I stumbled across the recent paper “Efficient Reactive Brownian Dynamics”, which proposes a reaction-diffusion scheme based on a technique called Strang splitting. While I was vaguely familiar with the notion of splitting, I wanted to write up this post to think out some of the details I hadn’t previously.

The reaction-diffusion scheme is a perfect context to understand splitting because there are effectively two (coupled, but distinct) dynamics going on: diffusion and reactions. This will be the generic setup we’ll consider in this post. Suppose we have some quantity \(u(t)\) that evolves by the differential equation \[\begin{equation} \frac{\partial u}{\partial t} = L_1(u) + L_2(u),\tag{1} \end{equation}\] where \(L_1,L_2\) are two operators. While we’ll consider some specific choices of these operators, I’ll mention now that we’re not limited to just ODEs for this. That is, \(u\) could be a function of \(x\), and \(L_i\) could be an differential operator in \(x\), giving us a PDE for \(u(x,t)\).

The whole idea is then: we want to somehow solve the two problems separately. That is, we want to split (1) into the subproblems \[\begin{equation} \begin{cases} \frac{\partial u_1}{\partial t} &= L_1 (u_1)\\ \frac{\partial u_2}{\partial t} &= L_2 (u_2) \end{cases}\tag{2} \end{equation}\] Say we were able to solve the system (2), how do we combine \(u_1\) and \(u_2\) to construct the solution \(u(t)\) to the full system (1)?

## Linearity, Matrix exponentials

Suppose that \(L_1, L_2\) are both **linear** operators, and \(u(t)\) does not depend on space, so \(L_1, L_2\) are just matrices. The solution to (1) can be constructed by the matrix exponential
\[\begin{equation}
u(t) = e^{(L_1+L_2)t}u(0).
\end{equation}\]
Using the same technique, we know the solutions to (2) are
\[\begin{equation}
u_1(t) = e^{L_1 t}u_1(0), \qquad u_2(t) e^{L_2 t} u_2(0).
\end{equation}\]
Because we’re cooking up \(u_1,u_2\) we can choose initial conditions on \(u_1, u_2\) however we’d like. Say, we take \(u_1(0)u_2(0)=u(0)\). Is this enough to make the answer obvious?
If we then consider
\[\begin{equation}
u_*(t) := u_1(t)u_2(t) = e^{L_1 t} u_1(0) e^{L_2 t} u_2(0) = e^{L_1 t} e^{L_2 t} u(0),
\end{equation}\]
does this give us the solution to our original problem? **No!**

Unfortunately, the properties of *matrix* exponentials tell us that (in general)
\[\begin{equation}
e^{A+B} \neq e^{A}e^{B},\tag{3}
\end{equation}\]
and therefore
\[\begin{equation}
e^{L_1 t} e^{L_2 t} \neq e^{(L_1+L_2)t}.
\end{equation}\]

## Commutators, BCH formula

Is \tag{3} ever true? That is, can we ever naively combine matrix exponentials? It turns out, the answer to this is *yes*, if \(A,B\) commute. That is
\[\begin{equation}
AB = BA \qquad \leftrightarrow \qquad e^{A}e^{B} = e^{A+B}.
\end{equation}\]
We’ll look at concrete examples later, but it turns out that this is pretty rare. Is all hope lost? Is there any way to recover an approximation for \(u(t)\) from this technique?

The answer (somewhat surprisingly) comes from Lie theory, in the form of the Baker-Campell-Hausdorff (BCH) formula. To state the formula, first I must mention the **commutator** of two matrices \(A,B\), defined by
\[\begin{equation}
[A,B] := AB - BA.
\end{equation}\]
Note that if \(A,B\) commute, \([A,B] = 0\). With this, we can state the classical result.

### Baker-Campell-Hausdorff formula

Consider the matrices \(A,B,C\) such that \[\begin{equation} e^{A}e^{B} = e^{C} \end{equation}\] then \(C\) can be computed by the series \[\begin{equation} C = A +B + \frac{1}{2} [A,B] + \frac{1}{12}[A,[A,B]]+ \cdots. \end{equation}\] The usefulness of this is hopefully apparent: even though our linear operators may not commute, we can take progressive terms of this series to get an approximation for the full solution to our system.

