Squashed 'third_party/eigen/' content from commit 61d72f6
Change-Id: Iccc90fa0b55ab44037f018046d2fcffd90d9d025
git-subtree-dir: third_party/eigen
git-subtree-split: 61d72f6383cfa842868c53e30e087b0258177257
diff --git a/unsupported/Eigen/src/MatrixFunctions/CMakeLists.txt b/unsupported/Eigen/src/MatrixFunctions/CMakeLists.txt
new file mode 100644
index 0000000..cdde64d
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/CMakeLists.txt
@@ -0,0 +1,6 @@
+FILE(GLOB Eigen_MatrixFunctions_SRCS "*.h")
+
+INSTALL(FILES
+ ${Eigen_MatrixFunctions_SRCS}
+ DESTINATION ${INCLUDE_INSTALL_DIR}/unsupported/Eigen/src/MatrixFunctions COMPONENT Devel
+ )
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
new file mode 100644
index 0000000..6825a78
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h
@@ -0,0 +1,451 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009, 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2011 Chen-Pang He <jdh8@ms63.hinet.net>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_EXPONENTIAL
+#define EIGEN_MATRIX_EXPONENTIAL
+
+#include "StemFunction.h"
+
+namespace Eigen {
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing the matrix exponential.
+ * \tparam MatrixType type of the argument of the exponential,
+ * expected to be an instantiation of the Matrix class template.
+ */
+template <typename MatrixType>
+class MatrixExponential {
+
+ public:
+
+ /** \brief Constructor.
+ *
+ * The class stores a reference to \p M, so it should not be
+ * changed (or destroyed) before compute() is called.
+ *
+ * \param[in] M matrix whose exponential is to be computed.
+ */
+ MatrixExponential(const MatrixType &M);
+
+ /** \brief Computes the matrix exponential.
+ *
+ * \param[out] result the matrix exponential of \p M in the constructor.
+ */
+ template <typename ResultType>
+ void compute(ResultType &result);
+
+ private:
+
+ // Prevent copying
+ MatrixExponential(const MatrixExponential&);
+ MatrixExponential& operator=(const MatrixExponential&);
+
+ /** \brief Compute the (3,3)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade3(const MatrixType &A);
+
+ /** \brief Compute the (5,5)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade5(const MatrixType &A);
+
+ /** \brief Compute the (7,7)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade7(const MatrixType &A);
+
+ /** \brief Compute the (9,9)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade9(const MatrixType &A);
+
+ /** \brief Compute the (13,13)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade13(const MatrixType &A);
+
+ /** \brief Compute the (17,17)-Padé approximant to the exponential.
+ *
+ * After exit, \f$ (V+U)(V-U)^{-1} \f$ is the Padé
+ * approximant of \f$ \exp(A) \f$ around \f$ A = 0 \f$.
+ *
+ * This function activates only if your long double is double-double or quadruple.
+ *
+ * \param[in] A Argument of matrix exponential
+ */
+ void pade17(const MatrixType &A);
+
+ /** \brief Compute Padé approximant to the exponential.
+ *
+ * Computes \c m_U, \c m_V and \c m_squarings such that
+ * \f$ (V+U)(V-U)^{-1} \f$ is a Padé of
+ * \f$ \exp(2^{-\mbox{squarings}}M) \f$ around \f$ M = 0 \f$. The
+ * degree of the Padé approximant and the value of
+ * squarings are chosen such that the approximation error is no
+ * more than the round-off error.
+ *
+ * The argument of this function should correspond with the (real
+ * part of) the entries of \c m_M. It is used to select the
+ * correct implementation using overloading.
+ */
+ void computeUV(double);
+
+ /** \brief Compute Padé approximant to the exponential.
+ *
+ * \sa computeUV(double);
+ */
+ void computeUV(float);
+
+ /** \brief Compute Padé approximant to the exponential.
+ *
+ * \sa computeUV(double);
+ */
+ void computeUV(long double);
+
+ typedef typename internal::traits<MatrixType>::Scalar Scalar;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ typedef typename std::complex<RealScalar> ComplexScalar;
+
+ /** \brief Reference to matrix whose exponential is to be computed. */
+ typename internal::nested<MatrixType>::type m_M;
+
+ /** \brief Odd-degree terms in numerator of Padé approximant. */
+ MatrixType m_U;
+
+ /** \brief Even-degree terms in numerator of Padé approximant. */
+ MatrixType m_V;
+
+ /** \brief Used for temporary storage. */
+ MatrixType m_tmp1;
+
+ /** \brief Used for temporary storage. */
+ MatrixType m_tmp2;
+
+ /** \brief Identity matrix of the same size as \c m_M. */
+ MatrixType m_Id;
+
+ /** \brief Number of squarings required in the last step. */
+ int m_squarings;
+
+ /** \brief L1 norm of m_M. */
+ RealScalar m_l1norm;
+};
+
+template <typename MatrixType>
+MatrixExponential<MatrixType>::MatrixExponential(const MatrixType &M) :
+ m_M(M),
+ m_U(M.rows(),M.cols()),
+ m_V(M.rows(),M.cols()),
+ m_tmp1(M.rows(),M.cols()),
+ m_tmp2(M.rows(),M.cols()),
+ m_Id(MatrixType::Identity(M.rows(), M.cols())),
+ m_squarings(0),
+ m_l1norm(M.cwiseAbs().colwise().sum().maxCoeff())
+{
+ /* empty body */
+}
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixExponential<MatrixType>::compute(ResultType &result)
+{
+#if LDBL_MANT_DIG > 112 // rarely happens
+ if(sizeof(RealScalar) > 14) {
+ result = m_M.matrixFunction(StdStemFunctions<ComplexScalar>::exp);
+ return;
+ }
+#endif
+ computeUV(RealScalar());
+ m_tmp1 = m_U + m_V; // numerator of Pade approximant
+ m_tmp2 = -m_U + m_V; // denominator of Pade approximant
+ result = m_tmp2.partialPivLu().solve(m_tmp1);
+ for (int i=0; i<m_squarings; i++)
+ result *= result; // undo scaling by repeated squaring
+}
+
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade3(const MatrixType &A)
+{
+ const RealScalar b[] = {120., 60., 12., 1.};
+ m_tmp1.noalias() = A * A;
+ m_tmp2 = b[3]*m_tmp1 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_V = b[2]*m_tmp1 + b[0]*m_Id;
+}
+
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade5(const MatrixType &A)
+{
+ const RealScalar b[] = {30240., 15120., 3360., 420., 30., 1.};
+ MatrixType A2 = A * A;
+ m_tmp1.noalias() = A2 * A2;
+ m_tmp2 = b[5]*m_tmp1 + b[3]*A2 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_V = b[4]*m_tmp1 + b[2]*A2 + b[0]*m_Id;
+}
+
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade7(const MatrixType &A)
+{
+ const RealScalar b[] = {17297280., 8648640., 1995840., 277200., 25200., 1512., 56., 1.};
+ MatrixType A2 = A * A;
+ MatrixType A4 = A2 * A2;
+ m_tmp1.noalias() = A4 * A2;
+ m_tmp2 = b[7]*m_tmp1 + b[5]*A4 + b[3]*A2 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_V = b[6]*m_tmp1 + b[4]*A4 + b[2]*A2 + b[0]*m_Id;
+}
+
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade9(const MatrixType &A)
+{
+ const RealScalar b[] = {17643225600., 8821612800., 2075673600., 302702400., 30270240.,
+ 2162160., 110880., 3960., 90., 1.};
+ MatrixType A2 = A * A;
+ MatrixType A4 = A2 * A2;
+ MatrixType A6 = A4 * A2;
+ m_tmp1.noalias() = A6 * A2;
+ m_tmp2 = b[9]*m_tmp1 + b[7]*A6 + b[5]*A4 + b[3]*A2 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_V = b[8]*m_tmp1 + b[6]*A6 + b[4]*A4 + b[2]*A2 + b[0]*m_Id;
+}
+
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade13(const MatrixType &A)
+{
+ const RealScalar b[] = {64764752532480000., 32382376266240000., 7771770303897600.,
+ 1187353796428800., 129060195264000., 10559470521600., 670442572800.,
+ 33522128640., 1323241920., 40840800., 960960., 16380., 182., 1.};
+ MatrixType A2 = A * A;
+ MatrixType A4 = A2 * A2;
+ m_tmp1.noalias() = A4 * A2;
+ m_V = b[13]*m_tmp1 + b[11]*A4 + b[9]*A2; // used for temporary storage
+ m_tmp2.noalias() = m_tmp1 * m_V;
+ m_tmp2 += b[7]*m_tmp1 + b[5]*A4 + b[3]*A2 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_tmp2 = b[12]*m_tmp1 + b[10]*A4 + b[8]*A2;
+ m_V.noalias() = m_tmp1 * m_tmp2;
+ m_V += b[6]*m_tmp1 + b[4]*A4 + b[2]*A2 + b[0]*m_Id;
+}
+
+#if LDBL_MANT_DIG > 64
+template <typename MatrixType>
+EIGEN_STRONG_INLINE void MatrixExponential<MatrixType>::pade17(const MatrixType &A)
+{
+ const RealScalar b[] = {830034394580628357120000.L, 415017197290314178560000.L,
+ 100610229646136770560000.L, 15720348382208870400000.L,
+ 1774878043152614400000.L, 153822763739893248000.L, 10608466464820224000.L,
+ 595373117923584000.L, 27563570274240000.L, 1060137318240000.L,
+ 33924394183680.L, 899510451840.L, 19554575040.L, 341863200.L, 4651200.L,
+ 46512.L, 306.L, 1.L};
+ MatrixType A2 = A * A;
+ MatrixType A4 = A2 * A2;
+ MatrixType A6 = A4 * A2;
+ m_tmp1.noalias() = A4 * A4;
+ m_V = b[17]*m_tmp1 + b[15]*A6 + b[13]*A4 + b[11]*A2; // used for temporary storage
+ m_tmp2.noalias() = m_tmp1 * m_V;
+ m_tmp2 += b[9]*m_tmp1 + b[7]*A6 + b[5]*A4 + b[3]*A2 + b[1]*m_Id;
+ m_U.noalias() = A * m_tmp2;
+ m_tmp2 = b[16]*m_tmp1 + b[14]*A6 + b[12]*A4 + b[10]*A2;
