Probabilistic Monads, Domains and Classical Information
Michael Mislove∗
Tulane University
New Orleans, LA 70118

Shannon’s classical information theory [18] uses probability theory to analyze channels as mechanisms for information flow. In this paper, we generalize results from [14] for binary channels to
show how some more modern tools — probabilistic monads and domain theory in particular — can
be used to model classical channels. As initiated in [14], the point of departure is to consider the
family of channels with fixed inputs and outputs, rather than trying to analyze channels one at a time.
The results show that domain theory has a role to play in the capacity of channels; in particular, the
n × n-stochastic matrices, which are the classical channels having the same sized input as output,
admit a quotient compact ordered space which is a domain, and the capacity map factors through this
quotient via a Scott-continuous map that measures the quotient domain. We also comment on how
some of our results relate to recent discoveries about quantum channels and free affine monoids.

1

Introduction

Classical information theory has its foundations in the seminal work of Claude Shannon [18], who first
conceived of analyzing the behavior of channels using entropy and deriving a formula for channel capacity based on mutual information (cf. [6] for a modern presentation of the basic results). Recent work
of Martin, et al. [14] reveals that the theory of compact, affine monoids and domain theory can be used
to analyze the family of binary channels. In this paper, our goal is to generalize the results in [14] to the
case of n × n-channels — channels that have n input ports and n output ports. Our approach also uses
the monadic properties of probability distributions to give an abstract presentation of how channels arise,
and that clarifies the role of the doubly stochastic matrices, which are special channels. While our work
focuses on the classical case, the situation around quantum information and quantum channels is also a
concern, and we point out how our results relate to some recent work [7, 15] on quantum qubit channels
and free affine monoids. While most of the ingredients we piece together are not new, we believe the
approach we present does represent a new way in which to understand families of channels and some of
their important features.
The rest of the paper is structured as follows. In the next section, we describe three monads based
on the probability measures over compact spaces, compact monoids and compact groups. Each of these
is used to present some aspect of the classical channels. We then introduce topology, and show how
the capacity of a channel can be viewed from a topological perspective. The main result here is that
capacity is the maximum distance from the surface determined by the entropy function and the underlying polytope generated by the rows of a channel matrix, viewed as vectors in Rn for appropriate n.
This leads to a generalization of Jensen’s Lemma that characterizes strictly concave functions. Domain
theory is then introduced, as applied to the finitely-generated polytopes residing in a compact convex set,
ordered by reverse inclusion. Here we characterize when proper maps measure a domain, in the sense of
Martin [13]; a closely related result can be found in [16]. Finally, we return to the compact monoid of
n × n-stochastic matrices and show that it has a natural, algebraically-defined pre-order relative to which
∗ The

support of the US Office of Naval Research is gratefully acknowledged.

E. Kashefi, J. Krivine, F. van Raamsdonk (Eds.)
DCM 2011
EPTCS 88, 2012, pp. 87–100, doi:10.4204/EPTCS.88.8

Probabilistic Monads, Domains and Classical Information

88

capacity measures the quotient partial order, which is a compact ordered space. The capacity mapping
is also shown to be strictly monotone with respect to this pre-order, which means that strictly smaller
channel matrices have strictly smaller capacity. We close with a summary and comments about future
work.

2

Three probabilistic monads

The categorical presentation of classical information relies on three monads, each of which has the
family Prob(X ) of probability distributions over a set X as the object-level of the left adjoint. The first of
these starts with compact Hausdorff spaces, and uses several results from functional analysis: standard
references for this material are [4, 17]. We present these monads in turn:

2.1 A spatial monad
We begin with the probability measure monad over topological spaces. If X is a compact Hausdorff
space, then C(X , R), the family of continuous, real-valued functions defined on X , is a Banach space
(complete, normed linear space) in the sup-norm. The Banach space dual of C(X , R), denoted C(X , R)∗
consists of all continuous linear functionals from C(X , R) into R. C(X , R)∗ is another Banach space,
and the Riesz Representation Theorem implies this is the Banach space of Radon measures on X (those
that are both inner- and outer regular). The unit sphere of C(X , R)∗ is the family Prob(X ) of probability measures over X . If we endow Prob(X ) with the weak∗ topology (the weakest topology making all continuous linear functionals into R continuous), then Prob(X ) becomes a compact, Hausdorff
space, by the Banach-Alaoglu Theorem. Prob extends to a functor ProbS : Comp → CompConvLC from
the category of compact Hausdorff spaces and continuous maps, to the category of compact, convex,
locally convex spaces and continuous affine maps, via ProbS (X ) = Prob(X ) and f : X → Y maps to
ProbS ( f ) : ProbS (X ) → ProbS (Y ) by ProbS ( f )(µ )(A) = µ ( f −1 (A)), for each Borel set A ⊆ Y .
Moreover, if the mapping x 7→ δx : X → C(X , R)∗ sending a point to the Dirac measure it defines,
is a continuous mapping into the weak∗ topology. Since X is compact Hausdorff, Urysohn’s Lemma
implies C(X , R) separates the points of X , and so x 7→ δx is a homeomorphism onto its image. Another
application of Urysohn’s Lemma shows each Dirac measure is an extreme point Prob(X ) and in fact the
Dirac measures form the set of extreme points of Prob(X ).
A simple measure is a finite, convex combination of Dirac measures, i.e., one of the form ∑i≤n ri δxi ,
where ri ≥ 0, ∑i ri = 1, and xi ∈ X for each i. We let Probsim (X ) denote this family. The Krein-Milman
Theorem implies that Probsim (X ) is weak∗ dense among the probability measures. So, if f : X → C is
a continuous function from X into a compact subset of a locally convex vector space, then the function
b
f (δx ) = f (x) extends uniquely to continuous function b
f (∑i≤n ri δxi ) = ∑i≤n ri f (xi ), and then to all of
Prob(X ), by the density of the simple measures. Obviously, fb(δx ) = f (x).
We conclude that the functor ProbS is left adjoint to the forgetful functor. In fact, ProbS defines a monad, where the unit of the adjunction is the mapping ηX (x) = δx and the multiplication
µ : Prob(Prob(X )) → Prob(X ) is integration.
Theorem 1. The functor ProbS sending a compact space to its family of probability measure in the weak∗
topology defines a monad on the category Comp. The unit of the monad sends a point x ∈ X to the Dirac
measure δx , and the image of the unit is the set of extreme points in Prob(X ).
Definition 1. Let X and Y be compact Hausdorff spaces. A (lossless) noisy channel from X to Y is a
mapping f : X → Prob(Y ).

