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Enable saving intermidiate (Schur decomposition) but disable unstable specialization for matrix power-matrix product.
This commit is contained in:
Chen-Pang He
parent
d5d99dd1f0
commit
87afd99433
@ -211,8 +211,9 @@ documentation of \ref matrixbase_exp "exp()".
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\include MatrixLogarithm.cpp
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Output: \verbinclude MatrixLogarithm.out
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\note \p M has to be a matrix of \c float, \c double, \c long double
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\c complex<float>, \c complex<double>, or \c complex<long double> .
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\note \p M has to be a matrix of \c float, \c double, <tt>long
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double</tt>, \c complex<float>, \c complex<double>, or \c complex<long
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double> .
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\sa MatrixBase::exp(), MatrixBase::matrixFunction(),
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class MatrixLogarithmAtomic, MatrixBase::sqrt().
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@ -234,27 +235,14 @@ where exp denotes the matrix exponential, and log denotes the matrix
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logarithm.
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The matrix \f$ M \f$ should meet the conditions to be an argument of
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matrix logarithm. If \p p is neither an integer nor the real scalar
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type of \p M, it is casted into the real scalar type of \p M.
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matrix logarithm. If \p p is not of the real scalar type of \p M, it
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is casted into the real scalar type of \p M.
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This function computes the matrix logarithm using the
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Schur-Padé algorithm as implemented by MatrixBase::pow().
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The exponent is split into integral part and fractional part, where
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the fractional part is in the interval \f$ (-1, 1) \f$. The main
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diagonal and the first super-diagonal is directly computed.
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The actual work is done by the MatrixPower class, which can compute
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\f$ M^p v \f$, where \p v is another matrix with the same rows as
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\p M. The matrix \p v is set to be the identity matrix by default.
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Therefore, the expression <tt>M.pow(p) * v</tt> is specialized for
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this. No temporary storage is created for the result. The code below
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directly evaluates R-values into L-values without aliasing issue. Do
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\b NOT try to \a optimize with noalias(). It won't compile.
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\code
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v = m.pow(p) * v;
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m = m.pow(q);
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// v2.noalias() = m.pow(p) * v1; Won't compile!
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\endcode
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This function computes the matrix power using the Schur-Padé
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algorithm as implemented by class MatrixPower. The exponent is split
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into integral part and fractional part, where the fractional part is
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in the interval \f$ (-1, 1) \f$. The main diagonal and the first
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super-diagonal is directly computed.
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Details of the algorithm can be found in: Nicholas J. Higham and
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Lijing Lin, "A Schur-Padé algorithm for fractional powers of a
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@ -277,8 +265,18 @@ the z-axis.
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\include MatrixPower.cpp
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Output: \verbinclude MatrixPower.out
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\note \p M has to be a matrix of \c float, \c double, \c long double
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\c complex<float>, \c complex<double>, or \c complex<long double> .
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MatrixBase::pow() is user-friendly. However, there are some
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circumstances under which you should use class MatrixPower directly.
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MatrixPower can save the result of Schur decomposition, so it's
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better for computing various powers for the same matrix.
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Example:
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\include MatrixPower_optimal.cpp
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Output: \verbinclude MatrixPower_optimal.out
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\note \p M has to be a matrix of \c float, \c double, <tt>long
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double</tt>, \c complex<float>, \c complex<double>, or \c complex<long
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double> .
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\sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower.
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@ -10,395 +10,69 @@
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#ifndef EIGEN_MATRIX_POWER
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#define EIGEN_MATRIX_POWER
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#ifndef M_PI
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#define M_PI 3.141592653589793238462643383279503L
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#endif
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namespace Eigen {
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/**
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* \ingroup MatrixFunctions_Module
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*
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* \brief Class for computing matrix powers.
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*
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* \tparam MatrixType type of the base, expected to be an instantiation
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* of the Matrix class template.
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* \tparam PlainObject type of the multiplier.
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*/
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template<typename MatrixType, typename PlainObject = MatrixType>
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class MatrixPower
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namespace internal {
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template<int IsComplex>
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struct recompose_complex_schur
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{
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private:
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typedef internal::traits<MatrixType> Traits;
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static const int Rows = Traits::RowsAtCompileTime;
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static const int Cols = Traits::ColsAtCompileTime;
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static const int Options = Traits::Options;
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static const int MaxRows = Traits::MaxRowsAtCompileTime;
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static const int MaxCols = Traits::MaxColsAtCompileTime;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef std::complex<RealScalar> ComplexScalar;
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typedef typename MatrixType::Index Index;
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typedef Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols> ComplexMatrix;
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typedef Array<ComplexScalar, Rows, 1, ColMajor, MaxRows> ComplexArray;
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public:
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/**
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* \brief Constructor.
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*
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* \param[in] A the base of the matrix power.
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* \param[in] p the exponent of the matrix power.
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* \param[in] b the multiplier.
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*/
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MatrixPower(const MatrixType& A, RealScalar p, const PlainObject& b) :
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m_A(A),
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m_p(p),
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m_b(b),
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m_dimA(A.cols()),
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m_dimb(b.cols())
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{ /* empty body */ }
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/**
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* \brief Compute the matrix power.
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*
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* \param[out] result \f$ A^p b \f$, as specified in the constructor.
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*/
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template<typename ResultType> void compute(ResultType& result);
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private:
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/**
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* \brief Compute the matrix to integral power.
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*
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* If \p b is \em fatter than \p A, it computes \f$ A^{p_{\textrm int}}
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* \f$ first, and then multiplies it with \p b. Otherwise,
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* #computeChainProduct optimizes the expression.
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*
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* \sa computeChainProduct(ResultType&);
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*/
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template<typename ResultType>
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void computeIntPower(ResultType& result);
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/**
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* \brief Convert integral power of the matrix into chain product.
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*
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* This optimizes the matrix expression. It automatically chooses binary
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* powering or matrix chain multiplication or solving the linear system
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* repetitively, according to which algorithm costs less.
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*/
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template<typename ResultType>
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void computeChainProduct(ResultType&);
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/** \brief Compute the cost of binary powering. */
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static int computeCost(RealScalar);
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/** \brief Solve the linear system repetitively. */
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template<typename ResultType>
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void partialPivLuSolve(ResultType&, RealScalar);
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/** \brief Compute Schur decomposition of #m_A. */
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void computeSchurDecomposition();
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/**
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* \brief Split #m_p into integral part and fractional part.
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*
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* This method stores the integral part \f$ p_{\textrm int} \f$ into
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* #m_pInt and the fractional part \f$ p_{\textrm frac} \f$ into
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* #m_pFrac, where #m_pFrac is in the interval \f$ (-1,1) \f$. To
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* choose between the possibilities below, it considers the computation
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* of \f$ A^{p_1} \f$ and \f$ A^{p_2} \f$ and determines which of these
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* computations is the better conditioned.
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*/
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void getFractionalExponent();
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/** \brief Compute power of 2x2 triangular matrix. */
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void compute2x2(RealScalar p);
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/**
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* \brief Compute power of triangular matrices with size > 2.
