fuchsia / third_party / eigen / bc25605ec2d13f9bd386b3d697d23d896d4c3a7b / . / unsupported / Eigen / MatrixFunctions

// This file is part of Eigen, a lightweight C++ template library | |

// for linear algebra. | |

// | |

// Copyright (C) 2009 Jitse Niesen <jitse@maths.leeds.ac.uk> | |

// Copyright (C) 2012 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_FUNCTIONS | |

#define EIGEN_MATRIX_FUNCTIONS | |

#include <cfloat> | |

#include <list> | |

#include <Eigen/Core> | |

#include <Eigen/LU> | |

#include <Eigen/Eigenvalues> | |

/** | |

* \defgroup MatrixFunctions_Module Matrix functions module | |

* \brief This module aims to provide various methods for the computation of | |

* matrix functions. | |

* | |

* To use this module, add | |

* \code | |

* #include <unsupported/Eigen/MatrixFunctions> | |

* \endcode | |

* at the start of your source file. | |

* | |

* This module defines the following MatrixBase methods. | |

* - \ref matrixbase_cos "MatrixBase::cos()", for computing the matrix cosine | |

* - \ref matrixbase_cosh "MatrixBase::cosh()", for computing the matrix hyperbolic cosine | |

* - \ref matrixbase_exp "MatrixBase::exp()", for computing the matrix exponential | |

* - \ref matrixbase_log "MatrixBase::log()", for computing the matrix logarithm | |

* - \ref matrixbase_pow "MatrixBase::pow()", for computing the matrix power | |

* - \ref matrixbase_matrixfunction "MatrixBase::matrixFunction()", for computing general matrix functions | |

* - \ref matrixbase_sin "MatrixBase::sin()", for computing the matrix sine | |

* - \ref matrixbase_sinh "MatrixBase::sinh()", for computing the matrix hyperbolic sine | |

* - \ref matrixbase_sqrt "MatrixBase::sqrt()", for computing the matrix square root | |

* | |

* These methods are the main entry points to this module. | |

* | |

* %Matrix functions are defined as follows. Suppose that \f$ f \f$ | |

* is an entire function (that is, a function on the complex plane | |

* that is everywhere complex differentiable). Then its Taylor | |

* series | |

* \f[ f(0) + f'(0) x + \frac{f''(0)}{2} x^2 + \frac{f'''(0)}{3!} x^3 + \cdots \f] | |

* converges to \f$ f(x) \f$. In this case, we can define the matrix | |

* function by the same series: | |

* \f[ f(M) = f(0) + f'(0) M + \frac{f''(0)}{2} M^2 + \frac{f'''(0)}{3!} M^3 + \cdots \f] | |

* | |

*/ | |

#include "src/MatrixFunctions/MatrixExponential.h" | |

#include "src/MatrixFunctions/MatrixFunction.h" | |

#include "src/MatrixFunctions/MatrixSquareRoot.h" | |

#include "src/MatrixFunctions/MatrixLogarithm.h" | |

#include "src/MatrixFunctions/MatrixPower.h" | |

/** | |

\page matrixbaseextra_page | |

\ingroup MatrixFunctions_Module | |

\section matrixbaseextra MatrixBase methods defined in the MatrixFunctions module | |