Mislove

89

Since ProbS is a monad, each channel f : X → Prob(Y ) corresponds uniquely to a continuous, affine
mapping ProbS ( f ) : Prob(X ) → Prob(Y ) in the Kleisli category KProbS of ProbS .
Example 1. Let n ≥ 1 and let n = {0, . . . , n − 1} be the discrete, compact space. Then Prob(n) is
the family of probability distributions on n points, and given m ≥ 1, a channel f : m → Prob(n) is an
m × n-stochastic matrix. The family ST(m, n) of m × n-stochastic matrices is then the family of lossless,
noisy channels from m to n. Moreover, from our comment about the Kleisli category KProbS , we conclude that the family of morphisms KProbS (m, Prob(n)) is ST(m, n) ֒→ Aff(Prob(m), Prob(n)), where
Aff(Prob(m), Prob(n)) is the family of continuous affine maps from Prob(m) to Prob(n).
This first probabilistic monad shows that classical channels correspond to mappings in the Kleisli
def
category of the “spatial” monad ProbS on the category Comp. If we let m = n, then ST(n) = ST(n, n)
is also a monoid using composition in the Kleisli category: if f , g ∈ ST(n), and gb: Prob(n) → Prob(n)
is the extension of g, then g ◦ f ::= gb ◦ f ∈ ST(n). We next present a second monad that gives another
account of this special case.

2.2 A monad on monoids
The second monad we define is based on the category CMon of compact monoids and compact monoid
homomorphisms. More precisely, a compact monoid is a monoid S — a non-empty set endowed with
an associative binary operation (x, y) 7→ xy : S × S → S that also has an identity element, 1S — that also
is a compact Hausdorff space for which the multiplication is continuous. We can apply the probability
functor to such an S to obtain the compact convex (Hausdorff) space Prob(S) of probability measures on
S. If we denote multiplication on S by ·, then Prob(·) : Prob(S × S) → Prob(S), and since ιS : Prob(S) ×
Prob(S) ֒→ Prob(S × S) is an embedding, we have a continuous affine map Prob(·) ◦ ιS : Prob(S) ×
Prob(S) → Prob(S). This map is called convolution, and we denote (Prob(·) ◦ ιS )(µ , ν ) = µ ∗S ν . It is
routine to show convolution is associative, so Prob(S) is a compact affine monoid.
If φ : S → T is a morphism
of compact semigroups, then Prob(φ ) : Prob(S) → Prob(T ) is defined
R
by Prob(φ )(µ )( f ) = f ◦ φ d µ for any f : T → R. If µ , ν ∈ Prob(S) and f ∈ C(T, R), then
Prob(µ ∗S ν )( f ) =

Z

S

( f ◦ φ ) d(µ ∗S ν ) =
1

=

Z Z

ZS ZS

S S

f ◦ φ ◦ mS d µ d ν

f ◦ mT ◦ (φ × φ ) d µ d ν

= (Prob(φ )(µ ) ∗T Prob(φ )(ν ))( f ),
where mS : S × S → S, mT : T × T → T are the semigroup operations, ∗S , ∗T denote convolution, and
1
where = follows from the fact that φ is a homomorphism. Thus ProbS : (Prob(S), ∗S ) → (Prob(T ), ∗T )
is a semigroup homomorphism. Finally, the fact that Prob(φ ) preserves the identity follows from the
observation that δx ∗S δy = δxy , which implies that δ1 is an identity for the simple measures, and consequently for all measures since the simple measures are dense.
It follows that restricting ProbS to the subcategory CMon of Comp yields a functor ProbM : CMon →
CAM into the category of locally convex compact affine monoids and continuous affine monoid maps.
Theorem 2. The restriction of ProbS to Mon induces a monad ProbM whose target is CAM, the category
of locally convex compact affine monoids and continuous, affine monoid homomorphisms. The unit of
the monad is again the Dirac map, and its image is again the set of extreme points of Prob(S).

