In mathematics, Kingman's subadditive ergodic theorem is one of several ergodic theorems. It can be seen as a generalization of Birkhoff's ergodic theorem.[1]
Intuitively, the subadditive ergodic theorem is a kind of random variable version of Fekete's lemma (hence the name ergodic).[2] As a result, it can be rephrased in the language of probability, e.g. using a sequence of random variables and expected values.  The theorem is named after John Kingman.
Statement of theorem
Let  be a measure-preserving transformation on the probability space
 be a measure-preserving transformation on the probability space  , and let
, and let  be a sequence of
 be a sequence of  functions such that
 functions such that  (subadditivity relation). Then
 (subadditivity relation). Then
 
for  -a.e. x, where g(x) is T-invariant.
-a.e. x, where g(x) is T-invariant. 
In particular, if T is ergodic, then g(x) is a constant.
Equivalent statement
Given a family of real random variables  , with
, with  , such that they are subadditive in the sense that
, such that they are subadditive in the sense that Then there exists a random variable
Then there exists a random variable  such that
 such that  ,
,  is invariant with respect to
 is invariant with respect to  , and
, and  a.s..
 a.s.. 
They are equivalent by setting
 with with ; ;
 with with . .
Proof
Proof due to (J. Michael Steele, 1989).[3]
Subadditivity by partition
Fix some  . By subadditivity, for any
. By subadditivity, for any  
  
 
We can picture this as starting with the set  , and then removing its length
, and then removing its length tail.
 tail.
Repeating this construction until the set  is all gone, we have a one-to-one correspondence between upper bounds of
 is all gone, we have a one-to-one correspondence between upper bounds of  and partitions of
 and partitions of  .
.
Specifically, let  be a partition of
 be a partition of  , then we have
, then we have  
Constructing g
Let  , then it is
, then it is  -invariant.
-invariant.
By subadditivity,  
  
Taking the  limit, we have
 limit, we have  We can visualize
 We can visualize  as hill-climbing on the graph of
 as hill-climbing on the graph of  . If
. If  actually causes a nontrivial amount of hill-climbing, then we would get a spatial contraction, and so
 actually causes a nontrivial amount of hill-climbing, then we would get a spatial contraction, and so  does not preserve measure. Therefore
 does not preserve measure. Therefore  a.e.
 a.e.
Let  , then
, then  and since both sides have the same measure, by squeezing, they are equal a.e..
 and since both sides have the same measure, by squeezing, they are equal a.e..
That is,  , a.e..
, a.e..
Now apply this for all rational  .
.
Reducing to the case of gn ≤ 0
By subadditivity, using the partition of  into singletons.
 into singletons.  Now, construct the sequence
 Now, construct the sequence  which satisfies
 which satisfies  for all
 for all  .
.
By the special case,  converges a.e. to a
 converges a.e. to a  -invariant function.
-invariant function.
By Birkhoff's pointwise ergodic theorem, the running average  converges a.e. to a
converges a.e. to a  -invariant function. Therefore, their sum does as well.
-invariant function. Therefore, their sum does as well.
Bounding the truncation
Fix arbitrary  , and construct the truncated function, still
, and construct the truncated function, still  -invariant:
-invariant:  With these, it suffices to prove an a.e. upper bound
 With these, it suffices to prove an a.e. upper bound since it would allow us to take the limit
since it would allow us to take the limit  , then the limit
, then the limit  , giving us a.e.
, giving us a.e.
 And by squeezing, we have
And by squeezing, we have  converging a.e. to
 converging a.e. to  .
Define two families of sets, one shrinking to the empty set, and one growing to the full set. For each "length"
.
Define two families of sets, one shrinking to the empty set, and one growing to the full set. For each "length"  , define
, define 
  Since
Since  , the
, the  family shrinks to the empty set.
 family shrinks to the empty set.
Fix  . Fix
. Fix  . Fix
. Fix  . The ordering of these qualifiers is vitally important, because we will be removing the qualifiers one by one in the reverse order.
. The ordering of these qualifiers is vitally important, because we will be removing the qualifiers one by one in the reverse order.
To prove the a.e. upper bound, we must use the subadditivity, which means we must construct a partition of the set  . We do this inductively:
. We do this inductively:
Take the smallest  not already in a partition.
 not already in a partition.
If  , then
, then  for some
 for some  . Take one such
. Take one such  – the choice does not matter.
 – the choice does not matter.
If  , then we cut out
, then we cut out  . Call these partitions "type 1". Else, we cut out
. Call these partitions "type 1". Else, we cut out  . Call these partitions "type 2".
. Call these partitions "type 2".
Else, we cut out  . Call these partitions "type 3".
. Call these partitions "type 3".
Now convert this partition into an inequality:  where
 where  are the heads of the partitions, and
 are the heads of the partitions, and  are the lengths.
 are the lengths.
Since all  , we can remove the other kinds of partitions:
, we can remove the other kinds of partitions:  By construction, each
 By construction, each  , thus
, thus  Now it would be tempting to continue with
 Now it would be tempting to continue with  , but unfortunately
, but unfortunately  , so the direction is the exact opposite. We must lower bound the sum
, so the direction is the exact opposite. We must lower bound the sum  .
.
The number of type 3 elements is equal to If a number
If a number  is of type 2, then it must be inside the last
 is of type 2, then it must be inside the last  elements of
 elements of  . Thus the number of type 2 elements is at most
. Thus the number of type 2 elements is at most  . 
Together, we have the lower bound:
. 
Together, we have the lower bound: 
 