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* \details This uses a Schur-Padé algorithm.
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*/
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void computeBig();
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/** \brief Get suitable degree for Pade approximation. (specialized for RealScalar = double) */
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inline int getPadeDegree(double);
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/** \brief Get suitable degree for Pade approximation. (specialized for RealScalar = float) */
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inline int getPadeDegree(float);
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/** \brief Get suitable degree for Pade approximation. (specialized for RealScalar = long double) */
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inline int getPadeDegree(long double);
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/** \brief Compute Padé approximation to matrix fractional power. */
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void computePade(const int& degree, const ComplexMatrix& IminusT);
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/** \brief Get a certain coefficient of the Padé approximation. */
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inline RealScalar coeff(const int& degree);
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/**
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* \brief Store the fractional power into #m_tmp.
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*
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* This intended for complex matrices.
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*/
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void computeTmp(ComplexScalar);
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/**
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* \brief Store the fractional power into #m_tmp.
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*
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* This is intended for real matrices. It takes the real part of
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* \f$ U T^{p_{\textrm frac}} U^H \f$.
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*
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* \sa computeTmp(ComplexScalar);
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*/
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void computeTmp(RealScalar);
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const MatrixType& m_A; ///< \brief Reference to the matrix base.
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const RealScalar m_p; ///< \brief The real exponent.
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const PlainObject& m_b; ///< \brief Reference to the multiplier.
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const Index m_dimA; ///< \brief The dimension of #m_A, equivalent to %m_A.cols().
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const Index m_dimb; ///< \brief The dimension of #m_b, equivalent to %m_b.cols().
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MatrixType m_tmp; ///< \brief Used for temporary storage.
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RealScalar m_pInt; ///< \brief Integral part of #m_p.
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RealScalar m_pFrac; ///< \brief Fractional part of #m_p.
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ComplexMatrix m_T; ///< \brief Triangular part of Schur decomposition.
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ComplexMatrix m_U; ///< \brief Unitary part of Schur decomposition.
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ComplexMatrix m_fT; ///< \brief #m_T to the power of #m_pFrac.
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ComplexArray m_logTdiag; ///< \brief Logarithm of the main diagonal of #m_T.
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template<typename ResultType, typename MatrixType>
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static inline void run(ResultType& res, const MatrixType& T, const MatrixType& U)
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{ res = U * (T.template triangularView<Upper>() * U.adjoint()); }
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};
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template<typename MatrixType, typename PlainObject>
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template<typename ResultType>
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void MatrixPower<MatrixType,PlainObject>::compute(ResultType& result)
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template<>
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struct recompose_complex_schur<0>
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{
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using std::floor;
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using std::pow;
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template<typename ResultType, typename MatrixType>
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static inline void run(ResultType& res, const MatrixType& T, const MatrixType& U)
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{ res = (U * (T.template triangularView<Upper>() * U.adjoint())).real(); }
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};
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m_pInt = floor(m_p + RealScalar(0.5));
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m_pFrac = m_p - m_pInt;
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if (!m_pFrac) {
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computeIntPower(result);
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} else if (m_dimA == 1)
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result = pow(m_A(0,0), m_p) * m_b;
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else {
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computeSchurDecomposition();
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getFractionalExponent();
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computeIntPower(result);
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if (m_dimA == 2)
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compute2x2(m_pFrac);
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else
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computeBig();
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computeTmp(Scalar());
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result = m_tmp * result;
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}
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}
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template<typename MatrixType, typename PlainObject>
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template<typename ResultType>
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void MatrixPower<MatrixType,PlainObject>::computeIntPower(ResultType& result)
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template<typename T>
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inline int binary_powering_cost(T p)
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{
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MatrixType tmp;
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if (m_dimb > m_dimA) {
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tmp = MatrixType::Identity(m_dimA, m_dimA);
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computeChainProduct(tmp);
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result.noalias() = tmp * m_b;
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} else {
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result = m_b;
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computeChainProduct(result);
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}
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}
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template<typename MatrixType, typename PlainObject>
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template<typename ResultType>
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void MatrixPower<MatrixType,PlainObject>::computeChainProduct(ResultType& result)
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{
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using std::abs;
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using std::fmod;
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using std::ldexp;
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RealScalar p = abs(m_pInt);
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int cost = computeCost(p);
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if (m_pInt < RealScalar(0)) {
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if (p * m_dimb <= cost * m_dimA && m_dimA > 2) {
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partialPivLuSolve(result, p);
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return;
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} else {
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m_tmp = m_A.inverse();
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}
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} else {
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m_tmp = m_A;
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}
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while (p * m_dimb > cost * m_dimA) {
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if (fmod(p, RealScalar(2)) >= RealScalar(1)) {
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result = m_tmp * result;
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cost--;
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}
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m_tmp *= m_tmp;
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cost--;
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p = ldexp(p, -1);
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}
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for (; p >= RealScalar(1); p--)
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result = m_tmp * result;
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}
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template<typename MatrixType, typename PlainObject>
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int MatrixPower<MatrixType,PlainObject>::computeCost(RealScalar p)
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{
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using std::frexp;
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using std::ldexp;
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int cost, tmp;
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frexp(p, &cost);
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while (frexp(p, &tmp), tmp > 0) {
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p -= ldexp(RealScalar(0.5), tmp);
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cost++;
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while (std::frexp(p, &tmp), tmp > 0) {
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p -= std::ldexp(static_cast<T>(0.5), tmp);
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++cost;
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}
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return cost;
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}
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template<typename MatrixType, typename PlainObject>
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template<typename ResultType>
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void MatrixPower<MatrixType,PlainObject>::partialPivLuSolve(ResultType& result, RealScalar p)
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{
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const PartialPivLU<MatrixType> Asolver(m_A);
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for (; p >= RealScalar(1); p--)
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result = Asolver.solve(result);
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}
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template<typename MatrixType, typename PlainObject>
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void MatrixPower<MatrixType,PlainObject>::computeSchurDecomposition()
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{
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const ComplexSchur<MatrixType> schurOfA(m_A);
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m_T = schurOfA.matrixT();
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m_U = schurOfA.matrixU();
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}
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template<typename MatrixType, typename PlainObject>
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void MatrixPower<MatrixType,PlainObject>::getFractionalExponent()
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{
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using std::pow;
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typedef Array<RealScalar, Rows, 1, ColMajor, MaxRows> RealArray;
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const ComplexArray Tdiag = m_T.diagonal();
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const RealArray absTdiag = Tdiag.abs();
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const RealScalar maxAbsEival = absTdiag.maxCoeff();
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const RealScalar minAbsEival = absTdiag.minCoeff();
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m_logTdiag = Tdiag.log();
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if (m_pFrac > RealScalar(0.5) && // This is just a shortcut.
