can still be squared without integer overflowing.
DynamicAsInteger = 10000,
ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
ScalarReadCostAsInteger = ScalarReadCost == Dynamic ? DynamicAsInteger : ScalarReadCost,
ScalarReadCostAsInteger = ScalarReadCost == Dynamic ? int(DynamicAsInteger) : int(ScalarReadCost),
CoeffReadCost = traits<T>::CoeffReadCost,
CoeffReadCostAsInteger = CoeffReadCost == Dynamic ? DynamicAsInteger : CoeffReadCost,
CoeffReadCostAsInteger = CoeffReadCost == Dynamic ? int(DynamicAsInteger) : int(CoeffReadCost),
NAsInteger = n == Dynamic ? int(DynamicAsInteger) : n,
CostEvalAsInteger = (NAsInteger+1) * ScalarReadCostAsInteger + CoeffReadCostAsInteger,
CostNoEvalAsInteger = NAsInteger * CoeffReadCostAsInteger

8
eigenlib/Eigen/src/Eigenvalues/ComplexSchur.h
View File

@ -336,6 +336,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
Index iu = m_matT.cols() - 1;
Index il;
Index iter = 0; // number of iterations we are working on the (iu,iu) element
Index totalIter = 0; // number of iterations for whole matrix
while(true)
{
@ -350,9 +351,10 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
// if iu is zero then we are done; the whole matrix is triangularized
if(iu==0) break;
// if we spent too many iterations on the current element, we give up
// if we spent too many iterations, we give up
iter++;
if(iter > m_maxIterations * m_matT.cols()) break;
totalIter++;
if(totalIter > m_maxIterations * m_matT.cols()) break;
// find il, the top row of the active submatrix
il = iu-1;
@ -382,7 +384,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
}
}
if(iter <= m_maxIterations * m_matT.cols())
if(totalIter <= m_maxIterations * m_matT.cols())
m_info = Success;
else
m_info = NoConvergence;

2
eigenlib/Eigen/src/Eigenvalues/ComplexSchur_MKL.h
View File

@ -40,7 +40,7 @@ namespace Eigen {
/** \internal Specialization for the data types supported by MKL */
#define EIGEN_MKL_SCHUR_COMPLEX(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \
template<> inline\
template<> inline \
ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \
{ \

10
eigenlib/Eigen/src/Eigenvalues/RealSchur.h
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@ -220,8 +220,9 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix,
// Rows il,...,iu is the part we are working on (the active window).
// Rows iu+1,...,end are already brought in triangular form.
Index iu = m_matT.cols() - 1;
Index iter = 0; // iteration count
Scalar exshift(0); // sum of exceptional shifts
Index iter = 0; // iteration count for current eigenvalue
Index totalIter = 0; // iteration count for whole matrix
Scalar exshift(0); // sum of exceptional shifts
Scalar norm = computeNormOfT();
if(norm!=0)
@ -251,14 +252,15 @@ RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const MatrixType& matrix,
Vector3s firstHouseholderVector(0,0,0), shiftInfo;
computeShift(iu, iter, exshift, shiftInfo);
iter = iter + 1;
if (iter > m_maxIterations * m_matT.cols()) break;
totalIter = totalIter + 1;
if (totalIter > m_maxIterations * matrix.cols()) break;
Index im;
initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
}
}
}
if(iter <= m_maxIterations * m_matT.cols())
if(totalIter <= m_maxIterations * matrix.cols())
m_info = Success;
else
m_info = NoConvergence;

11
eigenlib/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
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@ -743,7 +743,16 @@ static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index sta
// RealScalar e2 = 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] - (e / (td + (td>0 ? 1 : -1))) * (e / hypot(td,e));
RealScalar mu = diag[end];
if(td==0)
mu -= abs(e);
else
{
RealScalar e2 = abs2(subdiag[end-1]);
RealScalar h = hypot(td,e);
if(e2==0) mu -= (e / (td + (td>0 ? 1 : -1))) * (e / h);
else mu -= e2 / (td + (td>0 ? h : -h));
}
RealScalar x = diag[start] - mu;
RealScalar z = subdiag[start];

2
eigenlib/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_MKL.h
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@ -40,7 +40,7 @@ namespace Eigen {
/** \internal Specialization for the data types supported by MKL */
#define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW ) \
template<> inline\
template<> inline \
SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, int options) \
{ \