The remainder of the page documents the following MatrixBase methods | |

which are defined in the MatrixFunctions module. | |

\subsection matrixbase_cos MatrixBase::cos() | |

Compute the matrix cosine. | |

\code | |

const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cos() const | |

\endcode | |

\param[in] M a square matrix. | |

\returns expression representing \f$ \cos(M) \f$. | |

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. | |

\subsection matrixbase_cosh MatrixBase::cosh() | |

Compute the matrix hyberbolic cosine. | |

\code | |

const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const | |

\endcode | |

\param[in] M a square matrix. | |

\returns expression representing \f$ \cosh(M) \f$ | |

This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::cosh(). | |

\sa \ref matrixbase_sinh "sinh()" for an example. | |

\subsection matrixbase_exp MatrixBase::exp() | |

Compute the matrix exponential. | |

\code | |

const MatrixExponentialReturnValue<Derived> MatrixBase<Derived>::exp() const | |

\endcode | |

\param[in] M matrix whose exponential is to be computed. | |

\returns expression representing the matrix exponential of \p M. | |

The matrix exponential of \f$ M \f$ is defined by | |

\f[ \exp(M) = \sum_{k=0}^\infty \frac{M^k}{k!}. \f] | |

The matrix exponential can be used to solve linear ordinary | |

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. | |

The matrix exponential is computed using the scaling-and-squaring | |

method combined with Padé approximation. The matrix is first | |

rescaled, then the exponential of the reduced matrix is computed | |

approximant, and then the rescaling is undone by repeated | |

squaring. The degree of the Padé approximant is chosen such | |

that the approximation error is less than the round-off | |

error. However, errors may accumulate during the squaring phase. | |

Details of the algorithm can be found in: Nicholas J. Higham, "The | |

scaling and squaring method for the matrix exponential revisited," | |

<em>SIAM J. %Matrix Anal. Applic.</em>, <b>26</b>:1179–1193, | |

2005. | |

Example: The following program checks that | |

\f[ \exp \left[ \begin{array}{ccc} | |

0 & \frac14\pi & 0 \\ | |

-\frac14\pi & 0 & 0 \\ | |

0 & 0 & 0 | |

\end{array} \right] = \left[ \begin{array}{ccc} | |

\frac12\sqrt2 & -\frac12\sqrt2 & 0 \\ | |

\frac12\sqrt2 & \frac12\sqrt2 & 0 \\ | |

0 & 0 & 1 | |

\end{array} \right]. \f] | |

This corresponds to a rotation of \f$ \frac14\pi \f$ radians around | |

the z-axis. | |

\include MatrixExponential.cpp | |

Output: \verbinclude MatrixExponential.out | |

\note \p M has to be a matrix of \c float, \c double, \c long double | |

\c complex<float>, \c complex<double>, or \c complex<long double> . | |

\subsection matrixbase_log MatrixBase::log() | |

Compute the matrix logarithm. | |

\code | |

const MatrixLogarithmReturnValue<Derived> MatrixBase<Derived>::log() const | |

\endcode | |

\param[in] M invertible matrix whose logarithm is to be computed. | |

\returns expression representing the matrix logarithm root of \p M. | |

The matrix logarithm of \f$ M \f$ is a matrix \f$ X \f$ such that | |

\f$ \exp(X) = M \f$ where exp denotes the matrix exponential. As for | |

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 | |

only needs to be invertible. | |

This function computes the matrix logarithm using the Schur-Parlett | |

algorithm as implemented by MatrixBase::matrixFunction(). The | |

logarithm of an atomic block is computed by MatrixLogarithmAtomic, | |

which uses direct computation for 1-by-1 and 2-by-2 blocks and an | |

inverse scaling-and-squaring algorithm for bigger blocks, with the | |

square roots computed by MatrixBase::sqrt(). | |

Details of the algorithm can be found in Section 11.6.2 of: | |

Nicholas J. Higham, | |

<em>Functions of Matrices: Theory and Computation</em>, | |

SIAM 2008. ISBN 978-0-898716-46-7. | |

Example: The following program checks that | |

\f[ \log \left[ \begin{array}{ccc} | |

\frac12\sqrt2 & -\frac12\sqrt2 & 0 \\ | |

\frac12\sqrt2 & \frac12\sqrt2 & 0 \\ | |

0 & 0 & 1 | |

\end{array} \right] = \left[ \begin{array}{ccc} | |

0 & \frac14\pi & 0 \\ | |

-\frac14\pi & 0 & 0 \\ | |

0 & 0 & 0 | |

\end{array} \right]. \f] | |

This corresponds to a rotation of \f$ \frac14\pi \f$ radians around | |

the z-axis. This is the inverse of the example used in the | |

documentation of \ref matrixbase_exp "exp()". | |

\include MatrixLogarithm.cpp | |

Output: \verbinclude MatrixLogarithm.out | |

\note \p M has to be a matrix of \c float, \c double, <tt>long | |

double</tt>, \c complex<float>, \c complex<double>, or \c complex<long | |

double> . | |

\sa MatrixBase::exp(), MatrixBase::matrixFunction(), | |

class MatrixLogarithmAtomic, MatrixBase::sqrt(). | |

\subsection matrixbase_pow MatrixBase::pow() | |

Compute the matrix raised to arbitrary real power. | |

\code | |

const MatrixPowerReturnValue<Derived> MatrixBase<Derived>::pow(RealScalar p) const | |

