ODE solver as a functional fold

One of the most widely used numerical algorithms for solving differential equation is the 4th order Runge-Kutta method. This post shows how the Runge-Kutta method can be written naturally as a fold over the set of points where the solution is needed. These points do not need to be evenly spaced.

Given a differential equation of the form

with initial condition y(t₀) = y₀, the 4th order Runge-Kutta method advances the solution by an amount of time h by

where

The Haskell code for implementing the accumulator function for foldl looks very much like the mathematical description above. This is a nice feature of the where syntax.

 rk (t, y) t' = (t', y + h*(k1 + 2.0*k2 + 2.0*k3 + k4)/6.0) where h = t' - t k1 = f t y k2 = f (t + 0.5*h) (y + 0.5*h*k1) k3 = f (t + 0.5*h) (y + 0.5*h*k2) k4 = f (t + 1.0*h) (y + 1.0*h*k3) 

Suppose we want to solve the differential equation y ' = (t² - y²) sin(y) with initial condition y(0) = -1, and we want to approximate the solution at [0.01, 0.03, 0.04, 0.06]. We would implement the right side of the equation as

 f t y = (t**2 - y**2)*sin(y)

and fold the function rk over our time steps with

 foldl rk (0, -1) [0.01, 0.03, 0.04, 0.06]

This returns (0.06, -0.9527). The first part, 0.06, is no surprise since obviously we asked for the solution up to 0.06. The second part, -0.9527, is the part we're more interested in.

If you want to see the solution at all the specified points and not just the last one, replace foldl with scanl.

 scanl rk (0, -1) [0.01, 0.03, 0.04, 0.06]

This returns

 [(0.0, -1.0), (0.01, -0.9917), (0.03, -0.9756), (0.042, -0.9678), (0.06, -0.9527)]

As pointed out in the previous post, writing algorithms as folds separates the core of the algorithm from data access. This would allow us, for example, to change rk independently, such as using a different order Runge-Kutta method. (Which hardly anyone does. Fourth order is a kind of sweet spot.) Or we could swap out Runge-Kutta for a different ODE solver entirely just by passing a different function into the fold.

Next see how you could use a fold to compute mean, variance, skewness, and kurtosis in one pass through a stream of data using a fold.