+ m_V.noalias() = m_tmp1 * m_tmp2;
+ m_V += b[8]*m_tmp1 + b[6]*A6 + b[4]*A4 + b[2]*A2 + b[0]*m_Id;
+}
+#endif
+
+template <typename MatrixType>
+void MatrixExponential<MatrixType>::computeUV(float)
+{
+ using std::frexp;
+ using std::pow;
+ if (m_l1norm < 4.258730016922831e-001) {
+ pade3(m_M);
+ } else if (m_l1norm < 1.880152677804762e+000) {
+ pade5(m_M);
+ } else {
+ const float maxnorm = 3.925724783138660f;
+ frexp(m_l1norm / maxnorm, &m_squarings);
+ if (m_squarings < 0) m_squarings = 0;
+ MatrixType A = m_M / pow(Scalar(2), m_squarings);
+ pade7(A);
+ }
+}
+
+template <typename MatrixType>
+void MatrixExponential<MatrixType>::computeUV(double)
+{
+ using std::frexp;
+ using std::pow;
+ if (m_l1norm < 1.495585217958292e-002) {
+ pade3(m_M);
+ } else if (m_l1norm < 2.539398330063230e-001) {
+ pade5(m_M);
+ } else if (m_l1norm < 9.504178996162932e-001) {
+ pade7(m_M);
+ } else if (m_l1norm < 2.097847961257068e+000) {
+ pade9(m_M);
+ } else {
+ const double maxnorm = 5.371920351148152;
+ frexp(m_l1norm / maxnorm, &m_squarings);
+ if (m_squarings < 0) m_squarings = 0;
+ MatrixType A = m_M / pow(Scalar(2), m_squarings);
+ pade13(A);
+ }
+}
+
+template <typename MatrixType>
+void MatrixExponential<MatrixType>::computeUV(long double)
+{
+ using std::frexp;
+ using std::pow;
+#if LDBL_MANT_DIG == 53 // double precision
+ computeUV(double());
+#elif LDBL_MANT_DIG <= 64 // extended precision
+ if (m_l1norm < 4.1968497232266989671e-003L) {
+ pade3(m_M);
+ } else if (m_l1norm < 1.1848116734693823091e-001L) {
+ pade5(m_M);
+ } else if (m_l1norm < 5.5170388480686700274e-001L) {
+ pade7(m_M);
+ } else if (m_l1norm < 1.3759868875587845383e+000L) {
+ pade9(m_M);
+ } else {
+ const long double maxnorm = 4.0246098906697353063L;
+ frexp(m_l1norm / maxnorm, &m_squarings);
+ if (m_squarings < 0) m_squarings = 0;
+ MatrixType A = m_M / pow(Scalar(2), m_squarings);
+ pade13(A);
+ }
+#elif LDBL_MANT_DIG <= 106 // double-double
+ if (m_l1norm < 3.2787892205607026992947488108213e-005L) {
+ pade3(m_M);
+ } else if (m_l1norm < 6.4467025060072760084130906076332e-003L) {
+ pade5(m_M);
+ } else if (m_l1norm < 6.8988028496595374751374122881143e-002L) {
+ pade7(m_M);
+ } else if (m_l1norm < 2.7339737518502231741495857201670e-001L) {
+ pade9(m_M);
+ } else if (m_l1norm < 1.3203382096514474905666448850278e+000L) {
+ pade13(m_M);
+ } else {
+ const long double maxnorm = 3.2579440895405400856599663723517L;
+ frexp(m_l1norm / maxnorm, &m_squarings);
+ if (m_squarings < 0) m_squarings = 0;
+ MatrixType A = m_M / pow(Scalar(2), m_squarings);
+ pade17(A);
+ }
+#elif LDBL_MANT_DIG <= 112 // quadruple precison
+ if (m_l1norm < 1.639394610288918690547467954466970e-005L) {
+ pade3(m_M);
+ } else if (m_l1norm < 4.253237712165275566025884344433009e-003L) {
+ pade5(m_M);
+ } else if (m_l1norm < 5.125804063165764409885122032933142e-002L) {
+ pade7(m_M);
+ } else if (m_l1norm < 2.170000765161155195453205651889853e-001L) {
+ pade9(m_M);
+ } else if (m_l1norm < 1.125358383453143065081397882891878e+000L) {
+ pade13(m_M);
+ } else {
+ const long double maxnorm = 2.884233277829519311757165057717815L;
+ frexp(m_l1norm / maxnorm, &m_squarings);
+ if (m_squarings < 0) m_squarings = 0;
+ MatrixType A = m_M / pow(Scalar(2), m_squarings);
+ pade17(A);
+ }
+#else
+ // this case should be handled in compute()
+ eigen_assert(false && "Bug in MatrixExponential");
+#endif // LDBL_MANT_DIG
+}
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix exponential of some matrix (expression).
+ *
+ * \tparam Derived Type of the argument to the matrix exponential.
+ *
+ * This class holds the argument to the matrix exponential 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::exp() and most of the time this is the only way it is
+ * used.
+ */
+template<typename Derived> struct MatrixExponentialReturnValue
+: public ReturnByValue<MatrixExponentialReturnValue<Derived> >
+{
+ typedef typename Derived::Index Index;
+ public:
+ /** \brief Constructor.
+ *
+ * \param[in] src %Matrix (expression) forming the argument of the
+ * matrix exponential.
+ */
+ MatrixExponentialReturnValue(const Derived& src) : m_src(src) { }
+
+ /** \brief Compute the matrix exponential.
+ *
+ * \param[out] result the matrix exponential of \p src in the
+ * constructor.
+ */
+ template <typename ResultType>
+ inline void evalTo(ResultType& result) const
+ {
+ const typename Derived::PlainObject srcEvaluated = m_src.eval();
+ MatrixExponential<typename Derived::PlainObject> me(srcEvaluated);
+ me.compute(result);
+ }
+
+ Index rows() const { return m_src.rows(); }
+ Index cols() const { return m_src.cols(); }
+
+ protected:
+ const Derived& m_src;
+ private:
+ MatrixExponentialReturnValue& operator=(const MatrixExponentialReturnValue&);
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixExponentialReturnValue<Derived> >
+{
+ typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+template <typename Derived>
+const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const
+{
+ eigen_assert(rows() == cols());
+ return MatrixExponentialReturnValue<Derived>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_EXPONENTIAL
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
new file mode 100644
index 0000000..7d42664
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h
@@ -0,0 +1,591 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// 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_FUNCTION
+#define EIGEN_MATRIX_FUNCTION
+
+#include "StemFunction.h"
+#include "MatrixFunctionAtomic.h"
+
+
+namespace Eigen {
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing matrix functions.
+ * \tparam MatrixType type of the argument of the matrix function,
+ * expected to be an instantiation of the Matrix class template.
+ * \tparam AtomicType type for computing matrix function of atomic blocks.
+ * \tparam IsComplex used internally to select correct specialization.
+ *
+ * This class implements the Schur-Parlett algorithm for computing matrix functions. The spectrum of the
+ * matrix is divided in clustered of eigenvalues that lies close together. This class delegates the
+ * computation of the matrix function on every block corresponding to these clusters to an object of type
+ * \p AtomicType and uses these results to compute the matrix function of the whole matrix. The class
+ * \p AtomicType should have a \p compute() member function for computing the matrix function of a block.
+ *
+ * \sa class MatrixFunctionAtomic, class MatrixLogarithmAtomic
+ */
+template <typename MatrixType,
+ typename AtomicType,
+ int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+class MatrixFunction
+{
+ public:
+
+ /** \brief Constructor.
+ *
+ * \param[in] A argument of matrix function, should be a square matrix.
+ * \param[in] atomic class for computing matrix function of atomic blocks.
+ *
+ * The class stores references to \p A and \p atomic, so they should not be
+ * changed (or destroyed) before compute() is called.
+ */
+ MatrixFunction(const MatrixType& A, AtomicType& atomic);
+
+ /** \brief Compute the matrix function.
+ *
+ * \param[out] result the function \p f applied to \p A, as
+ * specified in the constructor.
+ *
+ * See MatrixBase::matrixFunction() for details on how this computation
+ * is implemented.
+ */
+ template <typename ResultType>
+ void compute(ResultType &result);
+};
+
+
+/** \internal \ingroup MatrixFunctions_Module
+ * \brief Partial specialization of MatrixFunction for real matrices
+ */
+template <typename MatrixType, typename AtomicType>
+class MatrixFunction<MatrixType, AtomicType, 0>
+{
+ private:
+
+ typedef internal::traits<MatrixType> Traits;
+ typedef typename Traits::Scalar Scalar;
+ static const int Rows = Traits::RowsAtCompileTime;
+ static const int Cols = Traits::ColsAtCompileTime;
+ static const int Options = MatrixType::Options;
+ static const int MaxRows = Traits::MaxRowsAtCompileTime;
+ static const int MaxCols = Traits::MaxColsAtCompileTime;
+
+ typedef std::complex<Scalar> ComplexScalar;
+ typedef Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols> ComplexMatrix;
+
+ public:
+
+ /** \brief Constructor.
+ *
+ * \param[in] A argument of matrix function, should be a square matrix.
+ * \param[in] atomic class for computing matrix function of atomic blocks.
+ */
+ MatrixFunction(const MatrixType& A, AtomicType& atomic) : m_A(A), m_atomic(atomic) { }
+
+ /** \brief Compute the matrix function.
+ *
+ * \param[out] result the function \p f applied to \p A, as
+ * specified in the constructor.
+ *
+ * This function converts the real matrix \c A to a complex matrix,
+ * uses MatrixFunction<MatrixType,1> and then converts the result back to
+ * a real matrix.