Probabilistic Monads, Domains and Classical Information

90

Applying the same reasoning as for ProbS , we see that each continuous monoid homomorphism
φ : S → Prob(T ) corresponds to a unique morphism of compact affine monoids, φb : Prob(S) → Prob(T )
in the Kleisli category KProbM . However, in relation to classical channels, our interest is in the object
level of ProbM :
Example 2. We return to the example ST(n) of stochastic n × n-matrices. These arise as channels on a
discrete, n-element set. For such a set n, the selfmaps of n form a finite — hence compact — monoid. If we
denote this monoid by [n → n], then applying ProbM we obtain a compact affine monoid ProbM ([n → n]).
Now, [n → n] ֒→ [n → Prob(n)] by f 7→ ηn ◦ f , where ηn is the unit for ProbS . Since ST(n) = [n →
Prob(n)] is a compact affine monoid, this mapping extends to a morphism of compact affine monoids

∑ ri δ f

i≤k

i

7→ ∑ ri ηn ◦ fi : ProbM ([n, n]) → ST(n).
i≤k

Since {ηn ◦ f | f ∈ [n → n]} is the set of extreme points of ST(n), this morphism is surjective. In fact this
map is an isomorphism. Thus ST(n) is the free compact affine monoid over [n → n].

2.3 A monad over compact groups
Our final use of Prob to define a monad starts with CGrp, the category of compact groups and continuous
group homomorphisms. Since CGrp is a subcategory of CMon, we know that applying ProbM to a
compact group yields a compact affine monoid. However, ProbM (G) is not a group in general, so the
forgetful functor from CMon does not take ProbM (G) to a group, but instead yields a compact monoid.
But, when applied to a compact group G qua compact monoid, the unit of the monad ProbM sends
each g ∈ G to δg ∈ Prob(G), and this is a monoid — hence group — homomorphism. So, we define
a new functor H : CMon → CGrp by H(S) = H(1S ), the group of units1 of the compact monoid S. If
φ : S → T is a morphism of compact affine monoids, then φ |H(1S ) : H(1S ) → H(1T ) is a morphism of
compact groups, so H defines a functor.
Theorem 3. The functor H : CAM → CGrp is right adjoint to the functor ProbG : CGrp → CAM. In
fact, the composition H ◦ ProbG defines a monad on CGrp. Moreover, for any compact group G, we have
H(ProbG G) ≃ G. Again, the unit of the monad is the Dirac map, and the image of G in ProbG (G) is the
set of extreme points.
Example 3. We again consider the case of classical channels. Here, given n ≥ 1, ST(n) has for its group
of units the permutation group S(n). Applying ProbG , we find that ProbG (S(n)) is the free compact affine
monoid over the group S(n). But this is just the family DT(n) of doubly stochastic n × n-matrices.
We can use information about ProbG (S(n)) to conclude information about DT(n). Wendel’s Theorem [20] states that, for a compact group G, the compact monoid Prob(G) has {δg | g ∈ G} as its group
of units, and the minimal ideal (every compact monoid has one — cf. [10]) is a zero, which in fact is
Haar measure on G. In the case of S(n), this reaffirms that the units of DT(n) are the permutations of n,
and that DT(n) has a zero, which is the equidistribution ∑i≤n 1n δi .
Corollary 1. If G is a finite group, then ProbG (G) is the free affine monoid over G, as well as the being
the free compact affine monoid over G.
1 A unit

of a monoid is an element that has a two-sided inverse with respect to the identity 1S . The set of units H(1S ) = {x ∈
S | (∃y ∈ S) xy = yx = 1S } forms the largest subgroup of S that has 1S as the identity; if S is compact, then so is the group of
units.

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91

Proof. If G is a finite group, then Prob(G) = {∑i≤k ri δgi | k ∈ N, ri ∈ [0, 1], ∑i ri = 1 ∧ gi ∈ G} consists of
simple measures. If S is an affine monoid and φ : G → S is a monoid homomorphism, then φ (G) ⊆ H(1S ),
and so φ : G → H(1S ) is a group homomorphism. Then φb(∑i≤k ri δgi ) = ∑i≤k ri φ (gi ) is easily seen to be
a morphism of affine monoids that satisfies φb(δg ) = φ (g) for each g ∈ G, and φb is the unique such
since ProbG (G) consists of simple measures. This shows ProbG (G) is the free affine monoid over G,
and the Theorem implies it also is the free compact affine monoid over G since G is finite, and hence
compact.
Remark 1. In [7,15], the free affine monoid over a finite group is employed to deduce properties of quantum channels. The Corollary shows that the free affine monoid over G is nothing other than ProbG (G),
which implies it is compact, as well as telling us that it has a zero — the uniform distribution on G.
We believe other useful properties about quantum channels over finite groups can be deduced from this
observation.

