diff options
Diffstat (limited to 'eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h')
| -rw-r--r-- | eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h | 486 |
1 files changed, 486 insertions, 0 deletions
diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h new file mode 100644 index 0000000..c744fc0 --- /dev/null +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h @@ -0,0 +1,486 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk> +// Copyright (C) 2011 Chen-Pang He <jdh8@ms63.hinet.net> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#ifndef EIGEN_MATRIX_LOGARITHM +#define EIGEN_MATRIX_LOGARITHM + +#ifndef M_PI +#define M_PI 3.141592653589793238462643383279503L +#endif + +namespace Eigen { + +/** \ingroup MatrixFunctions_Module + * \class MatrixLogarithmAtomic + * \brief Helper class for computing matrix logarithm of atomic matrices. + * + * \internal + * Here, an atomic matrix is a triangular matrix whose diagonal + * entries are close to each other. + * + * \sa class MatrixFunctionAtomic, MatrixBase::log() + */ +template <typename MatrixType> +class MatrixLogarithmAtomic +{ +public: + + typedef typename MatrixType::Scalar Scalar; + // typedef typename MatrixType::Index Index; + typedef typename NumTraits<Scalar>::Real RealScalar; + // typedef typename internal::stem_function<Scalar>::type StemFunction; + // typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; + + /** \brief Constructor. */ + MatrixLogarithmAtomic() { } + + /** \brief Compute matrix logarithm of atomic matrix + * \param[in] A argument of matrix logarithm, should be upper triangular and atomic + * \returns The logarithm of \p A. + */ + MatrixType compute(const MatrixType& A); + +private: + + void compute2x2(const MatrixType& A, MatrixType& result); + void computeBig(const MatrixType& A, MatrixType& result); + int getPadeDegree(float normTminusI); + int getPadeDegree(double normTminusI); + int getPadeDegree(long double normTminusI); + void computePade(MatrixType& result, const MatrixType& T, int degree); + void computePade3(MatrixType& result, const MatrixType& T); + void computePade4(MatrixType& result, const MatrixType& T); + void computePade5(MatrixType& result, const MatrixType& T); + void computePade6(MatrixType& result, const MatrixType& T); + void computePade7(MatrixType& result, const MatrixType& T); + void computePade8(MatrixType& result, const MatrixType& T); + void computePade9(MatrixType& result, const MatrixType& T); + void computePade10(MatrixType& result, const MatrixType& T); + void computePade11(MatrixType& result, const MatrixType& T); + + static const int minPadeDegree = 3; + static const int maxPadeDegree = std::numeric_limits<RealScalar>::digits<= 24? 5: // single precision + std::numeric_limits<RealScalar>::digits<= 53? 7: // double precision + std::numeric_limits<RealScalar>::digits<= 64? 8: // extended precision + std::numeric_limits<RealScalar>::digits<=106? 10: // double-double + 11; // quadruple precision + + // Prevent copying + MatrixLogarithmAtomic(const MatrixLogarithmAtomic&); + MatrixLogarithmAtomic& operator=(const MatrixLogarithmAtomic&); +}; + +/** \brief Compute logarithm of triangular matrix with clustered eigenvalues. */ +template <typename MatrixType> +MatrixType MatrixLogarithmAtomic<MatrixType>::compute(const MatrixType& A) +{ + using std::log; + MatrixType result(A.rows(), A.rows()); + if (A.rows() == 1) + result(0,0) = log(A(0,0)); + else if (A.rows() == 2) + compute2x2(A, result); + else + computeBig(A, result); + return result; +} + +/** \brief Compute logarithm of 2x2 triangular matrix. */ +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::compute2x2(const MatrixType& A, MatrixType& result) +{ + using std::abs; + using std::ceil; + using std::imag; + using std::log; + + Scalar logA00 = log(A(0,0)); + Scalar logA11 = log(A(1,1)); + + result(0,0) = logA00; + result(1,0) = Scalar(0); + result(1,1) = logA11; + + if (A(0,0) == A(1,1)) { + result(0,1) = A(0,1) / A(0,0); + } else if ((abs(A(0,0)) < 0.5*abs(A(1,1))) || (abs(A(0,0)) > 2*abs(A(1,1)))) { + result(0,1) = A(0,1) * (logA11 - logA00) / (A(1,1) - A(0,0)); + } else { + // computation in previous branch is inaccurate if A(1,1) \approx A(0,0) + int unwindingNumber = static_cast<int>(ceil((imag(logA11 - logA00) - M_PI) / (2*M_PI))); + Scalar y = A(1,1) - A(0,0), x = A(1,1) + A(0,0); + result(0,1) = A(0,1) * (Scalar(2) * numext::atanh2(y,x) + Scalar(0,2*M_PI*unwindingNumber)) / y; + } +} + +/** \brief Compute logarithm of triangular matrices with size > 2. + * \details This uses a inverse scale-and-square algorithm. */ +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computeBig(const MatrixType& A, MatrixType& result) +{ + using std::pow; + int numberOfSquareRoots = 0; + int numberOfExtraSquareRoots = 0; + int degree; + MatrixType T = A, sqrtT; + const RealScalar maxNormForPade = maxPadeDegree<= 5? 