## Splitting techniques, errors

We’ve discussed already the possibility of some approximations, but I just wanted to associate the names used in the literature with the explicit expressions used.

The most naive approximation, but also the simplest is

### First order splitting

\[\begin{equation} e^{t(L_1+L_2)} \approx e^{tL_1} e^{tL_2}. \end{equation}\]We know from the BCH formula that if \(L_1,L_2\) commute, this is an *exact* technique. However, the BCH formula also provides the next approximation

### Second order (Strang) splitting

\[\begin{equation} e^{t(L_1+L_2)} \approx e^{\frac{1}{2} tL_1} e^{tL_2} e^{\frac{1}{2}t L_1}. \end{equation}\]This approximation was popularized by Gil Strang and associated with his name. The perhaps more useful description of second order comes from the observation that the BCH formula actually provides error estimates
\[\begin{equation}
e^{t(L_1+L_2)} - e^{\frac{1}{2} tL_1} e^{tL_2} e^{\frac{1}{2}t L_1} \sim \mathcal{O}(h^3), \tag{4}
\end{equation}\]
which describes the *local error* introduced by the approximation, and therefore the global error
\[\begin{equation}
\text{global error} = \text{(# of steps)} \times \text{(error at each step)} \sim \frac{1}{h} \times h^3 \sim \mathcal{O}(h^2),
\end{equation}\]
hence the name *second order splitting*.

## Examples

### Advection-Diffusion

In this section, I’ll discuss a few examples, most of which are taken from these lecture notes.

Consider the classical advection-diffusion equation
\[\begin{equation}
\partial_t u = -v u_x + Du_{xx}.\tag{5}
\end{equation}\]
Here, we have two clear parts, so take
\[\begin{equation}
L_1 u := -v u_x, \qquad L_2 u := Du_{xx}.
\end{equation}\]
What happens when we apply splitting to this problem? Let’s use the BCH formula to see how these operators interact:
\[\begin{equation}
L_1 L_2 u = -v(D u_{xx})_{x} = D (-v u_{x})_{xx} = L_2 L_1 u,
\end{equation}\]
so $[L_1,L_2] =0 $. Therefore, we know that first order splitting is *exact* in this case, meaning that
\[\begin{equation}
u(x,t) = e^{(L_1+L_2)t} u(0) = e^{tL_1} e^{tL_2} u_0(x).
\tag{6}
\end{equation}\]
If we unpack (6) a bit, we’ll find that it provides a surprisingly intuitive result. Consider the two rightmost terms: \(\exp{(t L_2)} u(0)\). We know \(L_2\) is just the pure diffusion operator, so the exponential is the solution operator (generator of the semi-group, if those words mean anything) for the heat/diffusion equation equation applied to some initial condition. Consequently,
\[\begin{equation}
e^{tL_2} u_0(x) = u^\star(x,t) \quad \leftrightarrow \quad \partial_ tu^\star = D \partial_{xx} u^\star, \,\, u^\star(x,0) = u_0(x).
\end{equation}\]
Now, we must apply the \(L_1\) exponential, but this is simply the solution operator for the pure advection equation applied to an initial condition, which we know produces a traveling wave solution. Consequently, we immediately have
\[\begin{equation}
e^{tL_1} u^\star(x,t) = u^\star(x-vt,t).
\end{equation}\]
While this solution can easily be obtained by a Fourier transform, the splitting technique breaks down each operator’s contribution to the full solution to (5), in (what I think are) intuitive parts.