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m_pFrac > (RealScalar(1) - m_pFrac) * pow(maxAbsEival/minAbsEival, m_pFrac)) {
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m_pFrac--;
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m_pInt++;
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}
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}
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template<typename MatrixType, typename PlainObject>
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void MatrixPower<MatrixType,PlainObject>::compute2x2(RealScalar p)
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{
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using std::abs;
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using std::ceil;
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using std::exp;
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using std::imag;
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using std::ldexp;
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using std::pow;
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using std::sinh;
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int i, j, unwindingNumber;
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ComplexScalar w;
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m_fT(0,0) = pow(m_T(0,0), p);
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for (j = 1; j < m_dimA; j++) {
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i = j - 1;
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m_fT(j,j) = pow(m_T(j,j), p);
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if (m_T(i,i) == m_T(j,j)) {
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m_fT(i,j) = p * pow(m_T(i,j), p - RealScalar(1));
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} else if (abs(m_T(i,i)) < ldexp(abs(m_T(j,j)), -1) || abs(m_T(j,j)) < ldexp(abs(m_T(i,i)), -1)) {
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m_fT(i,j) = m_T(i,j) * (m_fT(j,j) - m_fT(i,i)) / (m_T(j,j) - m_T(i,i));
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} else {
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// computation in previous branch is inaccurate if abs(m_T(j,j)) \approx abs(m_T(i,i))
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unwindingNumber = ceil((imag(m_logTdiag[j] - m_logTdiag[i]) - M_PI) / (2 * M_PI));
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w = internal::atanh2(m_T(j,j) - m_T(i,i), m_T(j,j) + m_T(i,i)) + ComplexScalar(0, M_PI * unwindingNumber);
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m_fT(i,j) = m_T(i,j) * RealScalar(2) * exp(RealScalar(0.5) * p * (m_logTdiag[j] + m_logTdiag[i])) *
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sinh(p * w) / (m_T(j,j) - m_T(i,i));
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}
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}
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}
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template<typename MatrixType, typename PlainObject>
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void MatrixPower<MatrixType,PlainObject>::computeBig()
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{
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using std::ldexp;
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const int digits = std::numeric_limits<RealScalar>::digits;
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const RealScalar maxNormForPade = digits <= 24? 4.3386528e-1f: // sigle precision
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digits <= 53? 2.789358995219730e-1: // double precision
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digits <= 64? 2.4471944416607995472e-1L: // extended precision
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digits <= 106? 1.1016843812851143391275867258512e-01: // double-double
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9.134603732914548552537150753385375e-02; // quadruple precision
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int degree, degree2, numberOfSquareRoots = 0, numberOfExtraSquareRoots = 0;
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ComplexMatrix IminusT, sqrtT, T = m_T;
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RealScalar normIminusT;
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while (true) {
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IminusT = ComplexMatrix::Identity(m_dimA, m_dimA) - T;
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normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
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if (normIminusT < maxNormForPade) {
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degree = getPadeDegree(normIminusT);
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degree2 = getPadeDegree(normIminusT * RealScalar(0.5));
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if (degree - degree2 <= 1 || numberOfExtraSquareRoots)
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break;
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numberOfExtraSquareRoots++;
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}
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MatrixSquareRootTriangular<ComplexMatrix>(T).compute(sqrtT);
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T = sqrtT;
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numberOfSquareRoots++;
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}
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computePade(degree, IminusT);
|
||||
|
||||
for (; numberOfSquareRoots; numberOfSquareRoots--) {
|
||||
compute2x2(ldexp(m_pFrac, -numberOfSquareRoots));
|
||||
m_fT *= m_fT;
|
||||
}
|
||||
compute2x2(m_pFrac);
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
inline int MatrixPower<MatrixType,PlainObject>::getPadeDegree(float normIminusT)
|
||||
inline int matrix_power_get_pade_degree(float normIminusT)
|
||||
{
|
||||
const float maxNormForPade[] = { 2.8064004e-1f /* degree = 3 */ , 4.3386528e-1f };
|
||||
int degree = 3;
|
||||
for (; degree <= 4; degree++)
|
||||
for (; degree <= 4; ++degree)
|
||||
if (normIminusT <= maxNormForPade[degree - 3])
|
||||
break;
|
||||
return degree;
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
inline int MatrixPower<MatrixType,PlainObject>::getPadeDegree(double normIminusT)
|
||||
inline int matrix_power_get_pade_degree(double normIminusT)
|
||||
{
|
||||
const double maxNormForPade[] = { 1.884160592658218e-2 /* degree = 3 */ , 6.038881904059573e-2,
|
||||
1.239917516308172e-1, 1.999045567181744e-1, 2.789358995219730e-1 };
|
||||
const double maxNormForPade[] = { 1.884160592658218e-2 /* degree = 3 */ , 6.038881904059573e-2, 1.239917516308172e-1,
|
||||
1.999045567181744e-1, 2.789358995219730e-1 };
|
||||
int degree = 3;
|
||||
for (; degree <= 7; degree++)
|
||||
for (; degree <= 7; ++degree)
|
||||
if (normIminusT <= maxNormForPade[degree - 3])
|
||||
break;
|
||||
return degree;
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
inline int MatrixPower<MatrixType,PlainObject>::getPadeDegree(long double normIminusT)
|
||||
inline int matrix_power_get_pade_degree(long double normIminusT)
|
||||
{
|
||||
#if LDBL_MANT_DIG == 53
|
||||
#if LDBL_MANT_DIG == 53
|
||||
const int maxPadeDegree = 7;
|
||||
const double maxNormForPade[] = { 1.884160592658218e-2L /* degree = 3 */ , 6.038881904059573e-2L,
|
||||
1.239917516308172e-1L, 1.999045567181744e-1L, 2.789358995219730e-1L };
|
||||
|
||||
const double maxNormForPade[] = { 1.884160592658218e-2L /* degree = 3 */ , 6.038881904059573e-2L, 1.239917516308172e-1L,
|
||||
1.999045567181744e-1L, 2.789358995219730e-1L };
|
||||
#elif LDBL_MANT_DIG <= 64
|
||||
const int maxPadeDegree = 8;
|
||||
const double maxNormForPade[] = { 6.3854693117491799460e-3L /* degree = 3 */ , 2.6394893435456973676e-2L,
|
||||
6.4216043030404063729e-2L, 1.1701165502926694307e-1L, 1.7904284231268670284e-1L, 2.4471944416607995472e-1L };
|
||||
|
||||
#elif LDBL_MANT_DIG <= 106
|
||||
const int maxPadeDegree = 10;
|
||||
const double maxNormForPade[] = { 1.0007161601787493236741409687186e-4L /* degree = 3 */ ,
|
||||
@ -414,101 +88,369 @@ inline int MatrixPower<MatrixType,PlainObject>::getPadeDegree(long double normIm
|
||||
9.134603732914548552537150753385375e-2L };
|
||||
#endif
|
||||
int degree = 3;
|
||||
for (; degree <= maxPadeDegree; degree++)
|
||||
for (; degree <= maxPadeDegree; ++degree)
|
||||
if (normIminusT <= maxNormForPade[degree - 3])
|
||||
break;
|
||||
return degree;
|
||||
}
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
void MatrixPower<MatrixType,PlainObject>::computePade(const int& degree, const ComplexMatrix& IminusT)
|
||||
} // namespace internal
|
||||
|
||||
/* (non-doc)
|
||||
* \brief Class for computing triangular matrices to fractional power.