+ */
+ template <typename ResultType>
+ void compute(ResultType& result)
+ {
+ ComplexMatrix CA = m_A.template cast<ComplexScalar>();
+ ComplexMatrix Cresult;
+ MatrixFunction<ComplexMatrix, AtomicType> mf(CA, m_atomic);
+ mf.compute(Cresult);
+ result = Cresult.real();
+ }
+
+ private:
+ typename internal::nested<MatrixType>::type m_A; /**< \brief Reference to argument of matrix function. */
+ AtomicType& m_atomic; /**< \brief Class for computing matrix function of atomic blocks. */
+
+ MatrixFunction& operator=(const MatrixFunction&);
+};
+
+
+/** \internal \ingroup MatrixFunctions_Module
+ * \brief Partial specialization of MatrixFunction for complex matrices
+ */
+template <typename MatrixType, typename AtomicType>
+class MatrixFunction<MatrixType, AtomicType, 1>
+{
+ private:
+
+ typedef internal::traits<MatrixType> Traits;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::Index Index;
+ static const int RowsAtCompileTime = Traits::RowsAtCompileTime;
+ static const int ColsAtCompileTime = Traits::ColsAtCompileTime;
+ static const int Options = MatrixType::Options;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ typedef Matrix<Scalar, Traits::RowsAtCompileTime, 1> VectorType;
+ typedef Matrix<Index, Traits::RowsAtCompileTime, 1> IntVectorType;
+ typedef Matrix<Index, Dynamic, 1> DynamicIntVectorType;
+ typedef std::list<Scalar> Cluster;
+ typedef std::list<Cluster> ListOfClusters;
+ typedef Matrix<Scalar, Dynamic, Dynamic, Options, RowsAtCompileTime, ColsAtCompileTime> DynMatrixType;
+
+ public:
+
+ MatrixFunction(const MatrixType& A, AtomicType& atomic);
+ template <typename ResultType> void compute(ResultType& result);
+
+ private:
+
+ void computeSchurDecomposition();
+ void partitionEigenvalues();
+ typename ListOfClusters::iterator findCluster(Scalar key);
+ void computeClusterSize();
+ void computeBlockStart();
+ void constructPermutation();
+ void permuteSchur();
+ void swapEntriesInSchur(Index index);
+ void computeBlockAtomic();
+ Block<MatrixType> block(MatrixType& A, Index i, Index j);
+ void computeOffDiagonal();
+ DynMatrixType solveTriangularSylvester(const DynMatrixType& A, const DynMatrixType& B, const DynMatrixType& C);
+
+ typename internal::nested<MatrixType>::type m_A; /**< \brief Reference to argument of matrix function. */
+ AtomicType& m_atomic; /**< \brief Class for computing matrix function of atomic blocks. */
+ MatrixType m_T; /**< \brief Triangular part of Schur decomposition */
+ MatrixType m_U; /**< \brief Unitary part of Schur decomposition */
+ MatrixType m_fT; /**< \brief %Matrix function applied to #m_T */
+ ListOfClusters m_clusters; /**< \brief Partition of eigenvalues into clusters of ei'vals "close" to each other */
+ DynamicIntVectorType m_eivalToCluster; /**< \brief m_eivalToCluster[i] = j means i-th ei'val is in j-th cluster */
+ DynamicIntVectorType m_clusterSize; /**< \brief Number of eigenvalues in each clusters */
+ DynamicIntVectorType m_blockStart; /**< \brief Row index at which block corresponding to i-th cluster starts */
+ IntVectorType m_permutation; /**< \brief Permutation which groups ei'vals in the same cluster together */
+
+ /** \brief Maximum distance allowed between eigenvalues to be considered "close".
+ *
+ * This is morally a \c static \c const \c Scalar, but only
+ * integers can be static constant class members in C++. The
+ * separation constant is set to 0.1, a value taken from the
+ * paper by Davies and Higham. */
+ static const RealScalar separation() { return static_cast<RealScalar>(0.1); }
+
+ MatrixFunction& operator=(const MatrixFunction&);
+};
+
+/** \brief Constructor.
+ *
+ * \param[in] A argument of matrix function, should be a square matrix.
+ * \param[in] atomic class for computing matrix function of atomic blocks.
+ */
+template <typename MatrixType, typename AtomicType>
+MatrixFunction<MatrixType,AtomicType,1>::MatrixFunction(const MatrixType& A, AtomicType& atomic)
+ : m_A(A), m_atomic(atomic)
+{
+ /* empty body */
+}
+
+/** \brief Compute the matrix function.
+ *
+ * \param[out] result the function \p f applied to \p A, as
+ * specified in the constructor.
+ */
+template <typename MatrixType, typename AtomicType>
+template <typename ResultType>
+void MatrixFunction<MatrixType,AtomicType,1>::compute(ResultType& result)
+{
+ computeSchurDecomposition();
+ partitionEigenvalues();
+ computeClusterSize();
+ computeBlockStart();
+ constructPermutation();
+ permuteSchur();
+ computeBlockAtomic();
+ computeOffDiagonal();
+ result = m_U * (m_fT.template triangularView<Upper>() * m_U.adjoint());
+}
+
+/** \brief Store the Schur decomposition of #m_A in #m_T and #m_U */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::computeSchurDecomposition()
+{
+ const ComplexSchur<MatrixType> schurOfA(m_A);
+ m_T = schurOfA.matrixT();
+ m_U = schurOfA.matrixU();
+}
+
+/** \brief Partition eigenvalues in clusters of ei'vals close to each other
+ *
+ * This function computes #m_clusters. This is a partition of the
+ * eigenvalues of #m_T in clusters, such that
+ * # Any eigenvalue in a certain cluster is at most separation() away
+ * from another eigenvalue in the same cluster.
+ * # The distance between two eigenvalues in different clusters is
+ * more than separation().
+ * The implementation follows Algorithm 4.1 in the paper of Davies
+ * and Higham.
+ */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::partitionEigenvalues()
+{
+ using std::abs;
+ const Index rows = m_T.rows();
+ VectorType diag = m_T.diagonal(); // contains eigenvalues of A
+
+ for (Index i=0; i<rows; ++i) {
+ // Find set containing diag(i), adding a new set if necessary
+ typename ListOfClusters::iterator qi = findCluster(diag(i));
+ if (qi == m_clusters.end()) {
+ Cluster l;
+ l.push_back(diag(i));
+ m_clusters.push_back(l);
+ qi = m_clusters.end();
+ --qi;
+ }
+
+ // Look for other element to add to the set
+ for (Index j=i+1; j<rows; ++j) {
+ if (abs(diag(j) - diag(i)) <= separation() && std::find(qi->begin(), qi->end(), diag(j)) == qi->end()) {
+ typename ListOfClusters::iterator qj = findCluster(diag(j));
+ if (qj == m_clusters.end()) {
+ qi->push_back(diag(j));
+ } else {
+ qi->insert(qi->end(), qj->begin(), qj->end());
+ m_clusters.erase(qj);
+ }
+ }
+ }
+ }
+}
+
+/** \brief Find cluster in #m_clusters containing some value
+ * \param[in] key Value to find
+ * \returns Iterator to cluster containing \c key, or
+ * \c m_clusters.end() if no cluster in m_clusters contains \c key.
+ */
+template <typename MatrixType, typename AtomicType>
+typename MatrixFunction<MatrixType,AtomicType,1>::ListOfClusters::iterator MatrixFunction<MatrixType,AtomicType,1>::findCluster(Scalar key)
+{
+ typename Cluster::iterator j;
+ for (typename ListOfClusters::iterator i = m_clusters.begin(); i != m_clusters.end(); ++i) {
+ j = std::find(i->begin(), i->end(), key);
+ if (j != i->end())
+ return i;
+ }
+ return m_clusters.end();
+}
+
+/** \brief Compute #m_clusterSize and #m_eivalToCluster using #m_clusters */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::computeClusterSize()
+{
+ const Index rows = m_T.rows();
+ VectorType diag = m_T.diagonal();
+ const Index numClusters = static_cast<Index>(m_clusters.size());
+
+ m_clusterSize.setZero(numClusters);
+ m_eivalToCluster.resize(rows);
+ Index clusterIndex = 0;
+ for (typename ListOfClusters::const_iterator cluster = m_clusters.begin(); cluster != m_clusters.end(); ++cluster) {
+ for (Index i = 0; i < diag.rows(); ++i) {
+ if (std::find(cluster->begin(), cluster->end(), diag(i)) != cluster->end()) {
+ ++m_clusterSize[clusterIndex];
+ m_eivalToCluster[i] = clusterIndex;
+ }
+ }
+ ++clusterIndex;
+ }
+}
+
+/** \brief Compute #m_blockStart using #m_clusterSize */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::computeBlockStart()
+{
+ m_blockStart.resize(m_clusterSize.rows());
+ m_blockStart(0) = 0;
+ for (Index i = 1; i < m_clusterSize.rows(); i++) {
+ m_blockStart(i) = m_blockStart(i-1) + m_clusterSize(i-1);
+ }
+}
+
+/** \brief Compute #m_permutation using #m_eivalToCluster and #m_blockStart */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::constructPermutation()
+{
+ DynamicIntVectorType indexNextEntry = m_blockStart;
+ m_permutation.resize(m_T.rows());
+ for (Index i = 0; i < m_T.rows(); i++) {
+ Index cluster = m_eivalToCluster[i];
+ m_permutation[i] = indexNextEntry[cluster];
+ ++indexNextEntry[cluster];
+ }
+}
+
+/** \brief Permute Schur decomposition in #m_U and #m_T according to #m_permutation */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::permuteSchur()
+{
+ IntVectorType p = m_permutation;
+ for (Index i = 0; i < p.rows() - 1; i++) {
+ Index j;
+ for (j = i; j < p.rows(); j++) {
+ if (p(j) == i) break;
+ }
+ eigen_assert(p(j) == i);
+ for (Index k = j-1; k >= i; k--) {
+ swapEntriesInSchur(k);
+ std::swap(p.coeffRef(k), p.coeffRef(k+1));
+ }
+ }
+}
+
+/** \brief Swap rows \a index and \a index+1 in Schur decomposition in #m_U and #m_T */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::swapEntriesInSchur(Index index)
+{
+ JacobiRotation<Scalar> rotation;
+ rotation.makeGivens(m_T(index, index+1), m_T(index+1, index+1) - m_T(index, index));
+ m_T.applyOnTheLeft(index, index+1, rotation.adjoint());
+ m_T.applyOnTheRight(index, index+1, rotation);
+ m_U.applyOnTheRight(index, index+1, rotation);
+}
+
+/** \brief Compute block diagonal part of #m_fT.