### Reaction-Diffusion

In the last example, we saw that *sometimes* operators commute and work out nicely, however this is usually not the case. I started this post by suggesting that reaction-diffusion is a natural place for splitting to arise, so let’s consider the simplest scenario of such. Consider the PDE
\[\begin{equation}
\partial_t u = D \partial_{xx} u + (a-b u),
\end{equation}\]
and call \(L_1\) the diffusive part and \(L_2\) the reaction (just decay in this case). We can easily check that these do not commute by comparing
\[\begin{equation}
L_1 L_2 u = -bDu_{xx} \qquad \neq \qquad L_2 L_1 u = a-bD_{uxx},
\end{equation}\]
meaning this is a case where first order splitting is exact, but we’ve established a few appropriate approximations.

Here, I’ll show a brief (crude) implementation of Strang splitting in R, using Crank-Nicolson for the diffusion part and forward Euler for the reaction term.^{1}

We first just establish some preliminary constants for the simulation

```
dt = .001; # step size in time
dx = .05; # step size in space
T = 1; # max time
D = 1; # diff coefficient
b = .6; # decay rate
a = 1.0; # birth rate
L = 1; # length of domain
Nt = round(T/dt) # number of time stpes
Nx = round(L/dx) # number of spatial points
x = seq(0, L, length.out=Nx+1) # Mesh points in space
uvals = matrix(nrow=(Nx+1),ncol=(Nt+1)) # matrix of solutions
uvals[,1] = sin(pi*x/L) # just some nice initial condition that satisfies BCs
```

```
## reaction term f(u) = a-bu
f <- function(u, x,t){
return (a-b*u)
}
```

From there, we just need to specify how we’ll numerically solve each of the components. For the reaction, we just simply take an Euler step

```
# forward euler, u_{i+1} = u_i + dt*f(u_i)
rxn_step = function(u,dt){
return (u + dt*f(u))
}
```

For the diffusion term, things get a little more complicated, but ultimately we’re just going to use Crank-Nicolson, implemented (probably poorly) below

```
# Construct tridiagonal matrix
tridiag <- function(upper, lower, main){
out <- matrix(0,length(main),length(main))
diag(out) <- main
indx <- seq.int(length(upper))
out[cbind(indx+1,indx)] <- lower
out[cbind(indx,indx+1)] <- upper
return(out)
}
# Crank Nicolson, NOT including rxns because of splitting
diffusion_step = function(u,dt){
mu = 0.5*D*dt/(dx^2); # 0.5*CFL number
diagonal = rep(1+2*mu, Nx+1)
lower = rep(-mu,Nx)
upper = rep(-mu,Nx)
# BCs
diagonal[1] = 1;
upper[1] = 0;
diagonal[Nx+1] = 1;
lower[Nx] = 0;
A<- tridiag(upper,lower,diagonal)
bb = numeric(Nx+1);
bb[2:(Nx)] = u[2:(Nx)]+mu*(u[1:(Nx-1)] - 2*u[2:(Nx)] + u[3:(Nx+1)]);
usol <- solve(A,bb)
return(usol)
}
```

Now, we use the Strang splitting described by (4), which says at each step: take \(0.5 \Delta t\) of a reaction step, followed by \(\Delta t\) of a diffusion step, and then finally \(0.5\Delta t\) of a reaction step.

```
for (i in 1:Nt){
uold_split = uvals[,i];
uuu_split = rxn_step(uold_split,dt/2);
uu_split = diffusion_step(uuu_split,dt);
u_split = rxn_step(uu_split,dt/2);
uvals[,i+1] = u_split;
}
```

Now we’ve constructed our solution, we see we get a nice plot.

A lot of improvements can be made to this code, but hopefully this conveys the barebones idea.## Loose ends

This post really covers only the broad strokes of splitting. We haven’t addressed some very natural questions, such as:

*if each of the split subproblems is numerically stable, is the full problem?**how do we split so that steady-state solutions are preserved?**how do we extend this to stochastic equations?*

A great discussion (and huge resource for this post) can be found in this book chapter co-authored by Gil Strang himself. A nice discussion of a Python implementation of Strang splitting can be found here.

these choices are a little dumb, as the Euler stepping limits the error to first order, but I just want to show how easy it is to implement splitting↩