|
||||
*
|
||||
* \tparam MatrixType type of the base.
|
||||
*/
|
||||
template<typename MatrixType, int UpLo = Upper> class MatrixPowerTriangularAtomic
|
||||
{
|
||||
int i = degree << 1;
|
||||
m_fT = coeff(i) * IminusT;
|
||||
for (i--; i; i--) {
|
||||
m_fT = (ComplexMatrix::Identity(m_dimA, m_dimA) + m_fT).template triangularView<Upper>()
|
||||
.solve(coeff(i) * IminusT).eval();
|
||||
private:
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
typedef Array<Scalar,
|
||||
EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime),
|
||||
1,ColMajor,
|
||||
EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::MaxRowsAtCompileTime,MatrixType::MaxColsAtCompileTime)> ArrayType;
|
||||
const MatrixType& m_T;
|
||||
|
||||
void computePade(int degree, const MatrixType& IminusT, MatrixType& res, RealScalar p) const;
|
||||
void compute2x2(MatrixType& res, RealScalar p) const;
|
||||
void computeBig(MatrixType& res, RealScalar p) const;
|
||||
|
||||
public:
|
||||
explicit MatrixPowerTriangularAtomic(const MatrixType& T);
|
||||
void compute(MatrixType& res, RealScalar p) const;
|
||||
};
|
||||
|
||||
template<typename MatrixType, int UpLo>
|
||||
MatrixPowerTriangularAtomic<MatrixType,UpLo>::MatrixPowerTriangularAtomic(const MatrixType& T) :
|
||||
m_T(T)
|
||||
{ eigen_assert(T.rows() == T.cols()); }
|
||||
|
||||
template<typename MatrixType, int UpLo>
|
||||
void MatrixPowerTriangularAtomic<MatrixType,UpLo>::compute(MatrixType& res, RealScalar p) const
|
||||
{
|
||||
switch (m_T.rows()) {
|
||||
case 0:
|
||||
break;
|
||||
case 1:
|
||||
res(0,0) = std::pow(m_T(0,0), p);
|
||||
break;
|
||||
case 2:
|
||||
compute2x2(res, p);
|
||||
break;
|
||||
default:
|
||||
computeBig(res, p);
|
||||
}
|
||||
m_fT += ComplexMatrix::Identity(m_dimA, m_dimA);
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
inline typename MatrixType::RealScalar MatrixPower<MatrixType,PlainObject>::coeff(const int& i)
|
||||
template<typename MatrixType, int UpLo>
|
||||
void MatrixPowerTriangularAtomic<MatrixType,UpLo>::computePade(int degree, const MatrixType& IminusT, MatrixType& res,
|
||||
RealScalar p) const
|
||||
{
|
||||
if (i == 1)
|
||||
return -m_pFrac;
|
||||
else if (i & 1)
|
||||
return (-m_pFrac - RealScalar(i >> 1)) / RealScalar(i << 1);
|
||||
else
|
||||
return (m_pFrac - RealScalar(i >> 1)) / RealScalar((i - 1) << 1);
|
||||
int i = degree<<1;
|
||||
res = (p-(i>>1)) / ((i-1)<<1) * IminusT;
|
||||
for (--i; i; --i) {
|
||||
res = (MatrixType::Identity(m_T.rows(), m_T.cols()) + res).template triangularView<UpLo>()
|
||||
.solve((i==1 ? -p : i&1 ? (-p-(i>>1))/(i<<1) : (p-(i>>1))/((i-1)<<1)) * IminusT).eval();
|
||||
}
|
||||
res += MatrixType::Identity(m_T.rows(), m_T.cols());
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
void MatrixPower<MatrixType,PlainObject>::computeTmp(RealScalar)
|
||||
{ m_tmp = (m_U * m_fT * m_U.adjoint()).real(); }
|
||||
template<typename MatrixType, int UpLo>
|
||||
void MatrixPowerTriangularAtomic<MatrixType,UpLo>::compute2x2(MatrixType& res, RealScalar p) const
|
||||
{
|
||||
using std::abs;
|
||||
using std::pow;
|
||||
|
||||
template<typename MatrixType, typename PlainObject>
|
||||
void MatrixPower<MatrixType,PlainObject>::computeTmp(ComplexScalar)
|
||||
{ m_tmp = m_U * m_fT * m_U.adjoint(); }
|
||||
ArrayType logTdiag = m_T.diagonal().array().log();
|
||||
res(0,0) = pow(m_T(0,0), p);
|
||||
|
||||
for (int i=1; i < m_T.cols(); ++i) {
|
||||
res(i,i) = pow(m_T(i,i), p);
|
||||
if (m_T(i-1,i-1) == m_T(i,i)) {
|
||||
res(i-1,i) = p * pow(m_T(i-1,i), p-1);
|
||||
} else if (2*abs(m_T(i-1,i-1)) < abs(m_T(i,i)) || 2*abs(m_T(i,i)) < abs(m_T(i-1,i-1))) {
|
||||
res(i-1,i) = m_T(i-1,i) * (res(i,i)-res(i-1,i-1)) / (m_T(i,i)-m_T(i-1,i-1));
|
||||
} else {
|
||||
// computation in previous branch is inaccurate if abs(m_T(i,i)) \approx abs(m_T(i-1,i-1))
|
||||
int unwindingNumber = std::ceil(((logTdiag[i]-logTdiag[i-1]).imag() - M_PI) / (2*M_PI));
|
||||
Scalar w = internal::atanh2(m_T(i,i)-m_T(i-1,i-1), m_T(i,i)+m_T(i-1,i-1)) + Scalar(0, M_PI*unwindingNumber);
|
||||
res(i-1,i) = m_T(i-1,i) * RealScalar(2) * std::exp(RealScalar(0.5) * p * (logTdiag[i]+logTdiag[i-1])) *
|
||||
std::sinh(p * w) / (m_T(i,i) - m_T(i-1,i-1));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType, int UpLo>
|
||||
void MatrixPowerTriangularAtomic<MatrixType,UpLo>::computeBig(MatrixType& res, RealScalar p) const
|
||||
{
|
||||
const int digits = std::numeric_limits<RealScalar>::digits;
|
||||
const RealScalar maxNormForPade = digits <= 24? 4.3386528e-1f: // sigle precision
|
||||
digits <= 53? 2.789358995219730e-1: // double precision
|
||||
digits <= 64? 2.4471944416607995472e-1L: // extended precision
|
||||
digits <= 106? 1.1016843812851143391275867258512e-01: // double-double
|
||||
9.134603732914548552537150753385375e-02; // quadruple precision
|
||||
int degree, degree2, numberOfSquareRoots=0, numberOfExtraSquareRoots=0;
|
||||
MatrixType IminusT, sqrtT, T=m_T;
|
||||
RealScalar normIminusT;
|
||||
|
||||
while (true) {
|
||||
IminusT = MatrixType::Identity(m_T.rows(), m_T.cols()) - T;
|
||||
normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
|
||||
if (normIminusT < maxNormForPade) {
|
||||
degree = internal::matrix_power_get_pade_degree(normIminusT);
|
||||
degree2 = internal::matrix_power_get_pade_degree(normIminusT/2);
|
||||
if (degree - degree2 <= 1 || numberOfExtraSquareRoots)
|
||||
break;
|
||||
++numberOfExtraSquareRoots;
|
||||
}
|
||||
MatrixSquareRootTriangular<MatrixType>(T).compute(sqrtT);
|
||||
T = sqrtT;
|
||||
++numberOfSquareRoots;
|
||||
}
|
||||
computePade(degree, IminusT, res, p);
|
||||
|
||||
for (; numberOfSquareRoots; --numberOfSquareRoots) {
|
||||
compute2x2(res, std::ldexp(p,-numberOfSquareRoots));
|
||||
res *= res;
|
||||
}
|
||||
compute2x2(res, p);
|
||||
}
|
||||
|
||||
/**
|
||||
* \ingroup MatrixFunctions_Module
|
||||
*
|
||||
* \brief Proxy for the matrix power multiplied by other matrix.