+ *
+ * This routine computes the matrix function applied to the block diagonal part of #m_T, with the blocking
+ * given by #m_blockStart. The matrix function of each diagonal block is computed by #m_atomic. The
+ * off-diagonal parts of #m_fT are set to zero.
+ */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::computeBlockAtomic()
+{
+ m_fT.resize(m_T.rows(), m_T.cols());
+ m_fT.setZero();
+ for (Index i = 0; i < m_clusterSize.rows(); ++i) {
+ block(m_fT, i, i) = m_atomic.compute(block(m_T, i, i));
+ }
+}
+
+/** \brief Return block of matrix according to blocking given by #m_blockStart */
+template <typename MatrixType, typename AtomicType>
+Block<MatrixType> MatrixFunction<MatrixType,AtomicType,1>::block(MatrixType& A, Index i, Index j)
+{
+ return A.block(m_blockStart(i), m_blockStart(j), m_clusterSize(i), m_clusterSize(j));
+}
+
+/** \brief Compute part of #m_fT above block diagonal.
+ *
+ * This routine assumes that the block diagonal part of #m_fT (which
+ * equals the matrix function applied to #m_T) has already been computed and computes
+ * the part above the block diagonal. The part below the diagonal is
+ * zero, because #m_T is upper triangular.
+ */
+template <typename MatrixType, typename AtomicType>
+void MatrixFunction<MatrixType,AtomicType,1>::computeOffDiagonal()
+{
+ for (Index diagIndex = 1; diagIndex < m_clusterSize.rows(); diagIndex++) {
+ for (Index blockIndex = 0; blockIndex < m_clusterSize.rows() - diagIndex; blockIndex++) {
+ // compute (blockIndex, blockIndex+diagIndex) block
+ DynMatrixType A = block(m_T, blockIndex, blockIndex);
+ DynMatrixType B = -block(m_T, blockIndex+diagIndex, blockIndex+diagIndex);
+ DynMatrixType C = block(m_fT, blockIndex, blockIndex) * block(m_T, blockIndex, blockIndex+diagIndex);
+ C -= block(m_T, blockIndex, blockIndex+diagIndex) * block(m_fT, blockIndex+diagIndex, blockIndex+diagIndex);
+ for (Index k = blockIndex + 1; k < blockIndex + diagIndex; k++) {
+ C += block(m_fT, blockIndex, k) * block(m_T, k, blockIndex+diagIndex);
+ C -= block(m_T, blockIndex, k) * block(m_fT, k, blockIndex+diagIndex);
+ }
+ block(m_fT, blockIndex, blockIndex+diagIndex) = solveTriangularSylvester(A, B, C);
+ }
+ }
+}
+
+/** \brief Solve a triangular Sylvester equation AX + XB = C
+ *
+ * \param[in] A the matrix A; should be square and upper triangular
+ * \param[in] B the matrix B; should be square and upper triangular
+ * \param[in] C the matrix C; should have correct size.
+ *
+ * \returns the solution X.
+ *
+ * If A is m-by-m and B is n-by-n, then both C and X are m-by-n.
+ * The (i,j)-th component of the Sylvester equation is
+ * \f[
+ * \sum_{k=i}^m A_{ik} X_{kj} + \sum_{k=1}^j X_{ik} B_{kj} = C_{ij}.
+ * \f]
+ * This can be re-arranged to yield:
+ * \f[
+ * X_{ij} = \frac{1}{A_{ii} + B_{jj}} \Bigl( C_{ij}
+ * - \sum_{k=i+1}^m A_{ik} X_{kj} - \sum_{k=1}^{j-1} X_{ik} B_{kj} \Bigr).
+ * \f]
+ * It is assumed that A and B are such that the numerator is never
+ * zero (otherwise the Sylvester equation does not have a unique
+ * solution). In that case, these equations can be evaluated in the
+ * order \f$ i=m,\ldots,1 \f$ and \f$ j=1,\ldots,n \f$.
+ */
+template <typename MatrixType, typename AtomicType>
+typename MatrixFunction<MatrixType,AtomicType,1>::DynMatrixType MatrixFunction<MatrixType,AtomicType,1>::solveTriangularSylvester(
+ const DynMatrixType& A,
+ const DynMatrixType& B,
+ const DynMatrixType& C)
+{
+ eigen_assert(A.rows() == A.cols());
+ eigen_assert(A.isUpperTriangular());
+ eigen_assert(B.rows() == B.cols());
+ eigen_assert(B.isUpperTriangular());
+ eigen_assert(C.rows() == A.rows());
+ eigen_assert(C.cols() == B.rows());
+
+ Index m = A.rows();
+ Index n = B.rows();
+ DynMatrixType X(m, n);
+
+ for (Index i = m - 1; i >= 0; --i) {
+ for (Index j = 0; j < n; ++j) {
+
+ // Compute AX = \sum_{k=i+1}^m A_{ik} X_{kj}
+ Scalar AX;
+ if (i == m - 1) {
+ AX = 0;
+ } else {
+ Matrix<Scalar,1,1> AXmatrix = A.row(i).tail(m-1-i) * X.col(j).tail(m-1-i);
+ AX = AXmatrix(0,0);
+ }
+
+ // Compute XB = \sum_{k=1}^{j-1} X_{ik} B_{kj}
+ Scalar XB;
+ if (j == 0) {
+ XB = 0;
+ } else {
+ Matrix<Scalar,1,1> XBmatrix = X.row(i).head(j) * B.col(j).head(j);
+ XB = XBmatrix(0,0);
+ }
+
+ X(i,j) = (C(i,j) - AX - XB) / (A(i,i) + B(j,j));
+ }
+ }
+ return X;
+}
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix function 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::matrixFunction() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template<typename Derived> class MatrixFunctionReturnValue
+: public ReturnByValue<MatrixFunctionReturnValue<Derived> >
+{
+ public:
+
+ typedef typename Derived::Scalar Scalar;
+ typedef typename Derived::Index Index;
+ typedef typename internal::stem_function<Scalar>::type StemFunction;
+
+ /** \brief Constructor.
+ *
+ * \param[in] A %Matrix (expression) forming the argument of the
+ * matrix function.
+ * \param[in] f Stem function for matrix function under consideration.
+ */
+ MatrixFunctionReturnValue(const Derived& A, StemFunction f) : m_A(A), m_f(f) { }
+
+ /** \brief Compute the matrix function.
+ *
+ * \param[out] result \p f applied to \p A, where \p f and \p A
+ * are as 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 MatrixFunctionAtomic<DynMatrixType> AtomicType;
+ AtomicType atomic(m_f);
+
+ 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;
+ StemFunction *m_f;
+
+ MatrixFunctionReturnValue& operator=(const MatrixFunctionReturnValue&);
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixFunctionReturnValue<Derived> >
+{
+ typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+
+/********** MatrixBase methods **********/
+
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const
+{
+ eigen_assert(rows() == cols());
+ return MatrixFunctionReturnValue<Derived>(derived(), f);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const
+{
+ eigen_assert(rows() == cols());
+ typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+ return MatrixFunctionReturnValue<Derived>(derived(), StdStemFunctions<ComplexScalar>::sin);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const
+{
+ eigen_assert(rows() == cols());
+ typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+ return MatrixFunctionReturnValue<Derived>(derived(), StdStemFunctions<ComplexScalar>::cos);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const
+{
+ eigen_assert(rows() == cols());
+ typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+ return MatrixFunctionReturnValue<Derived>(derived(), StdStemFunctions<ComplexScalar>::sinh);
+}
+
+template <typename Derived>
+const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const
+{
+ eigen_assert(rows() == cols());
+ typedef typename internal::stem_function<Scalar>::ComplexScalar ComplexScalar;
+ return MatrixFunctionReturnValue<Derived>(derived(), StdStemFunctions<ComplexScalar>::cosh);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
new file mode 100644
index 0000000..efe332c
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
@@ -0,0 +1,131 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// 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_FUNCTION_ATOMIC
+#define EIGEN_MATRIX_FUNCTION_ATOMIC
+
+namespace Eigen {
+
+/** \ingroup MatrixFunctions_Module
+ * \class MatrixFunctionAtomic
+ * \brief Helper class for computing matrix functions of atomic matrices.
+ *
+ * \internal
+ * Here, an atomic matrix is a triangular matrix whose diagonal
+ * entries are close to each other.
+ */
+template <typename MatrixType>
+class MatrixFunctionAtomic
+{
+ 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
+ * \param[in] f matrix function to compute.