2
eigenlib/Eigen/src/Geometry/AlignedBox.h
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@ -79,7 +79,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
~AlignedBox() {}
/** \returns the dimension in which the box holds */
inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size()-1 : Index(AmbientDimAtCompileTime); }
inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime); }
/** \deprecated use isEmpty */
inline bool isNull() const { return isEmpty(); }

5
eigenlib/Eigen/src/PardisoSupport/PardisoSupport.h
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@ -108,6 +108,7 @@ class PardisoImpl
typedef Matrix<Scalar,Dynamic,1> VectorType;
typedef Matrix<Index, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
typedef Matrix<Index, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
typedef Array<Index,64,1,DontAlign> ParameterType;
enum {
ScalarIsComplex = NumTraits<Scalar>::IsComplex
};
@ -142,7 +143,7 @@ class PardisoImpl
/** \warning for advanced usage only.
* \returns a reference to the parameter array controlling PARDISO.
* See the PARDISO manual to know how to use it. */
Array<Index,64,1>& pardisoParameterArray()
ParameterType& pardisoParameterArray()
{
return m_iparm;
}
@ -295,7 +296,7 @@ class PardisoImpl
bool m_initialized, m_analysisIsOk, m_factorizationIsOk;
Index m_type, m_msglvl;
mutable void *m_pt[64];
mutable Array<Index,64,1> m_iparm;
mutable ParameterType m_iparm;
mutable IntColVectorType m_perm;
Index m_size;

2
eigenlib/Eigen/src/QR/ColPivHouseholderQR_MKL.h
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@ -41,7 +41,7 @@ namespace Eigen {
/** \internal Specialization for the data types supported by MKL */
#define EIGEN_MKL_QR_COLPIV(EIGTYPE, MKLTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
template<> inline\
template<> inline \
ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >& \
ColPivHouseholderQR<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> >::compute( \
const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix) \

2
eigenlib/Eigen/src/SVD/JacobiSVD_MKL.h
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@ -40,7 +40,7 @@ namespace Eigen {
/** \internal Specialization for the data types supported by MKL */
#define EIGEN_MKL_SVD(EIGTYPE, MKLTYPE, MKLRTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
template<> inline\
template<> inline \
JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
{ \

12
eigenlib/Eigen/src/SparseCore/SparseDiagonalProduct.h
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@ -126,11 +126,15 @@ class sparse_diagonal_product_inner_iterator_selector
SparseInnerVectorSet<Rhs,1>,
typename Lhs::DiagonalVectorType>::InnerIterator Base;
typedef typename Lhs::Index Index;
Index m_outer;
public:
inline sparse_diagonal_product_inner_iterator_selector(
const SparseDiagonalProductType& expr, Index outer)
: Base(expr.rhs().innerVector(outer) .cwiseProduct(expr.lhs().diagonal()), 0)
: Base(expr.rhs().innerVector(outer) .cwiseProduct(expr.lhs().diagonal()), 0), m_outer(outer)
{}
inline Index outer() const { return m_outer; }
inline Index col() const { return m_outer; }
};
template<typename Lhs, typename Rhs, typename SparseDiagonalProductType>
@ -160,11 +164,15 @@ class sparse_diagonal_product_inner_iterator_selector
SparseInnerVectorSet<Lhs,1>,
Transpose<const typename Rhs::DiagonalVectorType> >::InnerIterator Base;
typedef typename Lhs::Index Index;
Index m_outer;
public:
inline sparse_diagonal_product_inner_iterator_selector(
const SparseDiagonalProductType& expr, Index outer)
: Base(expr.lhs().innerVector(outer) .cwiseProduct(expr.rhs().diagonal().transpose()), 0)
: Base(expr.lhs().innerVector(outer) .cwiseProduct(expr.rhs().diagonal().transpose()), 0), m_outer(outer)
{}
inline Index outer() const { return m_outer; }
inline Index row() const { return m_outer; }
};
} // end namespace internal

