We continue our discussion of Dilworth’s theorem from last time. To recall, we proved by (a very algorithmic) induction that, if is a finite poset whose largest antichain has
elements, then
is the union of
chains. Today we’ll see how a batch of standard (and important) techniques can prove the theorem for general
, from the finite case. As before, we will often refer to decompositions of
into chains as colorings of
, which will always be subject to the restriction that two similarly colored elements be comparable. When we wish to emphasize this property, we may employ the term consistent coloring. It is easy to see that colorings using
colors are equivalent to decompositions into
chains.
The standard move for infinite would be Zorn’s lemma. By going carefully through the second part of our proof, where we “concentrate” each color on top of only one chain, we see that it can be adapted to arbitrary
without too much trouble: instead of looking for the squiggle with the smallest dark red element in its upper monochromatic set, we choose one which has “arbitrarily small” dark red elements there, and put all the dark red on top of it. This gives us the incremental part of a standard argument by Zorn’s Lemma, but our increment potentially involves a lot of fiddling with the chains, so we will probably not be able to define a very demanding order on the set
of partially-built structures. As we saw, this will probably make it difficult to prove the existence of upper bounds for metachains.
So, let’s scale back the difficulty and consider the case of denumerable ; with countable sets, we can usually build stuff “one element at a time”. Suppose, then, that
is a countable poset without antichains bigger than
. Let
be an antichain, and start with a partial decomposition of
into chains
, where
initially. Put
for definiteness. Using the finite form of Dilworth’s theorem, whereby each finite subset of
can be arranged into
chains, we hope to find such a decomposition of all of
.
Let’s just bulldoze our way forward and see what goes wrong. Stick in whatever
it will fit (
must be comparable to at least one
, otherwise we’d have an
-antichain), and continue by putting each
in whatever
is possible. If this can be done indefinitely, we get a decomposition of
. Otherwise, we run into trouble at some
, meaning each
contains, at that point, some
not comparable to
.
An obvious reaction is to use Dilworth’s: we know that can in fact be put consistently into the chains
. We do that, and continue on our merry way.
Unfortunately, it’s not that easy. Decomposing ever larger subsets of into chains is not enough: we need to harness that information into a single decomposition of
. Let me spell it out in the simple case.
Suppose we don’t run into trouble adding elements of to the
, one at a time. How exactly do we specify a decomposition of
from that? That is, given
, to which
does it belong to? What we had in mind was pretty simple: it belongs to whatever
it was assigned to, when it was its turn to be added!
Simple as it may be, we must answer a couple of questions. First, is that decomposition well-defined, unambiguous? In this case, yes, since each is assigned a unique
in the trouble-free case. Second, is each
really a chain when all is said and done? Also yes: suppose
, with
. By construction, when
was added to
(at step
), it was comparable to all the elements that had been assigned to
at that point. In particular, since
was added earlier (at step
),
must have been comparable to
.
Contrast this with the case where we do run into trouble, say at . Invoking Dilworth’s will cause a few among
to jump around between the
. If we keep doing this indefinitely, who’s to say that all the
eventually settle down and can be assigned a definite
? And if they don’t, what should we assign them?
Maybe if we take more care in assigning each to a
, we won’t have to change it afterwards. That is, we won’t have to apply Dilworth again and again. So what should we look out for?
It isn’t very clear how to express the obstructions to avoid in a succint form, e.g. involving only a few kinds of sentence about . Therefore, we resort to another venerable custom of mathematics: just state what you want, flat out, and use the structure arising from that alone.
There are possible colors for
:
. We want to choose the one which runs into the least trouble. Suppose coloring
with
inevitably runs into trouble at
, in the sense that any extension into a coloring of
has incomparable elements of the same color. A natural choice for the color of
would be that
with the biggest
, ie, that which runs into trouble the latest.