+ */
+ MatrixFunctionAtomic(StemFunction f) : m_f(f) { }
+
+ /** \brief Compute matrix function of atomic matrix
+ * \param[in] A argument of matrix function, should be upper triangular and atomic
+ * \returns f(A), the matrix function evaluated at the given matrix
+ */
+ MatrixType compute(const MatrixType& A);
+
+ private:
+
+ // Prevent copying
+ MatrixFunctionAtomic(const MatrixFunctionAtomic&);
+ MatrixFunctionAtomic& operator=(const MatrixFunctionAtomic&);
+
+ void computeMu();
+ bool taylorConverged(Index s, const MatrixType& F, const MatrixType& Fincr, const MatrixType& P);
+
+ /** \brief Pointer to scalar function */
+ StemFunction* m_f;
+
+ /** \brief Size of matrix function */
+ Index m_Arows;
+
+ /** \brief Mean of eigenvalues */
+ Scalar m_avgEival;
+
+ /** \brief Argument shifted by mean of eigenvalues */
+ MatrixType m_Ashifted;
+
+ /** \brief Constant used to determine whether Taylor series has converged */
+ RealScalar m_mu;
+};
+
+template <typename MatrixType>
+MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
+{
+ // TODO: Use that A is upper triangular
+ m_Arows = A.rows();
+ m_avgEival = A.trace() / Scalar(RealScalar(m_Arows));
+ m_Ashifted = A - m_avgEival * MatrixType::Identity(m_Arows, m_Arows);
+ computeMu();
+ MatrixType F = m_f(m_avgEival, 0) * MatrixType::Identity(m_Arows, m_Arows);
+ MatrixType P = m_Ashifted;
+ MatrixType Fincr;
+ for (Index s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary
+ Fincr = m_f(m_avgEival, static_cast<int>(s)) * P;
+ F += Fincr;
+ P = Scalar(RealScalar(1.0/(s + 1))) * P * m_Ashifted;
+ if (taylorConverged(s, F, Fincr, P)) {
+ return F;
+ }
+ }
+ eigen_assert("Taylor series does not converge" && 0);
+ return F;
+}
+
+/** \brief Compute \c m_mu. */
+template <typename MatrixType>
+void MatrixFunctionAtomic<MatrixType>::computeMu()
+{
+ const MatrixType N = MatrixType::Identity(m_Arows, m_Arows) - m_Ashifted;
+ VectorType e = VectorType::Ones(m_Arows);
+ N.template triangularView<Upper>().solveInPlace(e);
+ m_mu = e.cwiseAbs().maxCoeff();
+}
+
+/** \brief Determine whether Taylor series has converged */
+template <typename MatrixType>
+bool MatrixFunctionAtomic<MatrixType>::taylorConverged(Index s, const MatrixType& F,
+ const MatrixType& Fincr, const MatrixType& P)
+{
+ const Index n = F.rows();
+ const RealScalar F_norm = F.cwiseAbs().rowwise().sum().maxCoeff();
+ const RealScalar Fincr_norm = Fincr.cwiseAbs().rowwise().sum().maxCoeff();
+ if (Fincr_norm < NumTraits<Scalar>::epsilon() * F_norm) {
+ RealScalar delta = 0;
+ RealScalar rfactorial = 1;
+ for (Index r = 0; r < n; r++) {
+ RealScalar mx = 0;
+ for (Index i = 0; i < n; i++)
+ mx = (std::max)(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
+ if (r != 0)
+ rfactorial *= RealScalar(r);
+ delta = (std::max)(delta, mx / rfactorial);
+ }
+ const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
+ if (m_mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)
+ return true;
+ }
+ return false;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION_ATOMIC
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h
new file mode 100644
index 0000000..c744fc0
--- /dev/null
+++ b/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
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
new file mode 100644
index 0000000..78a307e
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
@@ -0,0 +1,508 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012, 2013 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_POWER
+#define EIGEN_MATRIX_POWER
+
+namespace Eigen {
+
+template<typename MatrixType> class MatrixPower;
+
+template<typename MatrixType>
+class MatrixPowerRetval : public ReturnByValue< MatrixPowerRetval<MatrixType> >
+{
+ public:
+ typedef typename MatrixType::RealScalar RealScalar;
+ typedef typename MatrixType::Index Index;
+
+ MatrixPowerRetval(MatrixPower<MatrixType>& pow, RealScalar p) : m_pow(pow), 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;
+ MatrixPowerRetval& operator=(const MatrixPowerRetval&);
+};
+
+template<typename MatrixType>
+class MatrixPowerAtomic
+{
+ private:
+ enum {
+ RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+ MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
+ };
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+ typedef std::complex<RealScalar> ComplexScalar;
+ typedef typename MatrixType::Index Index;
+ typedef Array<Scalar, RowsAtCompileTime, 1, ColMajor, MaxRowsAtCompileTime> ArrayType;
+
+ const MatrixType& m_A;
+ RealScalar m_p;
+
+ void computePade(int degree, const MatrixType& IminusT, MatrixType& res) const;
+ void compute2x2(MatrixType& res, RealScalar p) const;
+ void computeBig(MatrixType& res) const;
+ static int getPadeDegree(float normIminusT);
+ static int getPadeDegree(double normIminusT);
+ static int getPadeDegree(long double normIminusT);
+ static ComplexScalar computeSuperDiag(const ComplexScalar&, const ComplexScalar&, RealScalar p);
+ static RealScalar computeSuperDiag(RealScalar, RealScalar, RealScalar p);
+
+ public:
+ MatrixPowerAtomic(const MatrixType& T, RealScalar p);
+ void compute(MatrixType& res) const;
+};
+
+template<typename MatrixType>
+MatrixPowerAtomic<MatrixType>::MatrixPowerAtomic(const MatrixType& T, RealScalar p) :
+ m_A(T), m_p(p)
+{ eigen_assert(T.rows() == T.cols()); }
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute(MatrixType& res) const
+{
+ res.resizeLike(m_A);
+ switch (m_A.rows()) {
+ case 0:
+ break;
+ case 1:
+ res(0,0) = std::pow(m_A(0,0), m_p);
+ break;
+ case 2:
+ compute2x2(res, m_p);
+ break;
+ default:
+ computeBig(res);
+ }
+}
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computePade(int degree, const MatrixType& IminusT, MatrixType& res) const
+{
+ int i = degree<<1;
+ res = (m_p-degree) / ((i-1)<<1) * IminusT;
+ for (--i; i; --i) {
+ res = (MatrixType::Identity(IminusT.rows(), IminusT.cols()) + res).template triangularView<Upper>()
+ .solve((i==1 ? -m_p : i&1 ? (-m_p-(i>>1))/(i<<1) : (m_p-(i>>1))/((i-1)<<1)) * IminusT).eval();
+ }
+ res += MatrixType::Identity(IminusT.rows(), IminusT.cols());
+}
+
+// This function assumes that res has the correct size (see bug 614)
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::compute2x2(MatrixType& res, RealScalar p) const
+{
+ using std::abs;
+ using std::pow;
+
+ res.coeffRef(0,0) = pow(m_A.coeff(0,0), p);
+
+ for (Index i=1; i < m_A.cols(); ++i) {
+ res.coeffRef(i,i) = pow(m_A.coeff(i,i), p);
+ if (m_A.coeff(i-1,i-1) == m_A.coeff(i,i))
+ res.coeffRef(i-1,i) = p * pow(m_A.coeff(i,i), p-1);
+ else if (2*abs(m_A.coeff(i-1,i-1)) < abs(m_A.coeff(i,i)) || 2*abs(m_A.coeff(i,i)) < abs(m_A.coeff(i-1,i-1)))
+ res.coeffRef(i-1,i) = (res.coeff(i,i)-res.coeff(i-1,i-1)) / (m_A.coeff(i,i)-m_A.coeff(i-1,i-1));
+ else
+ res.coeffRef(i-1,i) = computeSuperDiag(m_A.coeff(i,i), m_A.coeff(i-1,i-1), p);
+ res.coeffRef(i-1,i) *= m_A.coeff(i-1,i);
+ }
+}
+
+template<typename MatrixType>
+void MatrixPowerAtomic<MatrixType>::computeBig(MatrixType& res) 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-1L: // double-double
+ 9.134603732914548552537150753385375e-2L; // quadruple precision
+ MatrixType IminusT, sqrtT, T = m_A.template triangularView<Upper>();
+ RealScalar normIminusT;
+ int degree, degree2, numberOfSquareRoots = 0;
+ bool hasExtraSquareRoot = false;
+
+ /* FIXME
+ * For singular T, norm(I - T) >= 1 but maxNormForPade < 1, leads to infinite
+ * loop. We should move 0 eigenvalues to bottom right corner. We need not
+ * worry about tiny values (e.g. 1e-300) because they will reach 1 if
+ * repetitively sqrt'ed.
+ *
+ * If the 0 eigenvalues are semisimple, they can form a 0 matrix at the
+ * bottom right corner.