3

Capacity as a topological concept

In this section we develop a new approach to understanding the capacity of a classical channel. Our idea
is to analyze capacity from a topological perspective, rather from the usual perspective of inequalities
prevalent in information theory. We begin with a brief reprise of the basics of Shannon information; the
standard reference for this material is [6].
If X : X → R is a random variable on a finite probability space (X , p), then the entropy2 of X
is defined as H(X ) = − ∑x∈X p(x) log2 p(x). If Y : Y → R is another finite random variable, then the
conditional entropy of Y given X is
H(Y |X ) =

∑

p(x)H(Y |X = x) =

x∈X

∑

x∈X

p(x)

∑

p(y|x) log 2

y∈Y

1
,
p(y|x)

(1)

and the mutual information in X and Y is
I (Y, X ) = H(Y ) − H(Y |X ) = H(X ) − H(X |Y).
If C : X → Y is a channel from inputs X to outputs Y , then C is an X × Y -matrix whose (x, y)-entry
is the conditional probability of output y occurring, given that the input was x. Each distribution p on the
inputs X then produces a corresponding distribution p ·C on Y . The capacity of a channel is given by
Cap : [Prob(X ) → Prob(Y )] → [0, 1]

by

Cap(C) =

sup H(p ·C) − H(p ·C | p),
p∈Pr(X )

i.e., Cap(C) is the supremum of the possible mutual information values I (p ·C | p) as p ranges over the
distributions on X , the set of inputs.
If we let X = Y = n and C : n → Prob(n) is a channel, then
"
#
Cap(C) = sup

∑i≤n ri δi

H( ∑ r jC( j|1), . . . , ∑ r jC( j|n)) − ∑ ri H(C(i|1), . . . ,C(i|n)) .
j≤n

j≤n

(2)

i≤n

This formula requires some interpretation.
2 We use H(X) to denote the entropy of a random variable X; this overloads our notation for the maximal subgroups of a
monoid S, but we believe the context will be sufficient to make the meaning clear.

Probabilistic Monads, Domains and Classical Information

92

1. First, the term to which H is first applied — (∑ j≤n r jC( j, 1), . . . , ∑ j≤n r jC( j, n)) — represents a
distribution on Y = n obtained from pC, where p = ∑i ri δi is a distribution on X = n. This
is the p-convex combination of the n vectors (C(1, 1), . . . ,C(1, n)), . . . , (C(n, 1), . . . ,C(n, n)) ∈
[0, 1]n comprising the rows of the channel C, where C(i, j) denotes the i, j-entry of C, interpreted as a conditional probability. Since C is a channel, each of these rows is a probability distribution on n. (As a sanity check, we see that applying H to the convex combination
(∑ j≤n r jC( j, 1), . . . , ∑ j≤n r jC( j, n)) thus makes sense, since a convex combination of probability
distributions is another such, and H applies to probability distributions.)
2. Now, the convex combination pC = (∑ j≤n r jC( j, 1), . . . , ∑ j≤n r jC( j, n)) is a point on the polytope
K ⊆ [0, 1]n the rows of C generate, so
( ∑ r jC( j, 1), . . . , ∑ r jC( j, n)), H( ∑ r jC( j, 1), . . . , ∑ r jC( j, n))) ∈ [0, 1]n × R
j≤n

j≤n

j≤n

j≤n

represents the point on the surface H generates over the polytope K.
3. Likewise the second term, ∑i≤n ri H(C(i|1), . . . ,C(i|n)) of Equation 2 is a p-convex combination,
p = ∑i≤n ri δi , of the terms H(C(i, 1), . . . ,C(i, n)), each of which is obtained by applying H to a row
of C, regarded as an elemnt of [0, 1]n . We can regard each of the points H(C(i, 1), . . . ,C(i, n)) as
being the n + 1-coordinate of a tuple (C(i, 1), . . . ,C(i, n), H(C(i, 1), . . . ,C(i, n)) ∈ Rn+1 , and hence
the p-convex combination of these points lies on the polytope these points generate.
4. Finally, the difference H(∑ j≤n r jC( j|1), . . . , ∑ j≤n r jC( j|n)) − ∑i≤n ri H(C(i|1), . . . ,C(i|n)) is the
difference in the n + 1-coordinates described under 2. and 3., so it is height of the vertical line
between the point pC in 3. and the corresponding point
(C(1, 1), . . . ,C(1, n), H(C(1, 1), . . . ,C(1, n)), . . . , (C(n, 1), . . . ,C(n, n), H(C(n, 1), . . . ,C(n, n))))
on the surface H generates over K.
Thus, Cap(C) as presented by Equation 2 takes the supremum of the differences between the value of H
at a convex combination of the rows of C and the same convex combination of H applied to the rows of
C. It is well-known that entropy H is a strictly concave function, and we now take advantage of this to
formulate a result about Cap.
Definition 2. Let K ⊆ Rn be a convex set. A function f : K → R is strictly concave if
→

→

→

→

f (r x + (1 − r) y ) > r f ( x ) + (1 − r) f ( y )
→ →

for all r ∈ (0, 1) and all x , y ∈ K.
We next recall Jensen’s Inequality:
Theorem 4 (Jensen (cf. [9])). If f : K → R is a convex function defined on a convex subset K of a vector
space V , then E f (X ) ≥ f (E(X )) for a finite random variable X : X → K, where E denotes expectation.
Moreover, if f is strictly convex, then E( f (X )) = f (E(X )) implies X is constant.
Jensen’s Inequality is a fundamental result of information theory; for example, it is crucial for proving
mutual information is non-negative, that the mutual information in a pair of random variables is 0 iff
the random variables are independent, and that entropy itself is strictly concave (cf. [6], Chapter 2).
Since f is (strictly) concave iff − f is (strictly) convex, the following generalizes Jensen’s Inequality by
strengthening the result in case f is strictly convex.