5.3149729967117310e-1: // single precision + maxPadeDegree<= 7? 2.6429608311114350e-1: // double precision + maxPadeDegree<= 8? 2.32777776523703892094e-1L: // extended precision + maxPadeDegree<=10? 1.05026503471351080481093652651105e-1L: // double-double + 1.1880960220216759245467951592883642e-1L; // quadruple precision + + while (true) { + RealScalar normTminusI = (T - MatrixType::Identity(T.rows(), T.rows())).cwiseAbs().colwise().sum().maxCoeff(); + if (normTminusI < maxNormForPade) { + degree = getPadeDegree(normTminusI); + int degree2 = getPadeDegree(normTminusI / RealScalar(2)); + if ((degree - degree2 <= 1) || (numberOfExtraSquareRoots == 1)) + break; + ++numberOfExtraSquareRoots; + } + MatrixSquareRootTriangular<MatrixType>(T).compute(sqrtT); + T = sqrtT.template triangularView<Upper>(); + ++numberOfSquareRoots; + } + + computePade(result, T, degree); + result *= pow(RealScalar(2), numberOfSquareRoots); +} + +/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = float) */ +template <typename MatrixType> +int MatrixLogarithmAtomic<MatrixType>::getPadeDegree(float normTminusI) +{ + const float maxNormForPade[] = { 2.5111573934555054e-1 /* degree = 3 */ , 4.0535837411880493e-1, + 5.3149729967117310e-1 }; + int degree = 3; + for (; degree <= maxPadeDegree; ++degree) + if (normTminusI <= maxNormForPade[degree - minPadeDegree]) + break; + return degree; +} + +/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = double) */ +template <typename MatrixType> +int MatrixLogarithmAtomic<MatrixType>::getPadeDegree(double normTminusI) +{ + const double maxNormForPade[] = { 1.6206284795015624e-2 /* degree = 3 */ , 5.3873532631381171e-2, + 1.1352802267628681e-1, 1.8662860613541288e-1, 2.642960831111435e-1 }; + int degree = 3; + for (; degree <= maxPadeDegree; ++degree) + if (normTminusI <= maxNormForPade[degree - minPadeDegree]) + break; + return degree; +} + +/* \brief Get suitable degree for Pade approximation. (specialized for RealScalar = long double) */ +template <typename MatrixType> +int MatrixLogarithmAtomic<MatrixType>::getPadeDegree(long double normTminusI) +{ +#if LDBL_MANT_DIG == 53 // double precision + 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 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 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 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 + int degree = 3; + for (; degree <= maxPadeDegree; ++degree) + if (normTminusI <= maxNormForPade[degree - minPadeDegree]) + break; + return degree; +} + +/* \brief Compute Pade approximation to matrix logarithm */ +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade(MatrixType& result, const MatrixType& T, int degree) +{ + switch (degree) { + case 3: computePade3(result, T); break; + case 4: computePade4(result, T); break; + case 5: computePade5(result, T); break; + case 6: computePade6(result, T); break; + case 7: computePade7(result, T); break; + case 8: computePade8(result, T); break; + case 9: computePade9(result, T); break; + case 10: computePade10(result, T); break; + case 11: computePade11(result, T); break; + default: assert(false); // should never happen + } +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade3(MatrixType& result, const MatrixType& T) +{ + const int degree = 3; + const RealScalar nodes[] = { 0.1127016653792583114820734600217600L, 0.5000000000000000000000000000000000L, + 0.8872983346207416885179265399782400L }; + const RealScalar weights[] = { 0.2777777777777777777777777777777778L, 0.4444444444444444444444444444444444L, + 0.2777777777777777777777777777777778L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade4(MatrixType& result, const MatrixType& T) +{ + const int degree = 4; + const RealScalar nodes[] = { 0.0694318442029737123880267555535953L, 0.3300094782075718675986671204483777L, + 0.6699905217924281324013328795516223L, 