42
eigenlib/Eigen/src/SparseCore/SparseMatrix.h
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@ -572,6 +572,16 @@ class SparseMatrix
*this = other.derived();
}
/** \brief Copy constructor with in-place evaluation */
template<typename OtherDerived>
SparseMatrix(const ReturnByValue<OtherDerived>& other)
: Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
{
check_template_parameters();
initAssignment(other);
other.evalTo(*this);
}
/** Swaps the content of two sparse matrices of the same type.
* This is a fast operation that simply swaps the underlying pointers and parameters. */
inline void swap(SparseMatrix& other)
@ -613,7 +623,10 @@ class SparseMatrix
template<typename OtherDerived>
inline SparseMatrix& operator=(const ReturnByValue<OtherDerived>& other)
{ return Base::operator=(other.derived()); }
{
initAssignment(other);
return Base::operator=(other.derived());
}
template<typename OtherDerived>
inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
@ -623,7 +636,6 @@ class SparseMatrix
template<typename OtherDerived>
EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other)
{
initAssignment(other.derived());
const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
if (needToTranspose)
{
@ -635,40 +647,45 @@ class SparseMatrix
typedef typename internal::remove_all<OtherCopy>::type _OtherCopy;
OtherCopy otherCopy(other.derived());
Eigen::Map<Matrix<Index, Dynamic, 1> > (m_outerIndex,outerSize()).setZero();
SparseMatrix dest(other.rows(),other.cols());
Eigen::Map<Matrix<Index, Dynamic, 1> > (dest.m_outerIndex,dest.outerSize()).setZero();
// pass 1
// FIXME the above copy could be merged with that pass
for (Index j=0; j<otherCopy.outerSize(); ++j)
for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
++m_outerIndex[it.index()];
++dest.m_outerIndex[it.index()];
// prefix sum
Index count = 0;
VectorXi positions(outerSize());
for (Index j=0; j<outerSize(); ++j)
VectorXi positions(dest.outerSize());
for (Index j=0; j<dest.outerSize(); ++j)
{
Index tmp = m_outerIndex[j];
m_outerIndex[j] = count;
Index tmp = dest.m_outerIndex[j];
dest.m_outerIndex[j] = count;
positions[j] = count;
count += tmp;
}
m_outerIndex[outerSize()] = count;
dest.m_outerIndex[dest.outerSize()] = count;
// alloc
m_data.resize(count);
dest.m_data.resize(count);
// pass 2
for (Index j=0; j<otherCopy.outerSize(); ++j)
{
for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
{
Index pos = positions[it.index()]++;
m_data.index(pos) = j;
m_data.value(pos) = it.value();
dest.m_data.index(pos) = j;
dest.m_data.value(pos) = it.value();
}
}
this->swap(dest);
return *this;
}
else
{
if(other.isRValue())
initAssignment(other.derived());
// there is no special optimization
return Base::operator=(other.derived());
}
@ -888,6 +905,7 @@ protected:
m_data.value(p) = m_data.value(p-1);
--p;
}
eigen_assert((p<=startId || m_data.index(p-1)!=inner) && "you cannot insert an element that already exist, you must call coeffRef to this end");
m_innerNonZeros[outer]++;

5
eigenlib/Eigen/src/SparseCore/SparseUtil.h
View File

@ -113,9 +113,10 @@ template<typename T,int Rows> struct sparse_eval<T,Rows,1> {
template<typename T,int Rows,int Cols> struct sparse_eval {
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;
};
template<typename T> struct sparse_eval<T,1,1> {

6
eigenlib/Eigen/src/SuperLUSupport/SuperLUSupport.h
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@ -496,8 +496,8 @@ class SuperLU : public SuperLUBase<_MatrixType,SuperLU<_MatrixType> >
SuperLU(const MatrixType& matrix) : Base()
{
Base::init();
compute(matrix);
init();
Base::compute(matrix);
}
~SuperLU()
@ -833,7 +833,7 @@ class SuperILU : public SuperLUBase<_MatrixType,SuperILU<_MatrixType> >
SuperILU(const MatrixType& matrix) : Base()
{
init();
compute(matrix);
Base::compute(matrix);
}
~SuperILU()

6
eigenlib/Eigen/src/plugins/ArrayCwiseBinaryOps.h
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@ -33,7 +33,8 @@ EIGEN_MAKE_CWISE_BINARY_OP(min,internal::scalar_min_op)
*
* \sa max()
*/
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived, const ConstantReturnType>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
(min)(const Scalar &other) const
{
return (min)(Derived::PlainObject::Constant(rows(), cols(), other));
@ -52,7 +53,8 @@ EIGEN_MAKE_CWISE_BINARY_OP(max,internal::scalar_max_op)
*
* \sa min()
*/
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived, const ConstantReturnType>
EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar>, const Derived,
const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
(max)(const Scalar &other) const
{
return (max)(Derived::PlainObject::Constant(rows(), cols(), other));