Mislove

93

Lemma 1. If f : K → R be defined from a convex subset K to R. Then the following are equivalent:
1. f is strictly concave.
→

→

2. For all r1 , . . . , rm ∈ (0, 1) and all x 1 , . . . , x m ∈ K,

∑ ri = 1

⇒

∑ ri x i
→

f

i≤m

i≤m

!

>

∑ ri f ( x i ).
→

i≤m

Proof. (ii) ⇒ (i) is obvious. For the reverse direction, we proceed by induction on m. The base case,
m = 2, is just the definition of strict concavity. So suppose (ii) holds for some m, and consider a family
→
→
r1 , . . . , rm+1 ∈ [0, 1] and x 1 , . . . , x m+1 ∈ K. Since ri ∈ (0, 1) for each i,
f

∑

i≤m+1

→
ri x i

!

!
rm+1 →
rm
→
xm+
x m+1 )
= f
∑
rm + rm+1
rm + rm+1
i≤m−1
rm+1 →
rm
→
→
>
∑ ri f ( x i ) + (rm + rm+1) f ( rm + rm+1 x m + rm + rm+1 x m+1)
i≤m−1
→
ri x i + (rm + rm+1 )(

>

∑

ri f ( x i ) + rm f ( x m ) + rm+1 f ( x m+1 )

∑

ri f ( x i ).

→

→

→

i≤m−1

=

→

i≤m+1

Notation: If K ⊆ Rn be a compact convex set, then Conn (K) denotes the family of convex polytopes
conv({x1 , . . . , xk }) generated by finite subsets {x1 , . . . , xk } ⊆ K, where k ≤ n.
def

Also note that Pn = {x ∈ [0, 1]n | ∑i xi = 1} is a compact, convex subset of [0, 1]n , which we identify
with the family Prob(n) of probability distributions on n.
Proposition 1. Let K ⊆ Rn be a compact, convex set, and let f : K → R be continuous and strictly
concave. Define
!
op
b
f : Conn (K) → R
by b
f (conv({x1 , . . . , xk })) =
sup
f ∑ ri xi − ∑ ri f (xi ).
(r1 ,...,rk )∈[0,1]k

i≤k

Then b
f is continuous and monotone with respect to reverse inclusion.

i≤k

Proof. The compactness of K implies that the family Conn (K) is closed under filtered intersecf follows
tions in the hyperspace of non-empty, closed subsets of K, and then the continuity of b
from the continuity of f . This map is clearly monotone. To show it is strictly monotone, let
conv({x1 , . . . , xk }), conv({y1 , . . . , ym }) ∈ Conn (K) with conv({x1 , . . . , xk }) ( conv({y1 , . . . , ym }). Since f
is continuous, b
f (conv({x1 , . . . , xk })) assumes its value at some point in conv({x1 , . . . , xk }), and since f is
strictly concave, this value is not assumed at xi for any index i. Thus, there is a k-tuple (r1 , . . . , rk ) ∈ (0, 1)k
with
!
b
f (conv({x1 , . . . , xk })) = f

∑ ri xi

i≤k

− ∑ ri f (xi ).
i≤k

Probabilistic Monads, Domains and Classical Information

94

Because conv({x1 , . . . , xk }) ( conv({y1 , . . . , yn }), for each i ≤ k, there is (si,1 , . . . , si,m ) ∈ [0, 1]m with
xi = ∑ j≤m si, j y j , and at least one of the families (si, j ) j≤m ∈ (0, 1)m . For this index i, we have f (xi ) =
f (∑ j≤m si, j y j ) > ∑ j≤m si, j f (y j ). Lemma 2(ii) then implies that
!

∑ ri f (xi ) = ∑ ri f ∑ si, j y j

i≤k

>∑

j≤m

i≤k

∑ ri si, j f (y j ),

i≤k j≤m

and so
b
f (conv({x1 , . . . , xk })) =
≤

f ( ∑ ri xi ) − ∑ ri f (xi )
i≤k

f

∑ ri ∑ si, j y j

i≤k

≤

i≤k

j≤m

sup
(s1 ,...,sm )∈[0,1]m

=

4

Domains

f

!!

∑ s jy j
j

−∑

∑ ri si, j f (y j )

!

j

ı≤k j≤m

− ∑ s j f (y j )

b
f (conv({y1 , . . . , ym })).

In this section, we introduce domains, which are the next ingredient in our analysis of classical channels.
For details about these structures, a standard reference is [1] or [8]. A partial order is a non-empty set
P endowed with a reflexive, antisymmetric and transitive relation. A subset D ⊆ P is directed is every
finite subset of D has an upper bound in D; P is directed complete if every directed subset of P has a least
upper bound in P. We denote directed complete partial orders as dcpos.
If P and Q are dcpos, then f : P → Q is Scott continuous if f is monotone and preserves suprema
of directed sets. An equivalent definition is available using topology: a subset U ⊆ P is Scott open if
U = ↑U = {x ∈ P | (∃u ∈ U ) u ≤ x} is an upper set, and for any directed subset D ⊆ P, if sup D ∈ U ,
then D ∩U 6= 0.
/ The Scott-open sets form a topology on P, called the Scott topology, and the functions
f : P → Q that are continuous with respect to this topology are exactly those that are Scott continuous,
as defined above.
Example 4. Let K be a compact convex subset of a topological vector space, and let Con(K) denote the
compact convex subsets of K. We can order these by reverse inclusion: C ⊑ C′ ⇔ C′ ⊆ C. A directed
family D ⊆ Con(K) is simply a filterbasis, and since K is compact and each set in D is convex, the set
T
D ∈ Con(K). Thus Con(K) is a dcpo.
We can say more. If C,C′ ∈ Con(K) and C′ ⊆ C◦ , the interior of C, then given any directed set D
T
with D ⊆ C′ , there is some E ∈ D with E ⊆ C◦ , and hence E ⊆ C. In this case we say C is way-below
C′ , and we write C ≪ C′ . In fact, if the ambient topological vector space is locally convex, then each
C′ ∈ Con(K) is the filtered intersection of those C satisfying C ≪ C′ : this follows from the fact that in any
compact Hausdorff space, each compact subset is the filtered intersection of its compact neighborhoods,
and the same applies to compact, convex sets in a locally convex topological vector space.
A domain is a dcpo satisfying {y ∈ P | y ≪ x} is directed and x = sup{y ∈ P | y ≪ x} for each
x ∈ P. The original motivation for domains was to provide semantic models for high-level programming
languages, where the fact that any Scott-continuous selfmap on a domain with least element has a least
fixed point gives a canonical model for recursion.