|
||||
* \brief Class for computing matrix powers.
|
||||
*
|
||||
* \tparam Derived type of the base, a matrix (expression).
|
||||
* \tparam RhsDerived type of the multiplier.
|
||||
* \tparam MatrixType type of the base, expected to be an instantiation
|
||||
* of the Matrix class template.
|
||||
*
|
||||
* This class holds the arguments to the matrix power 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
|
||||
* MatrixPowerReturnValue::operator*() and related functions and most
|
||||
* of the time this is the only way it is used.
|
||||
* This class is capable of computing real/complex matrices raised to
|
||||
* an arbitrary real power. Meanwhile, it saves the result of Schur
|
||||
* decomposition if an non-integral power has even been calculated.
|
||||
* Therefore, if you want to compute multiple (>= 2) matrix powers
|
||||
* for the same matrix, using the class directly is more efficient than
|
||||
* calling MatrixBase::pow().
|
||||
*
|
||||
* Example:
|
||||
* \include MatrixPower_optimal.cpp
|
||||
* Output: \verbinclude MatrixPower_optimal.out
|
||||
*/
|
||||
template<typename Derived, typename RhsDerived>
|
||||
class MatrixPowerProductReturnValue : public ReturnByValue<MatrixPowerProductReturnValue<Derived,RhsDerived> >
|
||||
template<typename MatrixType> class MatrixPower
|
||||
{
|
||||
private:
|
||||
typedef typename Derived::PlainObject MatrixType;
|
||||
typedef typename RhsDerived::PlainObject PlainObject;
|
||||
typedef typename RhsDerived::RealScalar RealScalar;
|
||||
typedef typename RhsDerived::Index Index;
|
||||
static const int Rows = MatrixType::RowsAtCompileTime;
|
||||
static const int Cols = MatrixType::ColsAtCompileTime;
|
||||
static const int Options = MatrixType::Options;
|
||||
static const int MaxRows = MatrixType::MaxRowsAtCompileTime;
|
||||
static const int MaxCols = MatrixType::MaxColsAtCompileTime;
|
||||
|
||||
typedef typename MatrixType::Scalar Scalar;
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
typedef typename MatrixType::Index Index;
|
||||
typedef Matrix<std::complex<RealScalar>,Rows,Cols,Options,MaxRows,MaxCols> ComplexMatrix;
|
||||
|
||||
const MatrixType& m_A;
|
||||
MatrixType m_tmp1, m_tmp2;
|
||||
ComplexMatrix m_T, m_U, m_fT;
|
||||
bool m_init;
|
||||
|
||||
RealScalar modfAndInit(RealScalar, RealScalar*);
|
||||
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void apply(const PlainObject&, ResultType&, bool&);
|
||||
|
||||
template<typename ResultType>
|
||||
void computeIntPower(ResultType&, RealScalar);
|
||||
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void computeIntPower(const PlainObject&, ResultType&, RealScalar);
|
||||
|
||||
template<typename ResultType>
|
||||
void computeFracPower(ResultType&, RealScalar);
|
||||
|
||||
public:
|
||||
/**
|
||||
* \brief Constructor.
|
||||
*
|
||||
* \param[in] A %Matrix (expression), the base of the matrix power.
|
||||
* \param[in] p scalar, the exponent of the matrix power.
|
||||
* \prarm[in] b %Matrix (expression), the multiplier.
|
||||
* \param[in] A the base of the matrix power.
|
||||
*/
|
||||
MatrixPowerProductReturnValue(const Derived& A, RealScalar p, const RhsDerived& b)
|
||||
: m_A(A), m_p(p), m_b(b) { }
|
||||
explicit MatrixPower(const MatrixType& A);
|
||||
|
||||
/**
|
||||
* \brief Compute the expression.
|
||||
* \brief Return the expression \f$ A^p \f$.
|
||||
*
|
||||
* \param[out] result \f$ A^p b \f$ where \p A, \p p and \p bare as
|
||||
* in the constructor.
|
||||
* \param[in] p exponent, a real scalar.
|
||||
*/
|
||||
template<typename ResultType>
|
||||
inline void evalTo(ResultType& result) const
|
||||
{
|
||||
const MatrixType A = m_A;
|
||||
const PlainObject b = m_b;
|
||||
MatrixPower<MatrixType, PlainObject> mp(A, m_p, b);
|
||||
mp.compute(result);
|
||||
}
|
||||
const MatrixPowerReturnValue<MatrixPower<MatrixType> > operator()(RealScalar p)
|
||||
{ return MatrixPowerReturnValue<MatrixPower<MatrixType> >(*this, p); }
|
||||
|
||||
Index rows() const { return m_b.rows(); }
|
||||
Index cols() const { return m_b.cols(); }
|
||||
/**
|
||||
* \brief Compute the matrix power.
|
||||
*
|
||||
* \param[in] p exponent, a real scalar.
|
||||
* \param[out] res \f$ A^p \f$ where A is specified in the
|
||||
* constructor.
|
||||
*/
|
||||
void compute(MatrixType& res, RealScalar p);
|
||||
|
||||
private:
|
||||
const Derived& m_A;
|
||||
const RealScalar m_p;
|
||||
const RhsDerived& m_b;
|
||||
MatrixPowerProductReturnValue& operator=(const MatrixPowerProductReturnValue&);
|
||||
/**
|
||||
* \brief Compute the matrix power multiplied by another matrix.
|
||||
*
|
||||
* \param[in] b a matrix with the same rows as A.
|
||||
* \param[in] p exponent, a real scalar.
|
||||
* \param[in] noalias
|
||||
* \param[out] res \f$ A^p b \f$, where A is specified in the
|
||||
* constructor.
