Probabilistic Monads, Domains and Classical Information

Shannon's classical information theory uses probability theory to analyze channels as mechanisms for information flow. In this paper, we generalize results of Martin, Allwein and Moskowitz 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 Martin, et al., 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 x 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.


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 R n 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 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.

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:

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 Prob S : Comp → CompConv LC from the category of compact Hausdorff spaces and continuous maps, to the category of compact, convex, locally convex spaces and continuous affine maps, via Prob S (X ) = Prob(X ) and f : X → Y maps to Moreover, if the mapping x → δ x : 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 → δ x 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 r i δ x i , where r i ≥ 0, ∑ i r i = 1, and x i ∈ X for each i. We let Prob sim (X ) denote this family. The Krein-Milman Theorem implies that Prob sim (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 f (δ x ) = f (x) extends uniquely to continuous function f (∑ i≤n r i δ x i ) = ∑ i≤n r i f (x i ), and then to all of Prob(X ), by the density of the simple measures.
We conclude that the functor Prob S is left adjoint to the forgetful functor. In fact, Prob S defines a monad, where the unit of the adjunction is the mapping η X (x) = δ x and the multiplication µ : Prob(Prob(X )) → Prob(X ) is integration. Theorem 1. The functor Prob S 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 δ x , 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 ).
Since Prob S is a monad, each channel f : X → Prob(Y ) corresponds uniquely to a continuous, affine mapping Prob S ( f ) : Prob(X ) → Prob(Y ) in the Kleisli category K Prob S of Prob S . 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 K Prob S , we conclude that the family of morphisms K Prob S (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 category of the "spatial" monad Prob S on the category Comp. If we let m = n, then ST(n) def = ST(n, n) is also a monoid using composition in the Kleisli category: if f , g ∈ ST(n), and g : Prob(n) → Prob(n) is the extension of g, then g • f ::= g • f ∈ ST(n). We next present a second monad that gives another account of this special case.

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) → xy : S × S → S that also has an identity element, 1 S -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 ι S : Prob(S) × Prob(S) ֒→ Prob(S × S) is an embedding, we have a continuous affine map Prob(·) • ι S : Prob(S) × Prob(S) → Prob(S). This map is called convolution, and we denote (Prob(·) • ι S )(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 by Prob(φ )(µ)( f ) = f • φ dµ for any f : T → R. If µ, ν ∈ Prob(S) and f ∈ C(T, R), then where m S : S × S → S, m T : T × T → T are the semigroup operations, * S , * T denote convolution, and where 1 = follows from the fact that φ is a homomorphism. Thus Prob S : (Prob(S), * S ) → (Prob(T ), * T ) is a semigroup homomorphism. Finally, the fact that Prob(φ ) preserves the identity follows from the observation that δ x * S δ y = δ xy , which implies that δ 1 is an identity for the simple measures, and consequently for all measures since the simple measures are dense.
It follows that restricting Prob S to the subcategory CMon of Comp yields a functor Prob M : CMon → CAM into the category of locally convex compact affine monoids and continuous affine monoid maps. Theorem 2. The restriction of Prob S to Mon induces a monad Prob M 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).
Applying the same reasoning as for Prob S , we see that each continuous monoid homomorphism φ : S → Prob(T ) corresponds to a unique morphism of compact affine monoids, φ : Prob(S) → Prob(T ) in the Kleisli category K Prob M . However, in relation to classical channels, our interest is in the object level of Prob M : 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 Prob M we obtain a compact affine monoid is a compact affine monoid, this mapping extends to a morphism of compact affine monoids Since

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 Prob M to a compact group yields a compact affine monoid. However, Prob M (G) is not a group in general, so the forgetful functor from CMon does not take Prob M (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 Prob M sends each g ∈ G to δ g ∈ Prob(G), and this is a monoid -hence group -homomorphism. So  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 Prob G , we find that Prob G (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 Prob G (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 {δ g | 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 1 n δ i . Corollary 1. If G is a finite group, then Prob G (G) is the free affine monoid over G, as well as the being the free compact affine monoid over G.
consists of simple measures. If S is an affine monoid and φ : G → S is a monoid homomorphism, then φ (G) ⊆ H(1 S ), and so φ : is easily seen to be a morphism of affine monoids that satisfies φ (δ g ) = φ (g) for each g ∈ G, and φ is the unique such since Prob G (G) consists of simple measures. This shows Prob G (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 Prob G (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.

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 entropy 2 of X is defined as and the mutual information in X and Y is 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 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 This formula requires some interpretation.
1. First, the term to which H is first applied -(∑ j≤n r j C( j, 1), . . . , ∑ j≤n r j C( j, n)) -represents a distribution on Y = n obtained from pC, where p = ∑ i r i δ i 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 j C( j, 1), . . . , ∑ j≤n r j C( 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 j C( j, 1), . . . , ∑ j≤n r j C( j, n)) is a point on the polytope K ⊆ [0, 1] n the rows of C generate, so  , 1), . . . ,C(i, n)) ∈ R n+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 j C( j|1), . . . , ∑ j≤n r j C( j|n)) − ∑ i≤n r i 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  , 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.
for all r ∈ (0, 1) and all 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.
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 r 1 , . . . , r m+1 ∈ [0, 1] and and so . . , y m })).

Domains
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 = / 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 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 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.
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, 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 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.
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 Proof. The discussion in Example 4 applies to K = P n to show that Con(P n ) is a domain, and Con n (P n ) is closed in Con(P n ) under filtered intersections. Since R n 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 (Con n (P n ), ⊇) 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(P n ) -and hence also Con n (P n ) -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 Con n (P n ) 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 Con n (P n ), 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.

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].
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({δ i C | i ≤ n}), where δ i ∈ 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 This is clearly a closed equivalence relation, and because channels are affine maps.
Now, C(δ i ) = δ i C = C(i), the i th row of C, so C ≡ C ′ iff C and C ′ have the same set of rows vectors. Hence, where M π 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: It is routine to show that ≡ M and ≤ M are both (topologically) closed relations on any compact monoid S.
Proof. For 1., it is clear from Equation 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.
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
showing that each lies on a one-parameter semigroup ψ : ([0, 1], ·) → ST(2) by ψ(p) = Z p . Now, ψ is a homomorphism, so p < p ′ ⇒ Z p = Z p p ′ Z p ′ , while obviously, p = p ′ ⇒ S(2)Z p ∩ S(2)Z p ′ = / 0. It follows 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 Similarly, the matrices form a one parameter semigroup from I 2 to M (ST (2)), along which Cap is strictly decreasing. Now where O i is the matrix both of whose rows are (δ 1i δ 2i ), for i = 1, 2. For each fixed r ∈ [0, 1], there is a one-parameter semigroup p → p · I 2 + (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({I 2 } ∪ 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 · I n + (1 − p) · O k , where p ∈ [0, 1] and O k is the channel in M (ST(n)) all of whose rows are (0, . . . , 0, 1, 0, . . .), where the unique 1 appears in the k th entry. As in the binary case, p ≤ p ′ ⇒ Z p = Z p p ′ Z p ′ , and p = p ′ ⇒ S(n)Z p ∩ S(n)Z p ′ = / 0. So we can again conclude that Cap is strictly decreasing along this one-parameter semigroup. As in the case of n = 2, M (ST(n)) = {∑ i≤n r k O k | ∑ k r k = 1}, so the result extends verbatim to these one-parameter semigroups.

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 Con n (P n ) 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 Conv n (P n ). 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 Prob S . The second shows that ST(n) is the free compact, affine monoid Prob M [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).
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.