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Motivated by examples of selfmaps that are not Scott continuous, Martin [13] devised another approach to guaranteeing fixed points, using the concept of a measurement: A Scott-continuous function
m : P → [0, ∞)op is said to measure the content at x ∈ P if, given U ⊆ P Scott open,
x ∈ U ⇒ (∃ε > 0) mε (x) ⊆ U,
where mε (x) = {y ≤ x | m(y) − m(x) < ε }. We say that m measures P if m measures the content at x for
each x ∈ P.
For our next result, we need some notions from topology. Recall that s subset A ⊆ X of a topological
T
T
space is saturated if A = {U | A ⊆ U open}. The saturation of a subset A is {U | A ⊆ U open}, so a
set is saturated iff it is equal to its saturation. Moreover, a subset is compact iff its saturation is compact.
Definition 3. A continuous function f : X → Y between topological spaces is proper if f −1 (K) is compact
for each saturated, compact subset K ⊆ Y .
A Scott-continuous map m : P → Q between continuous posets is proper iff m−1 (↑ y) is Scott compact
for each y ∈ Q. We say that a Scott-continuous mapping m : P → Q between posets is proper at x ∈ P
if ↓ x ∩ m−1 (↑ y) is Scott compact in ↓ x for each y ∈ Q. We use [0, ∞)op to denote the non-negative real
numbers in the dual order; the following result is from [10]:
Proposition 2. Let P be a domain and let m : P → [0, ∞)op be Scott continuous. If m is proper at x ∈ P,
then the following are equivalent:
1. m measures the content at x.
2. m is strictly monotone at x ∈ P: i.e., y ≤ x & m(y) = m(x) ⇒ y = x.
In particular, a Scott-continuous, proper map m : P → [0, ∞)op measures P iff m is strictly monotone at
each x ∈ P.
def
Corollary 2. Let Pn = {x ∈ [0, 1]n | ∑i xi = 1} be the compact, convex set of distributions on n. Then
(Conn (Pn ), ⊇) is a domain, and the mapping cap : Conn (Pn ) → [0, ∞)op by
(
)
cap(conv(F)) = sup

H( ∑ rx · x) − ( ∑ rx H(x)) | rx ≥ 0, ∑ rx = 1
x∈F

x∈F

x∈F

measures (Conn (Pn ), ⊇).
Proof. The discussion in Example 4 applies to K = Pn to show that Con(Pn ) is a domain, and Conn (Pn )
is closed in Con(Pn ) under filtered intersections. Since Rn is locally convex, it’s easy to show that each
conv(F) is the intersection of sets conv(G), where conv(F) ⊆ conv(G)◦ and |G| ≤ n if |F| ≤ n. Hence
(Conn (Pn ), ⊇) is a domain.
A compact, saturated subset of [0, ∞)op has the form A = [0, r] for some r, and then cap−1 ([0, r]) =
{conv(F) | cap(conv(F) ≤ r} is closed, and hence compact, since cap is continuous and Con(Pn ) — and
hence also Conn (Pn ) — are compact in the Lawson topology (cf. [1,8]). But cap−1 ([0, r]) also is an upper
set, so it is Scott compact. Thus cap is proper, and so it measures Conn (Pn ) iff it is strictly monotone.
But the latter follows from Proposition 1, since entropy is strictly concave.
Remark: As we will see in a moment, the real import of this last result is not so much that cap measures
Conn (Pn ), per se, but rather that this implies the mapping cap is strictly monotone. This will tell us that
under an appropriate (pre-)order, having one channel strictly below another implies that the capacity of
the lower channel is strictly less than that of the larger one.
We also note that Martin and Panangaden have obtained results in [16] that can be used to derive the
Proposition 2 and Corollary 2.