+ *
+ * [ T A ]^p [ T^p (T^-1 T^p A) ]
+ * [ ] = [ ]
+ * [ 0 0 ] [ 0 0 ]
+ */
+ for (Index i=0; i < m_A.cols(); ++i)
+ eigen_assert(m_A(i,i) != RealScalar(0));
+
+ while (true) {
+ IminusT = MatrixType::Identity(m_A.rows(), m_A.cols()) - T;
+ normIminusT = IminusT.cwiseAbs().colwise().sum().maxCoeff();
+ if (normIminusT < maxNormForPade) {
+ degree = getPadeDegree(normIminusT);
+ degree2 = getPadeDegree(normIminusT/2);
+ if (degree - degree2 <= 1 || hasExtraSquareRoot)
+ break;
+ hasExtraSquareRoot = true;
+ }
+ MatrixSquareRootTriangular<MatrixType>(T).compute(sqrtT);
+ T = sqrtT.template triangularView<Upper>();
+ ++numberOfSquareRoots;
+ }
+ computePade(degree, IminusT, res);
+
+ for (; numberOfSquareRoots; --numberOfSquareRoots) {
+ compute2x2(res, std::ldexp(m_p, -numberOfSquareRoots));
+ res = res.template triangularView<Upper>() * res;
+ }
+ compute2x2(res, m_p);
+}
+
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(float normIminusT)
+{
+ const float maxNormForPade[] = { 2.8064004e-1f /* degree = 3 */ , 4.3386528e-1f };
+ int degree = 3;
+ for (; degree <= 4; ++degree)
+ if (normIminusT <= maxNormForPade[degree - 3])
+ break;
+ return degree;
+}
+
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(double normIminusT)
+{
+ 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)
+ if (normIminusT <= maxNormForPade[degree - 3])
+ break;
+ return degree;
+}
+
+template<typename MatrixType>
+inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(long double normIminusT)
+{
+#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 };
+#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 */ ,
+ 1.0007161601787493236741409687186e-3L, 4.7069769360887572939882574746264e-3L, 1.3220386624169159689406653101695e-2L,
+ 2.8063482381631737920612944054906e-2L, 4.9625993951953473052385361085058e-2L, 7.7367040706027886224557538328171e-2L,
+ 1.1016843812851143391275867258512e-1L };
+#else
+ const int maxPadeDegree = 10;
+ const double maxNormForPade[] = { 5.524506147036624377378713555116378e-5L /* degree = 3 */ ,
+ 6.640600568157479679823602193345995e-4L, 3.227716520106894279249709728084626e-3L,
+ 9.619593944683432960546978734646284e-3L, 2.134595382433742403911124458161147e-2L,
+ 3.908166513900489428442993794761185e-2L, 6.266780814639442865832535460550138e-2L,
+ 9.134603732914548552537150753385375e-2L };
+#endif
+ int degree = 3;
+ for (; degree <= maxPadeDegree; ++degree)
+ if (normIminusT <= maxNormForPade[degree - 3])
+ break;
+ return degree;
+}
+
+template<typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::ComplexScalar
+MatrixPowerAtomic<MatrixType>::computeSuperDiag(const ComplexScalar& curr, const ComplexScalar& prev, RealScalar p)
+{
+ ComplexScalar logCurr = std::log(curr);
+ ComplexScalar logPrev = std::log(prev);
+ int unwindingNumber = std::ceil((numext::imag(logCurr - logPrev) - M_PI) / (2*M_PI));
+ ComplexScalar w = numext::atanh2(curr - prev, curr + prev) + ComplexScalar(0, M_PI*unwindingNumber);
+ return RealScalar(2) * std::exp(RealScalar(0.5) * p * (logCurr + logPrev)) * std::sinh(p * w) / (curr - prev);
+}
+
+template<typename MatrixType>
+inline typename MatrixPowerAtomic<MatrixType>::RealScalar
+MatrixPowerAtomic<MatrixType>::computeSuperDiag(RealScalar curr, RealScalar prev, RealScalar p)
+{
+ RealScalar w = numext::atanh2(curr - prev, curr + prev);
+ return 2 * std::exp(p * (std::log(curr) + std::log(prev)) / 2) * std::sinh(p * w) / (curr - prev);
+}
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Class for computing matrix powers.
+ *
+ * \tparam MatrixType type of the base, expected to be an instantiation
+ * of the Matrix class template.
+ *
+ * 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 MatrixType>
+class MatrixPower
+{
+ private:
+ enum {
+ RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+ ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+ MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+ MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+ };
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+ typedef typename MatrixType::Index Index;
+
+ public:
+ /**
+ * \brief Constructor.
+ *
+ * \param[in] A the base of the matrix power.
+ *
+ * The class stores a reference to A, so it should not be changed
+ * (or destroyed) before evaluation.
+ */
+ explicit MatrixPower(const MatrixType& A) : m_A(A), m_conditionNumber(0)
+ { eigen_assert(A.rows() == A.cols()); }
+
+ /**
+ * \brief Returns the matrix power.
+ *
+ * \param[in] p exponent, a real scalar.
+ * \return The expression \f$ A^p \f$, where A is specified in the
+ * constructor.
+ */
+ const MatrixPowerRetval<MatrixType> operator()(RealScalar p)
+ { return MatrixPowerRetval<MatrixType>(*this, p); }
+
+ /**
+ * \brief Compute the matrix power.
+ *
+ * \param[in] p exponent, a real scalar.
+ * \param[out] res \f$ A^p \f$ where A is specified in the
+ * constructor.
+ */
+ template<typename ResultType>
+ void compute(ResultType& res, RealScalar p);
+
+ Index rows() const { return m_A.rows(); }
+ Index cols() const { return m_A.cols(); }
+
+ private:
+ typedef std::complex<RealScalar> ComplexScalar;
+ typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, MatrixType::Options,
+ MaxRowsAtCompileTime, MaxColsAtCompileTime> ComplexMatrix;
+
+ typename MatrixType::Nested m_A;
+ MatrixType m_tmp;
+ ComplexMatrix m_T, m_U, m_fT;
+ RealScalar m_conditionNumber;
+
+ RealScalar modfAndInit(RealScalar, RealScalar*);
+
+ template<typename ResultType>
+ void computeIntPower(ResultType&, RealScalar);
+
+ template<typename ResultType>
+ void computeFracPower(ResultType&, RealScalar);
+
+ template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+ static void revertSchur(
+ Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+ const ComplexMatrix& T,
+ const ComplexMatrix& U);
+
+ template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+ static void revertSchur(
+ Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+ const ComplexMatrix& T,
+ const ComplexMatrix& U);
+};
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::compute(ResultType& res, RealScalar p)
+{
+ switch (cols()) {
+ case 0:
+ break;
+ case 1:
+ res(0,0) = std::pow(m_A.coeff(0,0), p);
+ break;
+ default:
+ RealScalar intpart, x = modfAndInit(p, &intpart);
+ computeIntPower(res, intpart);
+ computeFracPower(res, x);
+ }
+}
+
+template<typename MatrixType>
+typename MatrixPower<MatrixType>::RealScalar
+MatrixPower<MatrixType>::modfAndInit(RealScalar x, RealScalar* intpart)
+{
+ typedef Array<RealScalar, RowsAtCompileTime, 1, ColMajor, MaxRowsAtCompileTime> RealArray;
+
+ *intpart = std::floor(x);
+ RealScalar res = x - *intpart;
+
+ if (!m_conditionNumber && res) {
+ const ComplexSchur<MatrixType> schurOfA(m_A);
+ m_T = schurOfA.matrixT();
+ m_U = schurOfA.matrixU();
+
+ const RealArray absTdiag = m_T.diagonal().array().abs();
+ m_conditionNumber = absTdiag.maxCoeff() / absTdiag.minCoeff();
+ }
+
+ if (res>RealScalar(0.5) && res>(1-res)*std::pow(m_conditionNumber, res)) {
+ --res;
+ ++*intpart;
+ }
+ return res;
+}
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::computeIntPower(ResultType& res, RealScalar p)
+{
+ RealScalar pp = std::abs(p);
+
+ if (p<0) m_tmp = m_A.inverse();
+ else m_tmp = m_A;
+
+ res = MatrixType::Identity(rows(), cols());
+ while (pp >= 1) {
+ if (std::fmod(pp, 2) >= 1)
+ res = m_tmp * res;
+ m_tmp *= m_tmp;
+ pp /= 2;
+ }
+}
+
+template<typename MatrixType>
+template<typename ResultType>
+void MatrixPower<MatrixType>::computeFracPower(ResultType& res, RealScalar p)
+{
+ if (p) {
+ eigen_assert(m_conditionNumber);
+ MatrixPowerAtomic<ComplexMatrix>(m_T, p).compute(m_fT);
+ revertSchur(m_tmp, m_fT, m_U);
+ res = m_tmp * res;
+ }
+}
+
+template<typename MatrixType>
+template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(
+ Matrix<ComplexScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+ const ComplexMatrix& T,
+ const ComplexMatrix& U)
+{ res.noalias() = U * (T.template triangularView<Upper>() * U.adjoint()); }
+
+template<typename MatrixType>
+template<int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+inline void MatrixPower<MatrixType>::revertSchur(
+ Matrix<RealScalar, Rows, Cols, Options, MaxRows, MaxCols>& res,
+ const ComplexMatrix& T,
+ const ComplexMatrix& U)
+{ res.noalias() = (U * (T.template triangularView<Upper>() * U.adjoint())).real(); }
+
+/**
+ * \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix power of some matrix (expression).
+ *
+ * \tparam Derived type of the base, a matrix (expression).
+ *
+ * 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
+ * MatrixBase::pow() and related functions and most of the
+ * time this is the only way it is used.
+ */
+template<typename Derived>
+class MatrixPowerReturnValue : public ReturnByValue< MatrixPowerReturnValue<Derived> >
+{
+ public:
+ typedef typename Derived::PlainObject PlainObject;
+ typedef typename Derived::RealScalar RealScalar;
+ typedef typename Derived::Index Index;
+
+ /**
+ * \brief Constructor.
+ *
+ * \param[in] A %Matrix (expression), the base of the matrix power.
+ * \param[in] p scalar, the exponent of the matrix power.
+ */
+ MatrixPowerReturnValue(const Derived& A, RealScalar p) : m_A(A), m_p(p)
+ { }
+
+ /**
+ * \brief Compute the matrix power.
+ *
+ * \param[out] result \f$ A^p \f$ where \p A and \p p are as in the
+ * constructor.
+ */
+ template<typename ResultType>
+ inline void evalTo(ResultType& res) const
+ { MatrixPower<PlainObject>(m_A.eval()).compute(res, m_p); }
+
+ Index rows() const { return m_A.rows(); }
+ Index cols() const { return m_A.cols(); }
+
+ private:
+ const Derived& m_A;
+ const RealScalar m_p;
+ MatrixPowerReturnValue& operator=(const MatrixPowerReturnValue&);
+};
+
+namespace internal {
+
+template<typename MatrixPowerType>
+struct traits< MatrixPowerRetval<MatrixPowerType> >
+{ typedef typename MatrixPowerType::PlainObject ReturnType; };
+
+template<typename Derived>
+struct traits< MatrixPowerReturnValue<Derived> >
+{ typedef typename Derived::PlainObject ReturnType; };
+
+}
+
+template<typename Derived>
+const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(const RealScalar& p) const
+{ return MatrixPowerReturnValue<Derived>(derived(), p); }
+
+} // namespace Eigen
+
+#endif // EIGEN_MATRIX_POWER
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
new file mode 100644
index 0000000..b48ea9d
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h
@@ -0,0 +1,466 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// 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_SQUARE_ROOT
+#define EIGEN_MATRIX_SQUARE_ROOT
+
+namespace Eigen {
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing matrix square roots of upper quasi-triangular matrices.