0.9305681557970262876119732444464048L }; + const RealScalar weights[] = { 0.1739274225687269286865319746109997L, 0.3260725774312730713134680253890003L, + 0.3260725774312730713134680253890003L, 0.1739274225687269286865319746109997L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade5(MatrixType& result, const MatrixType& T) +{ + const int degree = 5; + const RealScalar nodes[] = { 0.0469100770306680036011865608503035L, 0.2307653449471584544818427896498956L, + 0.5000000000000000000000000000000000L, 0.7692346550528415455181572103501044L, + 0.9530899229693319963988134391496965L }; + const RealScalar weights[] = { 0.1184634425280945437571320203599587L, 0.2393143352496832340206457574178191L, + 0.2844444444444444444444444444444444L, 0.2393143352496832340206457574178191L, + 0.1184634425280945437571320203599587L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade6(MatrixType& result, const MatrixType& T) +{ + const int degree = 6; + const RealScalar nodes[] = { 0.0337652428984239860938492227530027L, 0.1693953067668677431693002024900473L, + 0.3806904069584015456847491391596440L, 0.6193095930415984543152508608403560L, + 0.8306046932331322568306997975099527L, 0.9662347571015760139061507772469973L }; + const RealScalar weights[] = { 0.0856622461895851725201480710863665L, 0.1803807865240693037849167569188581L, + 0.2339569672863455236949351719947755L, 0.2339569672863455236949351719947755L, + 0.1803807865240693037849167569188581L, 0.0856622461895851725201480710863665L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade7(MatrixType& result, const MatrixType& T) +{ + const int degree = 7; + const RealScalar nodes[] = { 0.0254460438286207377369051579760744L, 0.1292344072003027800680676133596058L, + 0.2970774243113014165466967939615193L, 0.5000000000000000000000000000000000L, + 0.7029225756886985834533032060384807L, 0.8707655927996972199319323866403942L, + 0.9745539561713792622630948420239256L }; + const RealScalar weights[] = { 0.0647424830844348466353057163395410L, 0.1398526957446383339507338857118898L, + 0.1909150252525594724751848877444876L, 0.2089795918367346938775510204081633L, + 0.1909150252525594724751848877444876L, 0.1398526957446383339507338857118898L, + 0.0647424830844348466353057163395410L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade8(MatrixType& result, const MatrixType& T) +{ + const int degree = 8; + const RealScalar nodes[] = { 0.0198550717512318841582195657152635L, 0.1016667612931866302042230317620848L, + 0.2372337950418355070911304754053768L, 0.4082826787521750975302619288199080L, + 0.5917173212478249024697380711800920L, 0.7627662049581644929088695245946232L, + 0.8983332387068133697957769682379152L, 0.9801449282487681158417804342847365L }; + const RealScalar weights[] = { 0.0506142681451881295762656771549811L, 0.1111905172266872352721779972131204L, + 0.1568533229389436436689811009933007L, 0.1813418916891809914825752246385978L, + 0.1813418916891809914825752246385978L, 0.1568533229389436436689811009933007L, + 0.1111905172266872352721779972131204L, 0.0506142681451881295762656771549811L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade9(MatrixType& result, const MatrixType& T) +{ + const int degree = 9; + const RealScalar nodes[] = { 0.0159198802461869550822118985481636L, 0.0819844463366821028502851059651326L, + 0.1933142836497048013456489803292629L, 0.3378732882980955354807309926783317L, + 0.5000000000000000000000000000000000L, 0.6621267117019044645192690073216683L, + 0.8066857163502951986543510196707371L, 0.9180155536633178971497148940348674L, + 0.9840801197538130449177881014518364L }; + const RealScalar weights[] = { 0.0406371941807872059859460790552618L, 0.0903240803474287020292360156214564L, + 0.1303053482014677311593714347093164L, 0.1561735385200014200343152032922218L, + 0.1651196775006298815822625346434870L, 0.1561735385200014200343152032922218L, + 0.1303053482014677311593714347093164L, 0.0903240803474287020292360156214564L, + 0.0406371941807872059859460790552618L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade10(MatrixType& result, const MatrixType& T) +{ + const int degree = 10; + const RealScalar nodes[] = { 0.0130467357414141399610179939577740L, 0.0674683166555077446339516557882535L, + 0.1602952158504877968828363174425632L, 0.2833023029353764046003670284171079L, + 0.4255628305091843945575869994351400L, 0.5744371694908156054424130005648600L, + 0.7166976970646235953996329715828921L, 