I spent pretty much the entirety of April 2012 writing an essay with the title *Multiplicative Coalescence*, as part of my Part III exams. As the results have now been published, this is probably an acceptable time to publish it here, in case anyone is interested. I certainly enjoyed writing the review, and hopefully some people may find it an interesting introduction to the more analytical side of the topic.

# Tag Archives: smoluchowski equation

# Coalescence 2: Marcus-Lushnikov Processes and a neat equivalence

Last time, discussed the Smoluchowski equations which define an infinite volume mean-field model for coalescence. Now we consider a stochastic coalescent model, the *Marcus-Lushnikov process*. Here, we start with a finite collection of finite mass particles, with total mass *N*. Then we define a process by demanding that given particles with masses *x *and *y *coalesce into a single particle with mass *x + y *at rate *K(x,y)/N*.

This can be formalised as a continuous-time Markov chain in different ways. The underlying state space consists of the set of unordered multisets of positive integers which sum to *N*. But rather than considering the configurations of masses themselves, it is sometimes more convenient to take the state space to be:

.

Here records the number of particles with mass *x*, and the constraint controls conservation of mass. Writing out a transition of this Markov chain is notationally annoying, but they have simple transition rates. The rate of transition from to the state where masses *x* and *y *have coalesced is given by

.

Therefore, the Marcus-Lushnikov process is the process of component sizes in the finite particle setting of coalescence. The existence and mode of convergence of these processes to the deterministic solutions to Smoluchowski’s equation are of particular interest in many models.

As discussed in the previous post, there is an obvious heuristic link between the multiplicative coalescent and random graph processes. An easy but interesting explicit equivalence can be drawn between the Marcus-Lushnikov process with kernel *K(x,y) = xy *and monodisperse (that is, starting with *N *unit mass particles) initial conditions and a specific random graph structure.

**Proposition: **The process with the conditions above is equivalent to the process of component sizes in .

*Proof: *First, observe that we can couple the latter process in the obvious way by associating a U[0,1] random variable with each of the edges. The edge *e* is included at time *t *iff . In this process, the appearances of different edges are independent and

.

Therefore the waiting times for a given edge to appear are independent RVs. In particular, the edge process is memoryless, hence the component size process is Markov. Then it is genuinely obvious that the rate at which an edge joining given distinct components of sizes *x *and *y *appears is . So the evolution is exactly the same as the Marcus-Lushnikov process, and the initial configuration is monodisperse.

# Coalescence 1: What is it, and why do we care?

As part of Part III, instead of sitting an extra exam paper I am writing an essay. I have chosen the topic of ‘Multiplicative Coalescence’. I want to avoid contravening plagiarism rules, which don’t allow you to quote your own words without a proper citation, which I figure is tricky on a blog, nor open publishing of anything you intend to submit. So just to be absolutely sure, I’m going to suppress this series of posts until after May 4th, when everything has to be handed in.

———–

**Informal Description**

*Coalescence* refers to a process in which particles join together over time. An example might be islands of foam on the surface of a cup of coffee. When two clumps meet, they join, and will never split. In this example, a model would need to take into account the shape of all the islands, their positions, their velocities, and boundary properties. To make things tractable, we need to distance ourselves from the idea that particles merge through collisions, which are highly physical and complicated, and instead just consider that they merge.

**Description of the Model**

When two particles coalesce, it is natural to assume that mass is conserved, as this will be necessary in any physical application. With this in mind, it makes sense to set up the entire model using only the masses of particles. Define the *kernel K(x,y) *which describes the relative rate or likelihood of the coalescence *{x,y} -> x+y*. This has a different precise meaning in different contexts. Effectively, we are making a mean-field assumption that all the complications of a physical model as described above can be absorbed into this coalescent kernel, either because the number of particles is large, or because the other effects are small.

When there is, initially, a finite number of particles, the process is stochastic. Coalescence almost surely happen one at a time, and so we can view the process as a continuous time Markov Chain with state space the set of relevant partitions of the total mass present. The transition rate *p(A,B) *is given by *K(x,y) *when the coalescence *{x,y} -> x+y *transforms partition *A *into *B*, and 0 otherwise. An observation is that the process recording the number of *{x,y} -> x+y *coalescences is an inhomogeneous Poisson process with local intensity *n(x,t)n(y,t)K(x,y) *where *n(x,t) *is the number of particles with mass *x *at time *t*.

This motivates the move to an infinite-volume setting. Suppose that there are infinitely many particles, so that coalescences are occurring continuously. The rate of *{x,y} -> x+y *coalescences is still *n(x,t)n(y,t)K(x,y) *but now *n(x,t) *specifies the density of particles with mass *x *at time *t*. Furthermore, because of the continuum framework, this rate is now *deterministic* rather than *stochastic*. This is extremely important, as by removing the probability from a probabilistic model, it can be treated as a large but simple ODE.

**Two Remarks**

1) Once this introduction is finished, we shall be bringing our focus onto multiplicative coalescence, where *K(x,y) = xy*. In particular, this is a homogeneous function, as are the other canonical kernels. This means that considering *K(x,y) = cxy* is the same as performing a constant factor time-change when *K(x,y) = xy*. Similarly, it is not important how the density *n(x,t) *is scaled as this can also be absorbed with a time-change. In some contexts, it will be natural and useful to demand that the total density be 1, but this will not always be possible. In general it is convenient to absorb as much as possible into the time parameter, particularly initial conditions, as will be discussed.

2) Working with an infinite volume of particles means that mass is no longer constrained to finitely many values. Generally, it is assumed that the masses are *discrete*, taking values in the positive integers, or *continuous*, taking values in the positive reals. In this case, the rate of coalescences between particles with masses in *(x, x+dx)* and *(y,y+dy) *is *n(x,t)n(y,t)K(x,y)dxdy*. The main difference between these will arise when we try to view the process as limits of finite processes. Continue reading