\endcode | |

\param[in] M base of the matrix power, should be a square matrix. | |

\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. 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. | |

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. | |

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>, | |

<b>32(3)</b>:1056–1078, 2011. | |

Example: The following program checks that | |

\f[ \left[ \begin{array}{ccc} | |

\cos1 & -\sin1 & 0 \\ | |

\sin1 & \cos1 & 0 \\ | |

0 & 0 & 1 | |

\end{array} \right]^{\frac14\pi} = \left[ \begin{array}{ccc} | |

\frac12\sqrt2 & -\frac12\sqrt2 & 0 \\ | |

\frac12\sqrt2 & \frac12\sqrt2 & 0 \\ | |

0 & 0 & 1 | |

\end{array} \right]. \f] | |

This corresponds to \f$ \frac14\pi \f$ rotations of 1 radian around | |

the z-axis. | |

\include MatrixPower.cpp | |

Output: \verbinclude MatrixPower.out | |

MatrixBase::pow() is user-friendly. However, there are some | |

circumstances under which you should use class MatrixPower directly. | |

MatrixPower can save the result of Schur decomposition, so it's | |

better for computing various powers for the same matrix. | |

Example: | |

\include MatrixPower_optimal.cpp | |

Output: \verbinclude MatrixPower_optimal.out | |

\note \p M has to be a matrix of \c float, \c double, <tt>long | |

double</tt>, \c complex<float>, \c complex<double>, or \c complex<long | |

double> . | |

\sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower. | |

\subsection matrixbase_matrixfunction MatrixBase::matrixFunction() | |

Compute a matrix function. | |

\code | |

const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::matrixFunction(typename internal::stem_function<typename internal::traits<Derived>::Scalar>::type f) const | |