A pleasant fact now asserts itself: there must be a without a corresponding
! That is, a choice of color for
which admits arbitrarily large extensions. It’s obvious once you think about it: if all colors
ran into dead-ends at
, there would be a largest dead-end
, and we would be unable to color beyond
. However, we know that all finite subsets, including
, admit consistent colorings.
Another way to see it is the following: let be the sequence of colors assigned to
in consistent colorings of
,
,
etc. Since there are only
possible colors, at least one must be repeated infinitely many times.
That seems as good a color to give as any; say it is
. What next? Well, since there are arbitrarily large colorings with
painted
, we can run the argument again using only those, and choose a color for
which admits arbitrarily large extensions. And so forth.
To sum up, here’s how we define a coloring of . For each
, let
be a coloring of
, that is, a map
such that two equally colored elements are comparable. Let
be a color such that arbitrarily large
have
, and let
be the subsequence of those which do. Inductively, given a subsequence of colorings
, all of which have the same first
colors, choose a subsequence
whose elements also have the same
-th color. This is possible because there are only
different colors. Finally, let the “actual” color
of
be that assigned by the
.
This process assigns each a definite color, and the result is consistent. Indeed, if
, then
extends
(being, in fact,
for some
); thus, if
and
are assigned the same color by
, they are assigned the same color by
, which, being a consistent coloring, makes
and
comparable.
So that’s it for countable . For general
, much the same idea works, with the caveat that we recast the argument in the language of ordinals (for the “step-by-step” angle) or nets in topological spaces (for the “subsequences” angle). We briefly work out the second approach. Let
be the set of finite subsets of
, and give it the structure of a directed set via
; an upper bound for two elements is just their set-theoretic union. Give
the product topology (over the discrete topology in
) and define a net
by setting
equal to a consistent coloring of
(given by finite Dilworth) plus some random coloring of
. Since
is compact by Tychonoff’s theorem, there must be a convergent subnet
. It is straightforward to check that
is a consistent coloring of all of
.
There is some crucial finiteness phenomenon underlying the results and arguments above, and it is more clearly seen in the contrapositive form of Dilworth’s theorem: if a poset cannot be decomposed into chains, then it must contain an antichain of size at least
. Therefore, to demonstrate the impossibility of such a decomposition, it suffices to pick out a finite witness: a large, but finite, antichain of
. Such a witness may be expressed by a finite number of statements of the form
.
These considerations suggest another approach to the infinite form of Dilworth’s theorem: mathematical logic. In particular, the compactness theorem for first-order logic.
Let be a language consisting of the usual symbols for variables, logical connectives and quantifiers, plus a family of constants
, a binary relation symbol
and a unary function symbol
. We can encode all the information about
in an
-theory
consisting of the poset axioms plus the sentences:
, for each
such that
;
, for each
such that
is not the case;
;
;
Suppose has a model
with constants
. We claim that the map
is an order-theoretic embedding of
in
.
First, all the are distinct: if
, then one of
,
fails to be the case (by antisymmetry), whence either
or
is in
. It follows that either
or
fails; and since
is the model of a poset,
would imply both.
Next, the first two types of sentence of obviously imply that
is order-isomorphic to its image by the map
. Therefore, if it is possible to decompose
into the union of
chains, the same goes for
. But that’s just what the last two types of sentence of
say, with the aid of the function symbol
: its realization
splits up
into
subsets (one for each
), and each subset is a chain.
To summarize: if has a model
, it can be decomposed into
chains, and has
as a subposet; thus
can be decomposed into
chains.
On the other hand, if didn’t have a model, by the compactness theorem in first-order logic there would be some finite subtheory
of
that didn’t have a model. However, a finite subtheory, having only a finite number of sentences, can only mention a finite number of the constants
; and the finite subposet of
corresponding to those constants, endowed with a decomposition into
chains (finite Dilworth), is a model for that subtheory. Therefore,
does have a model, and
is decomposable, as we wished.
Next time, we’ll look at some applications of Dilworth’s theorem in combinatorics, and its relationship to various other famous theorems in that field.