0.8397047841495122031171636825574368L, + 0.9325316833444922553660483442117465L, 0.9869532642585858600389820060422260L }; + const RealScalar weights[] = { 0.0333356721543440687967844049466659L, 0.0747256745752902965728881698288487L, + 0.1095431812579910219977674671140816L, 0.1346333596549981775456134607847347L, + 0.1477621123573764350869464973256692L, 0.1477621123573764350869464973256692L, + 0.1346333596549981775456134607847347L, 0.1095431812579910219977674671140816L, + 0.0747256745752902965728881698288487L, 0.0333356721543440687967844049466659L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +template <typename MatrixType> +void MatrixLogarithmAtomic<MatrixType>::computePade11(MatrixType& result, const MatrixType& T) +{ + const int degree = 11; + const RealScalar nodes[] = { 0.0108856709269715035980309994385713L, 0.0564687001159523504624211153480364L, + 0.1349239972129753379532918739844233L, 0.2404519353965940920371371652706952L, + 0.3652284220238275138342340072995692L, 0.5000000000000000000000000000000000L, + 0.6347715779761724861657659927004308L, 0.7595480646034059079628628347293048L, + 0.8650760027870246620467081260155767L, 0.9435312998840476495375788846519636L, + 0.9891143290730284964019690005614287L }; + const RealScalar weights[] = { 0.0278342835580868332413768602212743L, 0.0627901847324523123173471496119701L, + 0.0931451054638671257130488207158280L, 0.1165968822959952399592618524215876L, + 0.1314022722551233310903444349452546L, 0.1364625433889503153572417641681711L, + 0.1314022722551233310903444349452546L, 0.1165968822959952399592618524215876L, + 0.0931451054638671257130488207158280L, 0.0627901847324523123173471496119701L, + 0.0278342835580868332413768602212743L }; + eigen_assert(degree <= maxPadeDegree); + MatrixType TminusI = T - MatrixType::Identity(T.rows(), T.rows()); + result.setZero(T.rows(), T.rows()); + for (int k = 0; k < degree; ++k) + result += weights[k] * (MatrixType::Identity(T.rows(), T.rows()) + nodes[k] * TminusI) + .template triangularView<Upper>().solve(TminusI); +} + +/** \ingroup MatrixFunctions_Module + * + * \brief Proxy for the matrix logarithm of some matrix (expression). + * + * \tparam Derived Type of the argument to the matrix function. + * + * 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::log() and most of the time this is the only way it + * is used. + */ +template<typename Derived> class MatrixLogarithmReturnValue +: public ReturnByValue<MatrixLogarithmReturnValue<Derived> > +{ +public: + + typedef typename Derived::Scalar Scalar; + typedef typename Derived::Index Index; + + /** \brief Constructor. + * + * \param[in] A %Matrix (expression) forming the argument of the matrix logarithm. + */ + MatrixLogarithmReturnValue(const Derived& A) : m_A(A) { } + + /** \brief Compute the matrix logarithm. + * + * \param[out] result Logarithm of \p A, where \A is as specified in the constructor. + */ + template <typename ResultType> + inline void evalTo(ResultType& result) const + { + typedef typename Derived::PlainObject PlainObject; + typedef internal::traits<PlainObject> Traits; + static const int RowsAtCompileTime = Traits::RowsAtCompileTime; + static const int ColsAtCompileTime = Traits::ColsAtCompileTime; + static const int Options = PlainObject::Options; + typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar; + typedef Matrix<ComplexScalar, Dynamic, Dynamic, Options, RowsAtCompileTime, ColsAtCompileTime> DynMatrixType; + typedef MatrixLogarithmAtomic<DynMatrixType> AtomicType; + AtomicType atomic; + + const PlainObject Aevaluated = m_A.eval(); + MatrixFunction<PlainObject, AtomicType> mf(Aevaluated, atomic); + mf.compute(result); + } + + Index rows() const { return m_A.rows(); } + Index cols() const { return m_A.cols(); } + +private: + typename internal::nested<Derived>::type m_A; + + MatrixLogarithmReturnValue& operator=(const MatrixLogarithmReturnValue&); +}; + +namespace internal { + template<typename Derived> + struct traits<MatrixLogarithmReturnValue<Derived> > + { + typedef typename Derived::PlainObject ReturnType; + }; +} + + +/********** MatrixBase method **********/ + + +template <typename Derived> +const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const +{ + eigen_assert(rows() == cols()); + return MatrixLogarithmReturnValue<Derived>(derived()); +} + +} // end namespace Eigen + +#endif // EIGEN_MATRIX_LOGARITHM |