\endcode | |

\param[in] M argument of matrix function, should be a square matrix. | |

\param[in] f an entire function; \c f(x,n) should compute the n-th | |

derivative of f at x. | |

\returns expression representing \p f applied to \p M. | |

Suppose that \p M is a matrix whose entries have type \c Scalar. | |

Then, the second argument, \p f, should be a function with prototype | |

\code | |

ComplexScalar f(ComplexScalar, int) | |

\endcode | |

where \c ComplexScalar = \c std::complex<Scalar> if \c Scalar is | |

real (e.g., \c float or \c double) and \c ComplexScalar = | |

\c Scalar if \c Scalar is complex. The return value of \c f(x,n) | |

should be \f$ f^{(n)}(x) \f$, the n-th derivative of f at x. | |

This routine uses the algorithm described in: | |

Philip Davies and Nicholas J. Higham, | |

"A Schur-Parlett algorithm for computing matrix functions", | |

<em>SIAM J. %Matrix Anal. Applic.</em>, <b>25</b>:464–485, 2003. | |

The actual work is done by the MatrixFunction class. | |

Example: The following program checks that | |

\f[ \exp \left[ \begin{array}{ccc} | |

0 & \frac14\pi & 0 \\ | |

-\frac14\pi & 0 & 0 \\ | |

0 & 0 & 0 | |

\end{array} \right] = \left[ \begin{array}{ccc} | |

\frac12\sqrt2 & -\frac12\sqrt2 & 0 \\ | |

\frac12\sqrt2 & \frac12\sqrt2 & 0 \\ | |

0 & 0 & 1 | |

\end{array} \right]. \f] | |

This corresponds to a rotation of \f$ \frac14\pi \f$ radians around | |

the z-axis. This is the same example as used in the documentation | |

of \ref matrixbase_exp "exp()". | |

\include MatrixFunction.cpp | |

Output: \verbinclude MatrixFunction.out | |

Note that the function \c expfn is defined for complex numbers | |

\c x, even though the matrix \c A is over the reals. Instead of | |

\c expfn, we could also have used StdStemFunctions::exp: | |

\code | |

A.matrixFunction(StdStemFunctions<std::complex<double> >::exp, &B); | |

\endcode | |

\subsection matrixbase_sin MatrixBase::sin() | |

Compute the matrix sine. | |

\code | |

const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sin() const | |

\endcode | |

\param[in] M a square matrix. | |

\returns expression representing \f$ \sin(M) \f$. | |

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 | |

\subsection matrixbase_sinh MatrixBase::sinh() | |

Compute the matrix hyperbolic sine. | |

\code | |

MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::sinh() const | |

\endcode | |

\param[in] M a square matrix. | |

\returns expression representing \f$ \sinh(M) \f$ | |

This function calls \ref matrixbase_matrixfunction "matrixFunction()" with StdStemFunctions::sinh(). | |

Example: \include MatrixSinh.cpp | |

Output: \verbinclude MatrixSinh.out | |

\subsection matrixbase_sqrt MatrixBase::sqrt() | |

Compute the matrix square root. | |

\code | |

const MatrixSquareRootReturnValue<Derived> MatrixBase<Derived>::sqrt() const | |

\endcode | |

\param[in] M invertible matrix whose square root is to be computed. | |

\returns expression representing the matrix square root of \p M. | |

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$. 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 | |

complex conjugate eigenvalues are allowed). In that case, the matrix | |

has a square root which is also real, and this is the square root | |

computed by this function. | |

The matrix square root is computed by first reducing the matrix to | |

quasi-triangular form with the real Schur decomposition. The square | |

root of the quasi-triangular matrix can then be computed directly. The | |

cost is approximately \f$ 25 n^3 \f$ real flops for the real Schur | |

decomposition and \f$ 3\frac13 n^3 \f$ real flops for the remainder | |

(though the computation time in practice is likely more than this | |

indicates). | |

Details of the algorithm can be found in: Nicholas J. Highan, | |

"Computing real square roots of a real matrix", <em>Linear Algebra | |

Appl.</em>, 88/89:405–430, 1987. | |

If the matrix is <b>positive-definite symmetric</b>, then the square | |

root is also positive-definite symmetric. In this case, it is best to | |

use SelfAdjointEigenSolver::operatorSqrt() to compute it. | |

In the <b>complex case</b>, the matrix \f$ M \f$ should be invertible; | |

this is a restriction of the algorithm. The square root computed by | |

this algorithm is the one whose eigenvalues have an argument in the | |

interval \f$ (-\frac12\pi, \frac12\pi] \f$. This is the usual branch | |

cut. | |

The computation is the same as in the real case, except that the | |

complex Schur decomposition is used to reduce the matrix to a | |

triangular matrix. The theoretical cost is the same. Details are in: | |

Åke Björck and Sven Hammarling, "A Schur method for the | |

square root of a matrix", <em>Linear Algebra Appl.</em>, | |

52/53:127–140, 1983. | |

Example: The following program checks that the square root of | |

\f[ \left[ \begin{array}{cc} | |

\cos(\frac13\pi) & -\sin(\frac13\pi) \\ | |

\sin(\frac13\pi) & \cos(\frac13\pi) | |

\end{array} \right], \f] | |

corresponding to a rotation over 60 degrees, is a rotation over 30 degrees: | |

\f[ \left[ \begin{array}{cc} | |

\cos(\frac16\pi) & -\sin(\frac16\pi) \\ | |

\sin(\frac16\pi) & \cos(\frac16\pi) | |

\end{array} \right]. \f] | |

\include MatrixSquareRoot.cpp | |

Output: \verbinclude MatrixSquareRoot.out | |

\sa class RealSchur, class ComplexSchur, class MatrixSquareRoot, | |

SelfAdjointEigenSolver::operatorSqrt(). | |

*/ | |

#endif // EIGEN_MATRIX_FUNCTIONS | |