|
||||
*/
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void compute(const PlainObject& b, ResultType& res, RealScalar p);
|
||||
|
||||
Index rows() const { return m_A.rows(); }
|
||||
Index cols() const { return m_A.cols(); }
|
||||
};
|
||||
|
||||
template<typename MatrixType>
|
||||
MatrixPower<MatrixType>::MatrixPower(const MatrixType& A) :
|
||||
m_A(A),
|
||||
m_init(false)
|
||||
{ /* empty body */ }
|
||||
|
||||
template<typename MatrixType>
|
||||
void MatrixPower<MatrixType>::compute(MatrixType& res, RealScalar p)
|
||||
{
|
||||
switch (m_A.cols()) {
|
||||
case 0:
|
||||
break;
|
||||
case 1:
|
||||
res(0,0) = std::pow(m_A(0,0), p);
|
||||
break;
|
||||
default:
|
||||
RealScalar intpart;
|
||||
RealScalar x = modfAndInit(p, &intpart);
|
||||
res = MatrixType::Identity(m_A.rows(),m_A.cols());
|
||||
computeIntPower(res, intpart);
|
||||
computeFracPower(res, x);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void MatrixPower<MatrixType>::compute(const PlainObject& b, ResultType& res, RealScalar p)
|
||||
{
|
||||
switch (m_A.cols()) {
|
||||
case 0:
|
||||
break;
|
||||
case 1:
|
||||
res = std::pow(m_A(0,0), p) * b;
|
||||
break;
|
||||
default:
|
||||
RealScalar intpart;
|
||||
RealScalar x = modfAndInit(p, &intpart);
|
||||
computeIntPower(b, res, intpart);
|
||||
computeFracPower(res, x);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
typename MatrixType::RealScalar MatrixPower<MatrixType>::modfAndInit(RealScalar x, RealScalar* intpart)
|
||||
{
|
||||
static RealScalar maxAbsEival, minAbsEival;
|
||||
*intpart = std::floor(x);
|
||||
RealScalar res = x - *intpart;
|
||||
|
||||
if (!m_init && res) { // !init && res
|
||||
const ComplexSchur<MatrixType> schurOfA(m_A);
|
||||
m_T = schurOfA.matrixT();
|
||||
m_U = schurOfA.matrixU();
|
||||
m_init = true;
|
||||
|
||||
const Array<RealScalar,EIGEN_SIZE_MIN_PREFER_FIXED(Rows,Cols),1,ColMajor,EIGEN_SIZE_MIN_PREFER_FIXED(MaxRows,MaxCols)>
|
||||
absTdiag = m_T.diagonal().array().abs();
|
||||
maxAbsEival = absTdiag.maxCoeff();
|
||||
minAbsEival = absTdiag.minCoeff();
|
||||
}
|
||||
|
||||
if (res > RealScalar(0.5) && res > (1-res) * std::pow(maxAbsEival/minAbsEival, res)) {
|
||||
--res;
|
||||
++*intpart;
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void MatrixPower<MatrixType>::apply(const PlainObject& b, ResultType& res, bool& init)
|
||||
{
|
||||
if (init)
|
||||
res = m_tmp1 * res;
|
||||
else {
|
||||
init = true;
|
||||
res.noalias() = m_tmp1 * b;
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
template<typename ResultType>
|
||||
void MatrixPower<MatrixType>::computeIntPower(ResultType& res, RealScalar p)
|
||||
{
|
||||
RealScalar pp = std::abs(p);
|
||||
|
||||
if (p<0) m_tmp1 = m_A.inverse();
|
||||
else m_tmp1 = m_A;
|
||||
|
||||
while (pp >= 1) {
|
||||
if (std::fmod(pp, 2) >= 1)
|
||||
res = m_tmp1 * res;
|
||||
m_tmp1 *= m_tmp1;
|
||||
pp /= 2;
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
template<typename PlainObject, typename ResultType>
|
||||
void MatrixPower<MatrixType>::computeIntPower(const PlainObject& b, ResultType& res, RealScalar p)
|
||||
{
|
||||
if (b.cols() > m_A.cols()) {
|
||||
m_tmp2 = MatrixType::Identity(m_A.rows(),m_A.cols());
|
||||
computeIntPower(m_tmp2, p);
|
||||
res.noalias() = m_tmp2 * b;
|
||||
} else {
|
||||
RealScalar pp = std::abs(p);
|
||||
int cost = internal::binary_powering_cost(pp);
|
||||
bool init = false;
|
||||
|
||||
if (p==0) {
|
||||
res = b;
|
||||
return;
|
||||
}
|
||||
if (p<0) m_tmp1 = m_A.inverse();
|
||||
else m_tmp1 = m_A;
|
||||
|
||||
while (b.cols()*pp > m_A.cols()*cost) {
|
||||
if (std::fmod(pp, 2) >= 1) {
|
||||
apply(b, res, init);
|
||||
--cost;
|
||||
}
|
||||
m_tmp1 *= m_tmp1;
|
||||
--cost;
|
||||
pp /= 2;
|
||||
}
|
||||
for (; pp >= 1; --pp)
|
||||
apply(b, res, init);
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
template<typename ResultType>
|
||||
void MatrixPower<MatrixType>::computeFracPower(ResultType& res, RealScalar p)
|
||||
{
|
||||
if (p) {
|
||||
MatrixPowerTriangularAtomic<ComplexMatrix>(m_T).compute(m_fT, p);
|
||||
internal::recompose_complex_schur<NumTraits<Scalar>::IsComplex>::run(m_tmp1, m_fT, m_U);
|
||||
res = m_tmp1 * res;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* \ingroup MatrixFunctions_Module
|
||||
*
|
||||
@ -525,11 +467,10 @@ class MatrixPowerProductReturnValue : public ReturnByValue<MatrixPowerProductRet
|
||||
template<typename Derived>
|
||||
class MatrixPowerReturnValue : public ReturnByValue<MatrixPowerReturnValue<Derived> >
|
||||
{
|
||||
private:
|
||||
public:
|
||||
typedef typename Derived::RealScalar RealScalar;
|
||||
typedef typename Derived::Index Index;
|
||||
|
||||
public:
|
||||
/**
|
||||
* \brief Constructor.
|
||||
*
|
||||
@ -539,26 +480,6 @@ class MatrixPowerReturnValue : public ReturnByValue<MatrixPowerReturnValue<Deriv
|
||||
MatrixPowerReturnValue(const Derived& A, RealScalar p)
|
||||
: m_A(A), m_p(p) { }
|
||||
|
||||
/**
|
||||
* \brief Return the matrix power multiplied by %Matrix \p b.
|
||||
*
|
||||
* The %MatrixPower class can optimize \f$ A^p b \f$ computing, and
|
||||
* this method provides an elegant way to call it.
|
||||
*
|
||||
* Unlike general matrix-matrix / matrix-vector product, this does
|
||||
* \b NOT produce a temporary storage for the result. Therefore,
|
||||
* the code below is \a already optimal:
|
||||
* \code
|
||||
* v = A.pow(p) * b;
|
||||
* // v.noalias() = A.pow(p) * b; Won't compile!