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96

5

A domain-like structure of ST(n)

We have seen that ST(n) is a compact affine monoid, and we already commented that every compact
semigroup has a unique smallest ideal: i.e., a non-empty subset I ⊆ S satisfying IS ∪ SI ⊆ I. This
minimal ideal is denoted M (S), and it is closed, hence compact. For example, we noted that M (DT(n))
is a point, which is the equidistribution on n. A reference for much of the material in this section
is [10], where basic results about compact affine monoids are laid out. A good reference for results about
transformation semigroups can be found in [5].
Proposition 3. If C ∈ ST(n), then C(Prob(n)) = conv({δi | 1 ∈ n}) is a convex polytope in [0, 1]n .
Proof. If C ∈ ST(n), then C : Prob(n) → Prob(n) is an affine mapping, so it preserves the convex structure of Prob(n). It follows that C(Prob(n)) = conv({δiC | i ≤ n}), where δi ∈ Prob(n) is the Dirac
measure on i ∈ n. Thus, C(Prob(n)) is a convex polytope in Prob(n).
As a result of the Proposition, we can define a relation on ST(n) by
C ≡ C′ ⇔ C(Prob(n)) = C′ (Prob(n)).

(3)

This is clearly a closed equivalence relation, and because channels are affine maps.
C ≡ C′ ⇔ C({δi | i ∈ n}) = C′ ({δi | i ∈ n})

(4)

Now, C(δi ) = δiC = C(i), the ith row of C, so C ≡ C′ iff C and C′ have the same set of rows vectors.
Hence,
(5)
C ≡ C′ ⇔ (∃π ∈ S(n)) Mπ C = C′ ,
where Mπ is the stochastic matrix representing the permutation π ∈ S(n).
We also can obtain an algebraic representation of the relation ≡ using the monoid structure of ST(n).
Definition 4. If X is a set, then the full transformation semigroup T (X ) on X is the family of all selfmaps
of X under composition. A transformation semigroup is a subsemigroup of T (X ) for some set X .
Notation: If S ⊆ T (X ) is a transformation semigroup, then for s, s′ ∈ T (X ) and x ∈ X , the element ss′ ∈ S
denotes the function ss′ (x) = s′ (s(x)) — i.e., we use the “algebraic notation” for function application,
which agrees with our representation matrix multiplication as composition of functions.
Here are some simple observations about T (X ); the proofs are all straightforward:
1. T (X ) is a monoid whose group of units is the family of bijections of X ; if X is finite, this is just
S(|X |), the group of permutations of |X |-many letters.
2. Each constant map fx : X → X by fx (y) = x is s left zero in T (X ): if g ∈ T (X ), then g fx = fx . It
follows from general semigroup theory that M (T (X )) = { fx | x ∈ X }.
3. If S is a transformation semigroup on X and S ∩ M (T (X )) 6= 0,
/ then M (S) = S ∩ M (T (X )). Thus,
if S contains a constant map, then M (S) consists of constant maps. This follows from the fact that
g f is a constant map if either f or g is one, so the constant maps in S form an ideal.
4. If S is a transformation semigroup, then for each s ∈ S and each x ∈ X , fx ∈ S1 s ⇒ x ∈ s(X ).3
Indeed, if fx ∈ Sx ∪ {s}, then there is some s′ ∈ S with fx = s′ s, so x = s(s′ (y)) ∈ S(X ).
3 If

S is a semigroup, then S1 denotes the semigroup S with an identity element adjoined.

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5. Conversely, if M(S) = { fx | x ∈ X }, then x ∈ s(X ) ⇒ fx ∈ S! s. This follows since x ∈ s(X ) implies
x = s(y), for some y ∈ X ; if s 6= fx , then fx = fy s ∈ Ss.
Definition 5. Let S be a monoid. We define the relations ≡M and ≤M on S by:
s ≡M s′
′

s ≤M s

⇔

sM (S) = s′ M (S), and

⇔

′

(6)

sM (S) ⊆ s M (S).

It is routine to show that ≡M and ≤M are both (topologically) closed relations on any compact monoid
S.
Combining properties 4. and 5. above on transformation semigroups with equivalences 3—5 yields:
Proposition 4. Let n ≥ 1 and let C,C′ ∈ ST(n). Then
C(Prob(n)) = C′ (Prob(n)) ⇔ M (ST(n))C = M (ST(n))C′ ⇔ (∃π ∈ S(n)) Mπ C = C′ .

(7)

Since ≡M =≤M ∩ (≤M )−1 , we can form the relation ≤M / ≡M , which is a closed partial order on
ST(n)/ ≡M .
Theorem 5. Let n ≥ 1. Then
1. The relation ≡M is a left congruence on ST(n).
2. (ST(n)/≡M , ≤M /≡M ) is a compact ordered space, and the quotient map π : ST(n) → ST(n)/≡M
is an monotone map.
3. As an ordered space, ST(n)/ ≡M ≃ Conn (Pn ).
4. Thus cap : (ST(n)/ ≡M , ≤m / ≡M ) → [0, ∞)op measures (ST(n)/ ≡M , ≤m / ≡M ).
5. If C ∈ ST(n), then Cap(C) = cap(conv(C(1), . . . ,C(n))), where C(i) is the ith row of C.
Proof. For 1., it is clear from Equation 6 that C ≡M C′ implies C′′C ≡M C′′C′ . Thus, ≡M is a leftcongruence.
Because ≡M is a closed relation and ST(n) is compact, the quotient space is compact and the quotient
map is closed and continuous. The definition of the pre-order ≤M and the quotient order ≤M /≡M implies
the quotient map is monotone.
3. follows from Proposition 4, from which 4. and 5. are clear.
So we see that ST(n)) has a natural pre-order ≤M defined by its algebraic structure as a compact
monoid, and if C ≤M C′ then Cap(C) ≤ Cap(C′ ). Moreover, this pre-order turns into a partial order on
ST(n)/ ≡M , and here capacity is the mapping cap. Importantly, cap — and hence Cap on ST(n) — is
strictly monotone with respect to this partial (pre-) order. Moreover, C ≡M C′ iff S(n)C = S(n)C′ , so each
is a permutation of the rows of the other.
Example 5. Here’s an example of what our results tell us about ST(n). Recall that a Z-channel is a
binary channel of the form






