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There are many situations in combinatorics in which one is running some sort of iteration algorithm to continually “improve” some object {A}; each loop of the algorithm replaces {A} with some better version {A'} of itself, until some desired property of {A} is attained and the algorithm halts. In order for such arguments to yield a useful conclusion, it is often necessary that the algorithm halts in a finite amount of time, or (even better), in a bounded amount of time. (In general, one cannot use infinitary iteration tools, such as transfinite induction or Zorn’s lemma, in combinatorial settings, because the iteration processes used to improve some target object {A} often degrade some other finitary quantity {B} in the process, and an infinite iteration would then have the undesirable effect of making {B} infinite.)

A basic strategy to ensure termination of an algorithm is to exploit a monotonicity property, or more precisely to show that some key quantity keeps increasing (or keeps decreasing) with each loop of the algorithm, while simultaneously staying bounded. (Or, as the economist Herbert Stein was fond of saying, “If something cannot go on forever, it must stop.”)

Here are four common flavours of this monotonicity strategy:

  • The mass increment argument. This is perhaps the most familiar way to ensure termination: make each improved object {A'} “heavier” than the previous one {A} by some non-trivial amount (e.g. by ensuring that the cardinality of {A'} is strictly greater than that of {A}, thus {|A'| \geq |A|+1}). Dually, one can try to force the amount of “mass” remaining “outside” of {A} in some sense to decrease at every stage of the iteration. If there is a good upper bound on the “mass” of {A} that stays essentially fixed throughout the iteration process, and a lower bound on the mass increment at each stage, then the argument terminates. Many “greedy algorithm” arguments are of this type. The proof of the Hahn decomposition theorem in measure theory also falls into this category. The general strategy here is to keep looking for useful pieces of mass outside of {A}, and add them to {A} to form {A'}, thus exploiting the additivity properties of mass. Eventually no further usable mass remains to be added (i.e. {A} is maximal in some {L^1} sense), and this should force some desirable property on {A}.
  • The density increment argument. This is a variant of the mass increment argument, in which one increments the “density” of {A} rather than the “mass”. For instance, {A} might be contained in some ambient space {P}, and one seeks to improve {A} to {A'} (and {P} to {P'}) in such a way that the density of the new object in the new ambient space is better than that of the previous object (e.g. {|A'|/|P'| \geq |A|/|P| + c} for some {c>0}). On the other hand, the density of {A} is clearly bounded above by {1}. As long as one has a sufficiently good lower bound on the density increment at each stage, one can conclude an upper bound on the number of iterations in the algorithm. The prototypical example of this is Roth’s proof of his theorem that every set of integers of positive upper density contains an arithmetic progression of length three. The general strategy here is to keep looking for useful density fluctuations inside {A}, and then “zoom in” to a region of increased density by reducing {A} and {P} appropriately. Eventually no further usable density fluctuation remains (i.e. {A} is uniformly distributed), and this should force some desirable property on {A}.
  • The energy increment argument. This is an “{L^2}” analogue of the “{L^1}“-based mass increment argument (or the “{L^\infty}“-based density increment argument), in which one seeks to increments the amount of “energy” that {A} captures from some reference object {X}, or (equivalently) to decrement the amount of energy of {X} which is still “orthogonal” to {A}. Here {A} and {X} are related somehow to a Hilbert space, and the energy involves the norm on that space. A classic example of this type of argument is the existence of orthogonal projections onto closed subspaces of a Hilbert space; this leads among other things to the construction of conditional expectation in measure theory, which then underlies a number of arguments in ergodic theory, as discussed for instance in this earlier blog post. Another basic example is the standard proof of the Szemerédi regularity lemma (where the “energy” is often referred to as the “index”). These examples are related; see this blog post for further discussion. The general strategy here is to keep looking for useful pieces of energy orthogonal to {A}, and add them to {A} to form {A'}, thus exploiting square-additivity properties of energy, such as Pythagoras’ theorem. Eventually, no further usable energy outside of {A} remains to be added (i.e. {A} is maximal in some {L^2} sense), and this should force some desirable property on {A}.
  • The rank reduction argument. Here, one seeks to make each new object {A'} to have a lower “rank”, “dimension”, or “order” than the previous one. A classic example here is the proof of the linear algebra fact that given any finite set of vectors, there exists a linearly independent subset which spans the same subspace; the proof of the more general Steinitz exchange lemma is in the same spirit. The general strategy here is to keep looking for “collisions” or “dependencies” within {A}, and use them to collapse {A} to an object {A'} of lower rank. Eventually, no further usable collisions within {A} remain, and this should force some desirable property on {A}.

Much of my own work in additive combinatorics relies heavily on at least one of these types of arguments (and, in some cases, on a nested combination of two or more of them). Many arguments in nonlinear partial differential equations also have a similar flavour, relying on various monotonicity formulae for solutions to such equations, though the objective in PDE is usually slightly different, in that one wants to keep control of a solution as one approaches a singularity (or as some time or space coordinate goes off to infinity), rather than to ensure termination of an algorithm. (On the other hand, many arguments in the theory of concentration compactness, which is used heavily in PDE, does have the same algorithm-terminating flavour as the combinatorial arguments; see this earlier blog post for more discussion.)

Recently, a new species of monotonicity argument was introduced by Moser, as the primary tool in his elegant new proof of the Lovász local lemma. This argument could be dubbed an entropy compression argument, and only applies to probabilistic algorithms which require a certain collection {R} of random “bits” or other random choices as part of the input, thus each loop of the algorithm takes an object {A} (which may also have been generated randomly) and some portion of the random string {R} to (deterministically) create a better object {A'} (and a shorter random string {R'}, formed by throwing away those bits of {R} that were used in the loop). The key point is to design the algorithm to be partially reversible, in the sense that given {A'} and {R'} and some additional data {H'} that logs the cumulative history of the algorithm up to this point, one can reconstruct {A} together with the remaining portion {R} not already contained in {R'}. Thus, each stage of the argument compresses the information-theoretic content of the string {A+R} into the string {A'+R'+H'} in a lossless fashion. However, a random variable such as {A+R} cannot be compressed losslessly into a string of expected size smaller than the Shannon entropy of that variable. Thus, if one has a good lower bound on the entropy of {A+R}, and if the length of {A'+R'+H'} is significantly less than that of {A+R} (i.e. we need the marginal growth in the length of the history file {H'} per iteration to be less than the marginal amount of randomness used per iteration), then there is a limit as to how many times the algorithm can be run, much as there is a limit as to how many times a random data file can be compressed before no further length reduction occurs.

It is interesting to compare this method with the ones discussed earlier. In the previous methods, the failure of the algorithm to halt led to a new iteration of the object {A} which was “heavier”, “denser”, captured more “energy”, or “lower rank” than the previous instance of {A}. Here, the failure of the algorithm to halt leads to new information that can be used to “compress” {A} (or more precisely, the full state {A+R}) into a smaller amount of space. I don’t know yet of any application of this new type of termination strategy to the fields I work in, but one could imagine that it could eventually be of use (perhaps to show that solutions to PDE with sufficiently “random” initial data can avoid singularity formation?), so I thought I would discuss it here.

Below the fold I give a special case of Moser’s argument, based on a blog post of Lance Fortnow on this topic.

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