|
||||
* \endcode
|
||||
*
|
||||
* \param[in] b %Matrix (expression), the multiplier.
|
||||
*/
|
||||
template<typename RhsDerived>
|
||||
const MatrixPowerProductReturnValue<Derived,RhsDerived> operator*(const MatrixBase<RhsDerived>& b) const
|
||||
{ return MatrixPowerProductReturnValue<Derived,RhsDerived>(m_A, m_p, b.derived()); }
|
||||
|
||||
/**
|
||||
* \brief Compute the matrix power.
|
||||
*
|
||||
@ -566,14 +487,8 @@ class MatrixPowerReturnValue : public ReturnByValue<MatrixPowerReturnValue<Deriv
|
||||
* constructor.
|
||||
*/
|
||||
template<typename ResultType>
|
||||
inline void evalTo(ResultType& result) const
|
||||
{
|
||||
typedef typename Derived::PlainObject PlainObject;
|
||||
const PlainObject A = m_A;
|
||||
const PlainObject Identity = PlainObject::Identity(m_A.rows(), m_A.cols());
|
||||
MatrixPower<PlainObject> mp(A, m_p, Identity);
|
||||
mp.compute(result);
|
||||
}
|
||||
inline void evalTo(ResultType& res) const
|
||||
{ MatrixPower<typename Derived::PlainObject>(m_A).compute(res, m_p); }
|
||||
|
||||
Index rows() const { return m_A.rows(); }
|
||||
Index cols() const { return m_A.cols(); }
|
||||
@ -584,18 +499,42 @@ class MatrixPowerReturnValue : public ReturnByValue<MatrixPowerReturnValue<Deriv
|
||||
MatrixPowerReturnValue& operator=(const MatrixPowerReturnValue&);
|
||||
};
|
||||
|
||||
namespace internal {
|
||||
template<typename Derived>
|
||||
struct traits<MatrixPowerReturnValue<Derived> >
|
||||
{
|
||||
typedef typename Derived::PlainObject ReturnType;
|
||||
};
|
||||
template<typename MatrixType>
|
||||
class MatrixPowerReturnValue<MatrixPower<MatrixType> >
|
||||
: public ReturnByValue<MatrixPowerReturnValue<MatrixPower<MatrixType> > >
|
||||
{
|
||||
public:
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
typedef typename MatrixType::Index Index;
|
||||
|
||||
template<typename Derived, typename RhsDerived>
|
||||
struct traits<MatrixPowerProductReturnValue<Derived,RhsDerived> >
|
||||
{
|
||||
typedef typename RhsDerived::PlainObject ReturnType;
|
||||
};
|
||||
MatrixPowerReturnValue(MatrixPower<MatrixType>& ref, RealScalar p)
|
||||
: m_pow(ref), m_p(p) { }
|
||||
|
||||
template<typename ResultType>
|
||||
inline void evalTo(ResultType& res) const
|
||||
{ m_pow.compute(res, m_p); }
|
||||
|
||||
Index rows() const { return m_pow.rows(); }
|
||||
Index cols() const { return m_pow.cols(); }
|
||||
|
||||
private:
|
||||
MatrixPower<MatrixType>& m_pow;
|
||||
const RealScalar m_p;
|
||||
MatrixPowerReturnValue& operator=(const MatrixPowerReturnValue&);
|
||||
};
|
||||
|
||||
namespace internal {
|
||||
template<typename Derived>
|
||||
struct traits<MatrixPowerReturnValue<Derived> >
|
||||
{ typedef typename Derived::PlainObject ReturnType; };
|
||||
|
||||
template<typename MatrixType>
|
||||
struct traits<MatrixPowerReturnValue<MatrixPower<MatrixType> > >
|
||||
{ typedef MatrixType ReturnType; };
|
||||
|
||||
template<typename Derived>
|
||||
struct traits<MatrixPowerProductBase<Derived> >
|
||||
{ typedef typename traits<Derived>::ReturnType ReturnType; };
|
||||
}
|
||||
|
||||
template<typename Derived>
|
||||
|
||||
@ -11,6 +11,6 @@ int main()
|
||||
sin(1), cos(1), 0,
|
||||
0 , 0 , 1;
|
||||
std::cout << "The matrix A is:\n" << A << "\n\n"
|
||||
<< "The matrix power A^(pi/4) is:\n" << A.pow(pi/4) << std::endl;
|
||||
"The matrix power A^(pi/4) is:\n" << A.pow(pi/4) << std::endl;
|
||||
return 0;
|
||||
}
|
||||
|
||||
17
unsupported/doc/examples/MatrixPower_optimal.cpp
Normal file
17
unsupported/doc/examples/MatrixPower_optimal.cpp
Normal file
@ -0,0 +1,17 @@
|
||||
#include <unsupported/Eigen/MatrixFunctions>
|
||||
#include <iostream>
|
||||
|
||||
using namespace Eigen;
|
||||
|
||||
int main()
|
||||
{
|
||||
Matrix4cd A = Matrix4cd::Random();
|
||||
MatrixPower<Matrix4cd> Apow(A);
|
||||
|
||||
std::cout << "The matrix A is:\n" << A << "\n\n"
|
||||
"A^3.1 is:\n" << Apow(3.1) << "\n\n"
|
||||
"A^3.3 is:\n" << Apow(3.3) << "\n\n"
|
||||
"A^3.7 is:\n" << Apow(3.7) << "\n\n"
|
||||
"A^3.9 is:\n" << Apow(3.9) << std::endl;
|
||||
return 0;
|
||||
}
|
||||
@ -16,15 +16,17 @@ void test2dRotation(double tol)
|
||||
T angle, c, s;
|
||||
|
||||
A << 0, 1, -1, 0;
|
||||
for (int i = 0; i <= 20; i++) {
|
||||
MatrixPower<Matrix<T,2,2> > Apow(A);
|
||||
|
||||
for (int i=0; i<=20; ++i) {
|
||||
angle = pow(10, (i-10) / 5.);
|
||||
c = std::cos(angle);
|
||||
s = std::sin(angle);
|
||||
B << c, s, -s, c;
|
||||
|
||||
C = A.pow(std::ldexp(angle, 1) / M_PI);
|
||||
std::cout << "test2dRotation: i = " << i << " error powerm = " << relerr(C, B) << '\n';
|
||||
VERIFY(C.isApprox(B, T(tol)));
|
||||
C = Apow(std::ldexp(angle,1) / M_PI);
|
||||
std::cout << "test2dRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n';
|
||||
VERIFY(C.isApprox(B, static_cast<T>(tol)));
|
||||
}
|
||||
}
|
||||
|
||||
@ -36,15 +38,17 @@ void test2dHyperbolicRotation(double tol)
|
||||
std::complex<T> ish(0, std::sinh(1));
|
||||
|
||||
A << ch, ish, -ish, ch;
|
||||
for (int i = 0; i <= 20; i++) {
|
||||
