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13.7 Solving a System of Linear ODEs using a Matrix Exponential

Linear systems of ODEs can be solved using a matrix exponential. This can be considerably faster than using one of the ODE solvers.

The solution to \(\frac{d}{dt} y = ay\) is \(y = y_0e^{at}\), where the constant \(y_0\) is determined by boundary conditions. We can extend this solution to the vector case: \[ \frac{d}{dt}y = A \, y \] where \(y\) is now a vector of length \(n\) and \(A\) is an \(n\) by \(n\) matrix. The solution is then given by: \[ y = e^{tA} \, y_0 \] where the matrix exponential is formally defined by the convergent power series: \[ e^{tA} = \sum_{n=0}^{\infty} \dfrac{tA^n}{n!} = I + tA + \frac{t^2A^2}{2!} + \dotsb \]

We can apply this technique to the simple harmonic oscillator example, by setting \[ y = \begin{bmatrix} y_1 \\ y_2 \end{bmatrix} \qquad A = \begin{bmatrix} 0 & 1 \\ -1 & -\theta \end{bmatrix} \]

The Stan model to simulate noisy observations using a matrix exponential function is given below.

In general, computing a matrix exponential will be more efficient than using a numerical solver. We can however only apply this technique to systems of linear ODEs.

data {
  int<lower=1> T;
  vector[2] y0;
  real ts[T];
  real theta[1];
model {
generated quantities {
  vector[2] y_sim[T];
  matrix[2, 2] A = [[ 0,  1],
                    [-1, -theta[1]]]
  for (t in 1:T)
    y_sim[t] = matrix_exp((t - 1) * A) * y0;
  // add measurement error
  for (t in 1:T) {
    y_sim[t, 1] += normal_rng(0, 0.1);
    y_sim[t, 2] += normal_rng(0, 0.1);

This Stan program simulates noisy measurements from a simple harmonic oscillator. The system of linear differential equations is coded as a matrix. The system parameters theta and initial state y0 are read in as data along observation times ts. The generated quantities block is used to solve the ODE for the specified times and then add random measurement error, producing observations y_sim. Because the ODEs are linear, we can use the matrix_exp function to solve the system.