+ * \tparam MatrixType type of the argument of the matrix square root,
+ * expected to be an instantiation of the Matrix class template.
+ *
+ * This class computes the square root of the upper quasi-triangular
+ * matrix stored in the upper Hessenberg part of the matrix passed to
+ * the constructor.
+ *
+ * \sa MatrixSquareRoot, MatrixSquareRootTriangular
+ */
+template <typename MatrixType>
+class MatrixSquareRootQuasiTriangular
+{
+ public:
+
+ /** \brief Constructor.
+ *
+ * \param[in] A upper quasi-triangular matrix whose square root
+ * is to be computed.
+ *
+ * The class stores a reference to \p A, so it should not be
+ * changed (or destroyed) before compute() is called.
+ */
+ MatrixSquareRootQuasiTriangular(const MatrixType& A)
+ : m_A(A)
+ {
+ eigen_assert(A.rows() == A.cols());
+ }
+
+ /** \brief Compute the matrix square root
+ *
+ * \param[out] result square root of \p A, as specified in the constructor.
+ *
+ * Only the upper Hessenberg part of \p result is updated, the
+ * rest is not touched. See MatrixBase::sqrt() for details on
+ * how this computation is implemented.
+ */
+ template <typename ResultType> void compute(ResultType &result);
+
+ private:
+ typedef typename MatrixType::Index Index;
+ typedef typename MatrixType::Scalar Scalar;
+
+ void computeDiagonalPartOfSqrt(MatrixType& sqrtT, const MatrixType& T);
+ void computeOffDiagonalPartOfSqrt(MatrixType& sqrtT, const MatrixType& T);
+ void compute2x2diagonalBlock(MatrixType& sqrtT, const MatrixType& T, typename MatrixType::Index i);
+ void compute1x1offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j);
+ void compute1x2offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j);
+ void compute2x1offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j);
+ void compute2x2offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j);
+
+ template <typename SmallMatrixType>
+ static void solveAuxiliaryEquation(SmallMatrixType& X, const SmallMatrixType& A,
+ const SmallMatrixType& B, const SmallMatrixType& C);
+
+ const MatrixType& m_A;
+};
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixSquareRootQuasiTriangular<MatrixType>::compute(ResultType &result)
+{
+ result.resize(m_A.rows(), m_A.cols());
+ computeDiagonalPartOfSqrt(result, m_A);
+ computeOffDiagonalPartOfSqrt(result, m_A);
+}
+
+// pre: T is quasi-upper-triangular and sqrtT is a zero matrix of the same size
+// post: the diagonal blocks of sqrtT are the square roots of the diagonal blocks of T
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>::computeDiagonalPartOfSqrt(MatrixType& sqrtT,
+ const MatrixType& T)
+{
+ using std::sqrt;
+ const Index size = m_A.rows();
+ for (Index i = 0; i < size; i++) {
+ if (i == size - 1 || T.coeff(i+1, i) == 0) {
+ eigen_assert(T(i,i) >= 0);
+ sqrtT.coeffRef(i,i) = sqrt(T.coeff(i,i));
+ }
+ else {
+ compute2x2diagonalBlock(sqrtT, T, i);
+ ++i;
+ }
+ }
+}
+
+// pre: T is quasi-upper-triangular and diagonal blocks of sqrtT are square root of diagonal blocks of T.
+// post: sqrtT is the square root of T.
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>::computeOffDiagonalPartOfSqrt(MatrixType& sqrtT,
+ const MatrixType& T)
+{
+ const Index size = m_A.rows();
+ for (Index j = 1; j < size; j++) {
+ if (T.coeff(j, j-1) != 0) // if T(j-1:j, j-1:j) is a 2-by-2 block
+ continue;
+ for (Index i = j-1; i >= 0; i--) {
+ if (i > 0 && T.coeff(i, i-1) != 0) // if T(i-1:i, i-1:i) is a 2-by-2 block
+ continue;
+ bool iBlockIs2x2 = (i < size - 1) && (T.coeff(i+1, i) != 0);
+ bool jBlockIs2x2 = (j < size - 1) && (T.coeff(j+1, j) != 0);
+ if (iBlockIs2x2 && jBlockIs2x2)
+ compute2x2offDiagonalBlock(sqrtT, T, i, j);
+ else if (iBlockIs2x2 && !jBlockIs2x2)
+ compute2x1offDiagonalBlock(sqrtT, T, i, j);
+ else if (!iBlockIs2x2 && jBlockIs2x2)
+ compute1x2offDiagonalBlock(sqrtT, T, i, j);
+ else if (!iBlockIs2x2 && !jBlockIs2x2)
+ compute1x1offDiagonalBlock(sqrtT, T, i, j);
+ }
+ }
+}
+
+// pre: T.block(i,i,2,2) has complex conjugate eigenvalues
+// post: sqrtT.block(i,i,2,2) is square root of T.block(i,i,2,2)
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::compute2x2diagonalBlock(MatrixType& sqrtT, const MatrixType& T, typename MatrixType::Index i)
+{
+ // TODO: This case (2-by-2 blocks with complex conjugate eigenvalues) is probably hidden somewhere
+ // in EigenSolver. If we expose it, we could call it directly from here.
+ Matrix<Scalar,2,2> block = T.template block<2,2>(i,i);
+ EigenSolver<Matrix<Scalar,2,2> > es(block);
+ sqrtT.template block<2,2>(i,i)
+ = (es.eigenvectors() * es.eigenvalues().cwiseSqrt().asDiagonal() * es.eigenvectors().inverse()).real();
+}
+
+// pre: block structure of T is such that (i,j) is a 1x1 block,
+// all blocks of sqrtT to left of and below (i,j) are correct
+// post: sqrtT(i,j) has the correct value
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::compute1x1offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j)
+{
+ Scalar tmp = (sqrtT.row(i).segment(i+1,j-i-1) * sqrtT.col(j).segment(i+1,j-i-1)).value();
+ sqrtT.coeffRef(i,j) = (T.coeff(i,j) - tmp) / (sqrtT.coeff(i,i) + sqrtT.coeff(j,j));
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::compute1x2offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j)
+{
+ Matrix<Scalar,1,2> rhs = T.template block<1,2>(i,j);
+ if (j-i > 1)
+ rhs -= sqrtT.block(i, i+1, 1, j-i-1) * sqrtT.block(i+1, j, j-i-1, 2);
+ Matrix<Scalar,2,2> A = sqrtT.coeff(i,i) * Matrix<Scalar,2,2>::Identity();
+ A += sqrtT.template block<2,2>(j,j).transpose();
+ sqrtT.template block<1,2>(i,j).transpose() = A.fullPivLu().solve(rhs.transpose());
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::compute2x1offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j)
+{
+ Matrix<Scalar,2,1> rhs = T.template block<2,1>(i,j);
+ if (j-i > 2)
+ rhs -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 1);
+ Matrix<Scalar,2,2> A = sqrtT.coeff(j,j) * Matrix<Scalar,2,2>::Identity();
+ A += sqrtT.template block<2,2>(i,i);
+ sqrtT.template block<2,1>(i,j) = A.fullPivLu().solve(rhs);
+}
+
+// similar to compute1x1offDiagonalBlock()
+template <typename MatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::compute2x2offDiagonalBlock(MatrixType& sqrtT, const MatrixType& T,
+ typename MatrixType::Index i, typename MatrixType::Index j)
+{
+ Matrix<Scalar,2,2> A = sqrtT.template block<2,2>(i,i);
+ Matrix<Scalar,2,2> B = sqrtT.template block<2,2>(j,j);
+ Matrix<Scalar,2,2> C = T.template block<2,2>(i,j);
+ if (j-i > 2)
+ C -= sqrtT.block(i, i+2, 2, j-i-2) * sqrtT.block(i+2, j, j-i-2, 2);
+ Matrix<Scalar,2,2> X;
+ solveAuxiliaryEquation(X, A, B, C);
+ sqrtT.template block<2,2>(i,j) = X;
+}
+
+// solves the equation A X + X B = C where all matrices are 2-by-2
+template <typename MatrixType>
+template <typename SmallMatrixType>
+void MatrixSquareRootQuasiTriangular<MatrixType>
+ ::solveAuxiliaryEquation(SmallMatrixType& X, const SmallMatrixType& A,
+ const SmallMatrixType& B, const SmallMatrixType& C)
+{
+ EIGEN_STATIC_ASSERT((internal::is_same<SmallMatrixType, Matrix<Scalar,2,2> >::value),
+ EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
+
+ Matrix<Scalar,4,4> coeffMatrix = Matrix<Scalar,4,4>::Zero();
+ coeffMatrix.coeffRef(0,0) = A.coeff(0,0) + B.coeff(0,0);
+ coeffMatrix.coeffRef(1,1) = A.coeff(0,0) + B.coeff(1,1);
+ coeffMatrix.coeffRef(2,2) = A.coeff(1,1) + B.coeff(0,0);
+ coeffMatrix.coeffRef(3,3) = A.coeff(1,1) + B.coeff(1,1);
+ coeffMatrix.coeffRef(0,1) = B.coeff(1,0);
+ coeffMatrix.coeffRef(0,2) = A.coeff(0,1);
+ coeffMatrix.coeffRef(1,0) = B.coeff(0,1);
+ coeffMatrix.coeffRef(1,3) = A.coeff(0,1);
+ coeffMatrix.coeffRef(2,0) = A.coeff(1,0);
+ coeffMatrix.coeffRef(2,3) = B.coeff(1,0);
+ coeffMatrix.coeffRef(3,1) = A.coeff(1,0);
+ coeffMatrix.coeffRef(3,2) = B.coeff(0,1);
+
+ Matrix<Scalar,4,1> rhs;
+ rhs.coeffRef(0) = C.coeff(0,0);
+ rhs.coeffRef(1) = C.coeff(0,1);
+ rhs.coeffRef(2) = C.coeff(1,0);
+ rhs.coeffRef(3) = C.coeff(1,1);
+
+ Matrix<Scalar,4,1> result;
+ result = coeffMatrix.fullPivLu().solve(rhs);
+
+ X.coeffRef(0,0) = result.coeff(0);
+ X.coeffRef(0,1) = result.coeff(1);
+ X.coeffRef(1,0) = result.coeff(2);
+ X.coeffRef(1,1) = result.coeff(3);
+}
+
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing matrix square roots of upper triangular matrices.