1
eigenlib/Eigen/src/plugins/ArrayCwiseUnaryOps.h
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@ -200,3 +200,4 @@ EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator<=, std::less_equal)
EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator>, std::greater)
EIGEN_MAKE_SCALAR_CWISE_UNARY_OP(operator>=, std::greater_equal)

2
eigenlib/howto.txt
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@ -1,4 +1,4 @@
Current Eigen Version 3.1.1 (05.Oct.2012)
Current Eigen Version 3.1.2 (05.11.2012)
To update the lib:
- download Eigen
- unzip it somewhere

34
eigenlib/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
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@ -195,24 +195,24 @@ template <typename MatrixType>
int MatrixLogarithmAtomic<MatrixType>::getPadeDegree(long double normTminusI)
{
#if LDBL_MANT_DIG == 53 // double precision
const double maxNormForPade[] = { 1.6206284795015624e-2 /* degree = 3 */ , 5.3873532631381171e-2,
1.1352802267628681e-1, 1.8662860613541288e-1, 2.642960831111435e-1 };
const long double maxNormForPade[] = { 1.6206284795015624e-2L /* degree = 3 */ , 5.3873532631381171e-2L,
1.1352802267628681e-1L, 1.8662860613541288e-1L, 2.642960831111435e-1L };
#elif LDBL_MANT_DIG <= 64 // extended precision
const double maxNormForPade[] = { 5.48256690357782863103e-3 /* degree = 3 */, 2.34559162387971167321e-2,
5.84603923897347449857e-2, 1.08486423756725170223e-1, 1.68385767881294446649e-1,
2.32777776523703892094e-1 };
const long double maxNormForPade[] = { 5.48256690357782863103e-3L /* degree = 3 */, 2.34559162387971167321e-2L,
5.84603923897347449857e-2L, 1.08486423756725170223e-1L, 1.68385767881294446649e-1L,
2.32777776523703892094e-1L };
#elif LDBL_MANT_DIG <= 106 // double-double
const double maxNormForPade[] = { 8.58970550342939562202529664318890e-5 /* degree = 3 */,
9.34074328446359654039446552677759e-4, 4.26117194647672175773064114582860e-3,
1.21546224740281848743149666560464e-2, 2.61100544998339436713088248557444e-2,
4.66170074627052749243018566390567e-2, 7.32585144444135027565872014932387e-2,
1.05026503471351080481093652651105e-1 };
const long double maxNormForPade[] = { 8.58970550342939562202529664318890e-5L /* degree = 3 */,
9.34074328446359654039446552677759e-4L, 4.26117194647672175773064114582860e-3L,
1.21546224740281848743149666560464e-2L, 2.61100544998339436713088248557444e-2L,
4.66170074627052749243018566390567e-2L, 7.32585144444135027565872014932387e-2L,
1.05026503471351080481093652651105e-1L };
#else // quadruple precision
const double maxNormForPade[] = { 4.7419931187193005048501568167858103e-5 /* degree = 3 */,
5.8853168473544560470387769480192666e-4, 2.9216120366601315391789493628113520e-3,
8.8415758124319434347116734705174308e-3, 1.9850836029449446668518049562565291e-2,
3.6688019729653446926585242192447447e-2, 5.9290962294020186998954055264528393e-2,
8.6998436081634343903250580992127677e-2, 1.1880960220216759245467951592883642e-1 };
const long double maxNormForPade[] = { 4.7419931187193005048501568167858103e-5L /* degree = 3 */,
5.8853168473544560470387769480192666e-4L, 2.9216120366601315391789493628113520e-3L,
8.8415758124319434347116734705174308e-3L, 1.9850836029449446668518049562565291e-2L,
3.6688019729653446926585242192447447e-2L, 5.9290962294020186998954055264528393e-2L,
8.6998436081634343903250580992127677e-2L, 1.1880960220216759245467951592883642e-1L };
#endif
for (int degree = 3; degree <= maxPadeDegree; ++degree)
if (normTminusI <= maxNormForPade[degree - minPadeDegree])
@ -423,8 +423,8 @@ void MatrixLogarithmAtomic<MatrixType>::computePade11(MatrixType& result, const
* This class holds the argument to the matrix function until it is
* assigned or evaluated for some other reason (so the argument
* should not be changed in the meantime). It is the return type of
* matrixBase::matrixLogarithm() and most of the time this is the
* only way it is used.
* matrixBase::log() and most of the time this is the only way it
* is used.
*/
template<typename Derived> class MatrixLogarithmReturnValue
: public ReturnByValue<MatrixLogarithmReturnValue<Derived> >