angle = std::ldexp(T(i-10), -1);
|
||||
MatrixPower<Matrix<std::complex<T>,2,2> > Apow(A);
|
||||
|
||||
for (int i=0; i<=20; ++i) {
|
||||
angle = std::ldexp(static_cast<T>(i-10), -1);
|
||||
ch = std::cosh(angle);
|
||||
ish = std::complex<T>(0, std::sinh(angle));
|
||||
B << ch, ish, -ish, ch;
|
||||
|
||||
C = A.pow(angle);
|
||||
std::cout << "test2dHyperbolicRotation: i = " << i << " error powerm = " << relerr(C, B) << '\n';
|
||||
VERIFY(C.isApprox(B, T(tol)));
|
||||
C = Apow(angle);
|
||||
std::cout << "test2dHyperbolicRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n';
|
||||
VERIFY(C.isApprox(B, static_cast<T>(tol)));
|
||||
}
|
||||
}
|
||||
|
||||
@ -55,71 +59,37 @@ void testExponentLaws(const MatrixType& m, double tol)
|
||||
MatrixType m1, m2, m3, m4, m5;
|
||||
RealScalar x, y;
|
||||
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
for (int i=0; i<g_repeat; ++i) {
|
||||
generateTestMatrix<MatrixType>::run(m1, m.rows());
|
||||
MatrixPower<MatrixType> mpow(m1);
|
||||
|
||||
x = internal::random<RealScalar>();
|
||||
y = internal::random<RealScalar>();
|
||||
m2 = m1.pow(x);
|
||||
m3 = m1.pow(y);
|
||||
m2 = mpow(x);
|
||||
m3 = mpow(y);
|
||||
|
||||
m4 = m1.pow(x + y);
|
||||
m4 = mpow(x+y);
|
||||
m5.noalias() = m2 * m3;
|
||||
std::cout << "testExponentLaws: error powerm = " << relerr(m4, m5);
|
||||
VERIFY(m4.isApprox(m5, RealScalar(tol)));
|
||||
|
||||
if (!NumTraits<typename MatrixType::Scalar>::IsComplex) {
|
||||
m4 = m1.pow(x * y);
|
||||
m5 = m2.pow(y);
|
||||
std::cout << " " << relerr(m4, m5);
|
||||
VERIFY(m4.isApprox(m5, RealScalar(tol)));
|
||||
}
|
||||
std::cout << "testExponentLaws: error powerm = " << relerr(m4, m5);
|
||||
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
|
||||
|
||||
m4 = mpow(x*y);
|
||||
m5 = m2.pow(y);
|
||||
std::cout << " " << relerr(m4, m5);
|
||||
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
|
||||
|
||||
m4 = (std::abs(x) * m1).pow(y);
|
||||
m5 = std::pow(std::abs(x), y) * m3;
|
||||
std::cout << " " << relerr(m4, m5) << '\n';
|
||||
VERIFY(m4.isApprox(m5, RealScalar(tol)));
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType, typename VectorType>
|
||||
void testMatrixVectorProduct(const MatrixType& m, const VectorType& v, double tol)
|
||||
{
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
MatrixType m1;
|
||||
VectorType v1, v2, v3;
|
||||
RealScalar p;
|
||||
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
generateTestMatrix<MatrixType>::run(m1, m.rows());
|
||||
v1 = VectorType::Random(v.rows(), v.cols());
|
||||
p = internal::random<RealScalar>();
|
||||
|
||||
v2.noalias() = m1.pow(p).eval() * v1;
|
||||
v1 = m1.pow(p) * v1;
|
||||
std::cout << "testMatrixVectorProduct: error powerm = " << relerr(v2, v1) << '\n';
|
||||
VERIFY(v2.isApprox(v1, RealScalar(tol)));
|
||||
}
|
||||
}
|
||||
|
||||
template<typename MatrixType>
|
||||
void testAliasing(const MatrixType& m)
|
||||
{
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
MatrixType m1, m2;
|
||||
RealScalar p;
|
||||
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
generateTestMatrix<MatrixType>::run(m1, m.rows());
|
||||
p = internal::random<RealScalar>();
|
||||
|
||||
m2 = m1.pow(p);
|
||||
m1 = m1.pow(p);
|
||||
VERIFY(m1 == m2);
|
||||
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
|
||||
}
|
||||
}
|
||||
|
||||
void test_matrix_power()
|
||||
{
|
||||
typedef Matrix<long double,Dynamic,Dynamic> MatrixXe;
|
||||
|
||||
CALL_SUBTEST_2(test2dRotation<double>(1e-13));
|
||||
CALL_SUBTEST_1(test2dRotation<float>(2e-5)); // was 1e-5, relaxed for clang 2.8 / linux / x86-64
|
||||
CALL_SUBTEST_9(test2dRotation<long double>(1e-13));
|
||||
@ -135,20 +105,4 @@ void test_matrix_power()
|
||||
CALL_SUBTEST_5(testExponentLaws(Matrix3cf(), 1e-4));
|
||||
CALL_SUBTEST_8(testExponentLaws(Matrix4f(), 1e-4));
|
||||
CALL_SUBTEST_6(testExponentLaws(MatrixXf(8,8), 1e-4));
|
||||
|
||||
CALL_SUBTEST_2(testMatrixVectorProduct(Matrix2d(), Vector2d(), 1e-13));
|
||||
CALL_SUBTEST_7(testMatrixVectorProduct(Matrix<double,3,3,RowMajor>(), Vector3d(), 1e-13));
|
||||
CALL_SUBTEST_3(testMatrixVectorProduct(Matrix4cd(), Vector4cd(), 1e-13));
|
||||
CALL_SUBTEST_4(testMatrixVectorProduct(MatrixXd(8,8), MatrixXd(8,2), 1e-13));
|
||||
CALL_SUBTEST_1(testMatrixVectorProduct(Matrix2f(), Vector2f(), 1e-4));
|
||||
CALL_SUBTEST_5(testMatrixVectorProduct(Matrix3cf(), Vector3cf(), 1e-4));
|
||||
CALL_SUBTEST_8(testMatrixVectorProduct(Matrix4f(), Vector4f(), 1e-4));
|
||||
CALL_SUBTEST_6(testMatrixVectorProduct(MatrixXf(8,8), VectorXf(8), 1e-4));
|
||||
CALL_SUBTEST_9(testMatrixVectorProduct(Matrix<long double,Dynamic,Dynamic>(7,7), Matrix<long double,7,9>(), 1e-13));
|
||||
|
||||
CALL_SUBTEST_7(testAliasing(Matrix<double,3,3,RowMajor>()));
|
||||
CALL_SUBTEST_3(testAliasing(Matrix4cd()));
|
||||
CALL_SUBTEST_4(testAliasing(MatrixXd(8,8)));
|
||||
CALL_SUBTEST_5(testAliasing(Matrix3cf()));
|
||||
CALL_SUBTEST_6(testAliasing(MatrixXf(8,8)));
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user