0 1
1 0
1− p p
,
+p
= (1 − p)
Zp =
0 1
0 1
0
1
showing that each lies on a one-parameter semigroup ψ : ([0, 1], ·) → ST(2) by ψ (p) = Z p . Now, ψ is a
/ It follows
homomorphism, so p < p′ ⇒ Z p = Z p′ Z p′ , while obviously, p 6= p′ ⇒ S(2)Z p ∩ S(2)Z p′ = 0.
p

Probabilistic Monads, Domains and Classical Information

98

that p < p′ ⇒ π (Z p ) < π (Z p′ ) ⇒ Cap(Z p ) = cap(π (Z p )) < cap(π (Z p′ )) = Cap(Z p′ ), so the Z-channels
{Z p | 0 ≤ p ≤ 1} all have distinct capacities.
Similarly, the matrices
Z ′p








1
0
1 0
1 0
=
=p
+ (1 − p)
,
1− p p
0 1
1 0

form a one parameter semigroup from I2 to M (ST(2)), along which Cap is strictly decreasing. Now
M (ST(2)) = {r · O1 + (1 − r)O2 | 0 ≤ r ≤ 1}, where Oi is the matrix both of whose rows are (δ1i δ2i ),
for i = 1, 2. For each fixed r ∈ [0, 1], there is a one-parameter semigroup p 7→ p · I2 + (1 − p) · (r · Z p +
(1 − r) · Z ′p ), and combining the earlier results, we conclude that along this one-parameter semigroup,
Cap is strictly decreasing. Note as well that conv({I2 } ∪ M (ST (2))) is equal to the union of these
one-parameter semigroups.
We can generalize this verbatim to ST(n): define a Z-channel in ST(n) to be one of the form
Z p = p · In + (1 − p) · Ok , where p ∈ [0, 1] and Ok is the channel in M (ST(n)) all of whose rows are
(0, . . . , 0, 1, 0, . . .), where the unique 1 appears in the kth entry. As in the binary case, p ≤ p′ ⇒ Z p =
/ So we can again conclude that Cap is strictly decreasing
Z p′ Z p′ , and p 6= p′ ⇒ S(n)Z p ∩ S(n)Z p′ = 0.
p

along this one-parameter semigroup. As in the case of n = 2, M (ST(n)) = {∑i≤n rk Ok | ∑k rk = 1}, so
the result extends verbatim to these one-parameter semigroups.

6

Summary and future work

We have used an array of tools to analyze the capacity map on the set of classical channels. In Section 3,
we gave a topological interpretation of capacity of a channel: it is the maximum distance between the
surface generated by the entropy function applied to the rows of the channel, and the polytope generated
by the entropy function applied to each individual row. This suggests an method for computing capacity: the Generalized Mean Value Theorem implies the capacity is achieved at the unique place where
gradient of the capacity function is 0 — this point is unique because entropy is strongly concave. Moreover, this produces the input distribution where capacity is achieved — the celebrated Arimoto–Blahut
Algorithm [2, 3] commonly used to compute the capacity of a discrete memoryless channel is an iterative procedure that approximates the capacity, not the input distribution where its value is assumed. An
algorithm more closely related to our results can be found in [19], where the iteration scheme follows
the concavity of the capacity function using Newton’s Method.
We also applied our topological result to derive a domain-theoretic interpretation of capacity, again
using the strong concavity of entropy: the family Conn (Pn ) of polytopes with at most n vertices is a
domain, and capacity measures this domain. The important point is that this implies capacity is strictly
monotone with respect to the partial order. We found that ST(n) has a natural, algebraically-defined preorder whose associated equivalence ≡M defines a closed congruence on ST(n), and modulo which we
obtain a copy of Convn (Pn ). This implies that capacity is strictly monotone with respect to the pre-order
on ST(n).
In addition, we used the probabilistic measures on compact spaces to define three monads, each of
which tells us something about classical channels. The first realizes ST(m, n) as the morphisms on the
Kleisli category of the monad ProbS . The second shows that ST(n) is the free compact, affine monoid
ProbM [n → n], while the third shows that DT(n) is the free compact monoid over S(n), the group of
permutations on n letters. This last also implies DT(n) has a zero, which is Haar measure on S(n).

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The work discussed here concerns classical channels, but we believe that much of it could be generalized to the quantum setting. We pointed out one connection to existing work on the roll of free affine
monoids in analyzing quantum qubit channels. In any case, we have shown that the basic results of [14]
generalize from the binary case, where the capacity function of a binary channel was studied in terms
of the subinterval of [0, 1] it determines. The analog here is the polytope the rows of an n × n-stochastic
channel determine.

Acknowledgment
Many of the results in this paper are based on results in the paper [10], and I want to express my thanks
to my longtime colleague, K ARL H. H OFMANN for many enlightening and stimulating conversations
about topics related to this work.

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