+ * \tparam MatrixType type of the argument of the matrix square root,
+ * expected to be an instantiation of the Matrix class template.
+ *
+ * This class computes the square root of the upper triangular matrix
+ * stored in the upper triangular part (including the diagonal) of
+ * the matrix passed to the constructor.
+ *
+ * \sa MatrixSquareRoot, MatrixSquareRootQuasiTriangular
+ */
+template <typename MatrixType>
+class MatrixSquareRootTriangular
+{
+ public:
+ MatrixSquareRootTriangular(const MatrixType& A)
+ : m_A(A)
+ {
+ eigen_assert(A.rows() == A.cols());
+ }
+
+ /** \brief Compute the matrix square root
+ *
+ * \param[out] result square root of \p A, as specified in the constructor.
+ *
+ * Only the upper triangular part (including the diagonal) of
+ * \p result is updated, the rest is not touched. See
+ * MatrixBase::sqrt() for details on how this computation is
+ * implemented.
+ */
+ template <typename ResultType> void compute(ResultType &result);
+
+ private:
+ const MatrixType& m_A;
+};
+
+template <typename MatrixType>
+template <typename ResultType>
+void MatrixSquareRootTriangular<MatrixType>::compute(ResultType &result)
+{
+ using std::sqrt;
+
+ // Compute square root of m_A and store it in upper triangular part of result
+ // This uses that the square root of triangular matrices can be computed directly.
+ result.resize(m_A.rows(), m_A.cols());
+ typedef typename MatrixType::Index Index;
+ for (Index i = 0; i < m_A.rows(); i++) {
+ result.coeffRef(i,i) = sqrt(m_A.coeff(i,i));
+ }
+ for (Index j = 1; j < m_A.cols(); j++) {
+ for (Index i = j-1; i >= 0; i--) {
+ typedef typename MatrixType::Scalar Scalar;
+ // if i = j-1, then segment has length 0 so tmp = 0
+ Scalar tmp = (result.row(i).segment(i+1,j-i-1) * result.col(j).segment(i+1,j-i-1)).value();
+ // denominator may be zero if original matrix is singular
+ result.coeffRef(i,j) = (m_A.coeff(i,j) - tmp) / (result.coeff(i,i) + result.coeff(j,j));
+ }
+ }
+}
+
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Class for computing matrix square roots of general matrices.
+ * \tparam MatrixType type of the argument of the matrix square root,
+ * expected to be an instantiation of the Matrix class template.
+ *
+ * \sa MatrixSquareRootTriangular, MatrixSquareRootQuasiTriangular, MatrixBase::sqrt()
+ */
+template <typename MatrixType, int IsComplex = NumTraits<typename internal::traits<MatrixType>::Scalar>::IsComplex>
+class MatrixSquareRoot
+{
+ public:
+
+ /** \brief Constructor.
+ *
+ * \param[in] A matrix whose square root is to be computed.
+ *
+ * The class stores a reference to \p A, so it should not be
+ * changed (or destroyed) before compute() is called.
+ */
+ MatrixSquareRoot(const MatrixType& A);
+
+ /** \brief Compute the matrix square root
+ *
+ * \param[out] result square root of \p A, as specified in the constructor.
+ *
+ * See MatrixBase::sqrt() for details on how this computation is
+ * implemented.
+ */
+ template <typename ResultType> void compute(ResultType &result);
+};
+
+
+// ********** Partial specialization for real matrices **********
+
+template <typename MatrixType>
+class MatrixSquareRoot<MatrixType, 0>
+{
+ public:
+
+ MatrixSquareRoot(const MatrixType& A)
+ : m_A(A)
+ {
+ eigen_assert(A.rows() == A.cols());
+ }
+
+ template <typename ResultType> void compute(ResultType &result)
+ {
+ // Compute Schur decomposition of m_A
+ const RealSchur<MatrixType> schurOfA(m_A);
+ const MatrixType& T = schurOfA.matrixT();
+ const MatrixType& U = schurOfA.matrixU();
+
+ // Compute square root of T
+ MatrixType sqrtT = MatrixType::Zero(m_A.rows(), m_A.cols());
+ MatrixSquareRootQuasiTriangular<MatrixType>(T).compute(sqrtT);
+
+ // Compute square root of m_A
+ result = U * sqrtT * U.adjoint();
+ }
+
+ private:
+ const MatrixType& m_A;
+};
+
+
+// ********** Partial specialization for complex matrices **********
+
+template <typename MatrixType>
+class MatrixSquareRoot<MatrixType, 1>
+{
+ public:
+
+ MatrixSquareRoot(const MatrixType& A)
+ : m_A(A)
+ {
+ eigen_assert(A.rows() == A.cols());
+ }
+
+ template <typename ResultType> void compute(ResultType &result)
+ {
+ // Compute Schur decomposition of m_A
+ const ComplexSchur<MatrixType> schurOfA(m_A);
+ const MatrixType& T = schurOfA.matrixT();
+ const MatrixType& U = schurOfA.matrixU();
+
+ // Compute square root of T
+ MatrixType sqrtT;
+ MatrixSquareRootTriangular<MatrixType>(T).compute(sqrtT);
+
+ // Compute square root of m_A
+ result = U * (sqrtT.template triangularView<Upper>() * U.adjoint());
+ }
+
+ private:
+ const MatrixType& m_A;
+};
+
+
+/** \ingroup MatrixFunctions_Module
+ *
+ * \brief Proxy for the matrix square root of some matrix (expression).
+ *
+ * \tparam Derived Type of the argument to the matrix square root.
+ *
+ * This class holds the argument to the matrix square root 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::sqrt() and most of the time this is the only way it is
+ * used.
+ */
+template<typename Derived> class MatrixSquareRootReturnValue
+: public ReturnByValue<MatrixSquareRootReturnValue<Derived> >
+{
+ typedef typename Derived::Index Index;
+ public:
+ /** \brief Constructor.
+ *
+ * \param[in] src %Matrix (expression) forming the argument of the
+ * matrix square root.
+ */
+ MatrixSquareRootReturnValue(const Derived& src) : m_src(src) { }
+
+ /** \brief Compute the matrix square root.
+ *
+ * \param[out] result the matrix square root of \p src in the
+ * constructor.
+ */
+ template <typename ResultType>
+ inline void evalTo(ResultType& result) const
+ {
+ const typename Derived::PlainObject srcEvaluated = m_src.eval();
+ MatrixSquareRoot<typename Derived::PlainObject> me(srcEvaluated);
+ me.compute(result);
+ }
+
+ Index rows() const { return m_src.rows(); }
+ Index cols() const { return m_src.cols(); }
+
+ protected:
+ const Derived& m_src;
+ private:
+ MatrixSquareRootReturnValue& operator=(const MatrixSquareRootReturnValue&);
+};
+
+namespace internal {
+template<typename Derived>
+struct traits<MatrixSquareRootReturnValue<Derived> >
+{
+ typedef typename Derived::PlainObject ReturnType;
+};
+}
+
+template <typename Derived>
+const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const
+{
+ eigen_assert(rows() == cols());
+ return MatrixSquareRootReturnValue<Derived>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_FUNCTION
diff --git a/unsupported/Eigen/src/MatrixFunctions/StemFunction.h b/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
new file mode 100644
index 0000000..724e55c
--- /dev/null
+++ b/unsupported/Eigen/src/MatrixFunctions/StemFunction.h
@@ -0,0 +1,105 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// 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_STEM_FUNCTION
+#define EIGEN_STEM_FUNCTION
+
+namespace Eigen {
+
+/** \ingroup MatrixFunctions_Module
+ * \brief Stem functions corresponding to standard mathematical functions.
+ */
+template <typename Scalar>
+class StdStemFunctions
+{
+ public:
+
+ /** \brief The exponential function (and its derivatives). */
+ static Scalar exp(Scalar x, int)
+ {
+ return std::exp(x);
+ }
+
+ /** \brief Cosine (and its derivatives). */
+ static Scalar cos(Scalar x, int n)
+ {
+ Scalar res;
+ switch (n % 4) {
+ case 0:
+ res = std::cos(x);
+ break;
+ case 1:
+ res = -std::sin(x);
+ break;
+ case 2:
+ res = -std::cos(x);
+ break;
+ case 3:
+ res = std::sin(x);
+ break;
+ }
+ return res;
+ }
+
+ /** \brief Sine (and its derivatives). */
+ static Scalar sin(Scalar x, int n)
+ {
+ Scalar res;
+ switch (n % 4) {
+ case 0:
+ res = std::sin(x);
+ break;
+ case 1:
+ res = std::cos(x);
+ break;
+ case 2:
+ res = -std::sin(x);
+ break;
+ case 3:
+ res = -std::cos(x);
+ break;
+ }
+ return res;
+ }
+
+ /** \brief Hyperbolic cosine (and its derivatives). */
+ static Scalar cosh(Scalar x, int n)
+ {
+ Scalar res;
+ switch (n % 2) {
+ case 0:
+ res = std::cosh(x);
+ break;
+ case 1:
+ res = std::sinh(x);
+ break;
+ }
+ return res;
+ }
+
+ /** \brief Hyperbolic sine (and its derivatives). */
+ static Scalar sinh(Scalar x, int n)
+ {
+ Scalar res;
+ switch (n % 2) {
+ case 0:
+ res = std::sinh(x);
+ break;
+ case 1:
+ res = std::cosh(x);
+ break;
+ }
+ return res;
+ }
+
+}; // end of class StdStemFunctions
+
+} // end namespace Eigen
+
+#endif // EIGEN_STEM_FUNCTION