diff options
Diffstat (limited to 'eigen/unsupported/Eigen/MatrixFunctions')
| -rw-r--r-- | eigen/unsupported/Eigen/MatrixFunctions | 76 |
1 files changed, 65 insertions, 11 deletions
diff --git a/eigen/unsupported/Eigen/MatrixFunctions b/eigen/unsupported/Eigen/MatrixFunctions index 0991817..0320606 100644 --- a/eigen/unsupported/Eigen/MatrixFunctions +++ b/eigen/unsupported/Eigen/MatrixFunctions @@ -13,8 +13,6 @@ #include <cfloat> #include <list> -#include <functional> -#include <iterator> #include <Eigen/Core> #include <Eigen/LU> @@ -84,7 +82,9 @@ const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const \param[in] M a square matrix. \returns expression representing \f$ \cos(M) \f$. -This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos(). +This function computes the matrix cosine. Use ArrayBase::cos() for computing the entry-wise cosine. + +The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cos(). \sa \ref matrixbase_sin "sin()" for an example. @@ -125,6 +125,9 @@ differential equations: the solution of \f$ y' = My \f$ with the initial condition \f$ y(0) = y_0 \f$ is given by \f$ y(t) = \exp(M) y_0 \f$. +The matrix exponential is different from applying the exp function to all the entries in the matrix. +Use ArrayBase::exp() if you want to do the latter. + The cost of the computation is approximately \f$ 20 n^3 \f$ for matrices of size \f$ n \f$. The number 20 depends weakly on the norm of the matrix. @@ -179,6 +182,9 @@ the scalar logarithm, the equation \f$ \exp(X) = M \f$ may have multiple solutions; this function returns a matrix whose eigenvalues have imaginary part in the interval \f$ (-\pi,\pi] \f$. +The matrix logarithm is different from applying the log function to all the entries in the matrix. +Use ArrayBase::log() if you want to do the latter. + In the real case, the matrix \f$ M \f$ should be invertible and it should have no eigenvalues which are real and negative (pairs of complex conjugate eigenvalues are allowed). In the complex case, it @@ -230,22 +236,66 @@ const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) con \endcode \param[in] M base of the matrix power, should be a square matrix. -\param[in] p exponent of the matrix power, should be real. +\param[in] p exponent of the matrix power. The matrix power \f$ M^p \f$ is defined as \f$ \exp(p \log(M)) \f$, where exp denotes the matrix exponential, and log denotes the matrix -logarithm. +logarithm. This is different from raising all the entries in the matrix +to the p-th power. Use ArrayBase::pow() if you want to do the latter. -The matrix \f$ M \f$ should meet the conditions to be an argument of -matrix logarithm. If \p p is not of the real scalar type of \p M, it -is casted into the real scalar type of \p M. +If \p p is complex, the scalar type of \p M should be the type of \p +p . \f$ M^p \f$ simply evaluates into \f$ \exp(p \log(M)) \f$. +Therefore, the matrix \f$ M \f$ should meet the conditions to be an +argument of matrix logarithm. -This function computes the matrix power using the Schur-Padé +If \p p is real, it is casted into the real scalar type of \p M. Then +this function computes the matrix power using the Schur-Padé algorithm as implemented by class MatrixPower. The exponent is split into integral part and fractional part, where the fractional part is in the interval \f$ (-1, 1) \f$. The main diagonal and the first super-diagonal is directly computed. +If \p M is singular with a semisimple zero eigenvalue and \p p is +positive, the Schur factor \f$ T \f$ is reordered with Givens +rotations, i.e. + +\f[ T = \left[ \begin{array}{cc} + T_1 & T_2 \\ + 0 & 0 + \end{array} \right] \f] + +where \f$ T_1 \f$ is invertible. Then \f$ T^p \f$ is given by + +\f[ T^p = \left[ \begin{array}{cc} + T_1^p & T_1^{-1} T_1^p T_2 \\ + 0 & 0 + \end{array}. \right] \f] + +\warning Fractional power of a matrix with a non-semisimple zero +eigenvalue is not well-defined. We introduce an assertion failure +against inaccurate result, e.g. \code +#include <unsupported/Eigen/MatrixFunctions> +#include <iostream> + +int main() +{ + Eigen::Matrix4d A; + A << 0, 0, 2, 3, + 0, 0, 4, 5, + 0, 0, 6, 7, + 0, 0, 8, 9; + std::cout << A.pow(0.37) << std::endl; + + // The 1 makes eigenvalue 0 non-semisimple. + A.coeffRef(0, 1) = 1; + + // This fails if EIGEN_NO_DEBUG is undefined. + std::cout << A.pow(0.37) << std::endl; + + return 0; +} +\endcode + Details of the algorithm can be found in: Nicholas J. Higham and Lijing Lin, "A Schur-Padé algorithm for fractional powers of a matrix," <em>SIAM J. %Matrix Anal. Applic.</em>, @@ -350,7 +400,9 @@ const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const \param[in] M a square matrix. \returns expression representing \f$ \sin(M) \f$. -This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin(). +This function computes the matrix sine. Use ArrayBase::sin() for computing the entry-wise sine. + +The implementation calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sin(). Example: \include MatrixSine.cpp Output: \verbinclude MatrixSine.out @@ -387,7 +439,9 @@ const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const The matrix square root of \f$ M \f$ is the matrix \f$ M^{1/2} \f$ whose square is the original matrix; so if \f$ S = M^{1/2} \f$ then -\f$ S^2 = M \f$. +\f$ S^2 = M \f$. This is different from taking the square root of all +the entries in the matrix; use ArrayBase::sqrt() if you want to do the +latter. In the <b>real case</b>, the matrix \f$ M \f$ should be invertible and it should have no eigenvalues which are real and negative (pairs of |