Peeling off the first qualifier
Remove the  qualifier by taking the
 qualifier by taking the  limit.
 limit.
By Birkhoff's pointwise ergodic theorem, there exists an a.e. pointwise limit satisfying
  satisfying
![{\displaystyle \int {\bar {1}}_{B_{L}}=\mu (B_{L});\quad {\bar {1}}_{B_{L}}(x)\in [0,1]}](./_assets_/dece87dd63ea759ccc4eacde0cd390c30988f9f0.svg) At the limit, we find that for a.e.
 At the limit, we find that for a.e.  ,
,  
Peeling off the second qualifier
Remove the  qualifier by taking the
 qualifier by taking the  limit.
 limit.
Since we have  and
and  as
 as  , we can apply the same argument used for proving Markov's inequality, to obtain
, we can apply the same argument used for proving Markov's inequality, to obtain
 for a.e.
for a.e.  .
.
In detail, the argument is as follows: since  , and
, and  , we know that for any small
, we know that for any small  , all large enough
, all large enough  satisfies
 satisfies  everywhere except on a set of size
 everywhere except on a set of size  . Thus,
. Thus, with probability
with probability  . Now take both
. Now take both  .
.
Applications
Taking  recovers Birkhoff's pointwise ergodic theorem.
 recovers Birkhoff's pointwise ergodic theorem.
Taking all  constant functions, we recover the Fekete's subadditive lemma.
 constant functions, we recover the Fekete's subadditive lemma.
Kingman's subadditive ergodic theorem can be used to prove statements about Lyapunov exponents. It also has applications to percolations and longest increasing subsequence.[4]
Longest increasing subsequence
To study the longest increasing subsequence of a random permutation  , we generate it in an equivalent way. A random permutation on
, we generate it in an equivalent way. A random permutation on  is equivalently generated by uniformly sampling
 is equivalently generated by uniformly sampling  points in a square, then find the longest increasing subsequence of that.
 points in a square, then find the longest increasing subsequence of that.
Now, define the Poisson point process with density 1 on  , and define the random variables
, and define the random variables  to be the length of the longest increasing subsequence in the square
 to be the length of the longest increasing subsequence in the square  . Define the measure-preserving transform
. Define the measure-preserving transform  by shifting the plane by
 by shifting the plane by  , then chopping off the parts that have fallen out of
, then chopping off the parts that have fallen out of  .
.
The process is subadditive, that is,  . To see this, notice that the right side constructs an increasing subsequence first in the square
. To see this, notice that the right side constructs an increasing subsequence first in the square  , then in the square
, then in the square  , and finally concatenate them together. This produces an increasing subsequence in
, and finally concatenate them together. This produces an increasing subsequence in  , but not necessarily the longest one.
, but not necessarily the longest one. 
Also,  is ergodic, so by Kingman's theorem,
 is ergodic, so by Kingman's theorem,  converges to a constant almost surely. Since at the limit, there are
 converges to a constant almost surely. Since at the limit, there are  points in the square, we have
 points in the square, we have  converging to a constant almost surely.
 converging to a constant almost surely.
References