Eigen for exponential matrix

Useful link
http://eigen.tuxfamily.org/dox/unsupported/group__MatrixFunctions__Module.html

Reference:
(Implementation and theoretical details) Nicholas J. Higham, “The scaling and squaring method for the matrix exponential revisited,”SIAM J. Matrix Anal. Applic., 26:1179–1193, 2005.

g++ a.cpp -o a -I$(EIGENPackage), where EIGENPackage = where your eigen source code

#include <unsupported/Eigen/MatrixFunctions>
#include <iostream>
using namespace Eigen;
int main()
{
  const double pi = std::acos(-1.0);
  MatrixXd A(3,3);
  A << 0,    -pi/4, 0,
       pi/4, 0,     0,
       0,    0,     0;
  std::cout << "The matrix A is:\n" << A << "\n\n";
  std::cout << "The matrix exponential of A is:\n" << A.exp() << "\n\n";
}

Besides stiffness problem, the explicit methods are still not better than implicit methods?

Considering the simple dynamical system as follows

{\bf C}{\bf \dot{x}}(t)={\bf G}{\bf }x(t)+{\bf B}{\bf u}(t)

where \bf{C} and \bf{G} are sparse matrices, {\bf u}(t) is a input and \bf{B} is incident matrix which poses {\bf u}(t) in the system.

In my thought, the explicit methods work if there is not \bf{C} on the left hand side,

{\bf \dot{x}}(t)={\bf G}{\bf x}(t)+{\bf B}{\bf u}(t)

e.g. solve it by forward Euler method and ignore the input for simplicity (\bf{\dot{x}}(t)={\bf G}{\bf x}(t))), then

{\bf x}(t+h)={\bf x}(t)+h{\bf G}{\bf x}(t), where h is the discretized time step.

However, when this is not true, the explicit method still need to solve linear system, because

{\bf C}{\bf x}(t+h)={\bf C}{\bf x}(t)+h{\bf G}{\bf x}(t) or say it requires inversion of matrix {\bf x}(t+h)={\bf x}(t)+h{\bf C}^{-1}{\bf G}{\bf x}(t)

Any comments?