It is often stated that Foldable is effectively the toList class. However, this turns out to be wrong. The real fundamental member of Foldable is foldMap (which should look suspiciously like traverse, incidentally). To understand exactly why this is, it helps to understand another surprising fact: lists are not free monoids in Haskell.

This latter fact can be seen relatively easily by considering another list-like type:

data SL a = Empty | SL a :> a
instance Monoid (SL a) where
  mempty = Empty
  mappend ys Empty = ys
  mappend ys (xs :> x) = (mappend ys xs) :> x
single :: a -> SL a
single x = Empty :> x

So, we have a type SL a of snoc lists, which are a monoid, and a function that embeds a into SL a. If (ordinary) lists were the free monoid, there would be a unique monoid homomorphism from lists to snoc lists. Such a homomorphism (call it h) would have the following properties:

h [] = Empty
h (xs <> ys) = h xs <> h ys
h [x] = single x

And in fact, this (together with some general facts about Haskell functions) should be enough to define h for our purposes (or any purposes, really). So, let's consider its behavior on two values:

h [1] = single 1
h [1,1..] = h ([1] <> [1,1..]) -- [1,1..] is an infinite list of 1s
          = h [1] <> h [1,1..]

This second equation can tell us what the value of h is at this infinite value, since we can consider it the definition of a possibly infinite value:

x = h [1] <> x = fix (single 1 <>)
h [1,1..] = x

(single 1 <>) is a strict function, so the fixed point theorem tells us that x = ⊥.

This is a problem, though. Considering some additional equations:

[1,1..] <> [n] = [1,1..] -- true for all n
h [1,1..] = ⊥
h ([1,1..] <> [1]) = h [1,1..] <> h [1]
                   = ⊥ <> single 1
                   = ⊥ :> 1
                   ≠ ⊥

So, our requirements for h are contradictory, and no such homomorphism can exist.

The issue is that Haskell types are domains. They contain these extra partially defined values and infinite values. The monoid structure on (cons) lists has infinite lists absorbing all right-hand sides, while the snoc lists are just the opposite.

This also means that finite lists (or any method of implementing finite sequences) are not free monoids in Haskell. They, as domains, still contain the additional bottom element, and it absorbs all other elements, which is incorrect behavior for the free monoid:

pure x <> ⊥ = ⊥
h ⊥ = ⊥
h (pure x <> ⊥) = [x] <> h ⊥
                = [x] ++ ⊥
                = x:⊥
                ≠ ⊥

So, what is the free monoid? In a sense, it can't be written down at all in Haskell, because we cannot enforce value-level equations, and because we don't have quotients. But, if conventions are good enough, there is a way. First, suppose we have a free monoid type FM a. Then for any other monoid m and embedding a -> m, there must be a monoid homomorphism from FM a to m. We can model this as a Haskell type:

forall a m. Monoid m => (a -> m) -> FM a -> m

Where we consider the Monoid m constraint to be enforcing that m actually has valid monoid structure. Now, a trick is to recognize that this sort of universal property can be used to define types in Haskell (or, GHC at least), due to polymorphic types being first class; we just rearrange the arguments and quantifiers, and take FM a to be the polymorphic type:

newtype FM a = FM { unFM :: forall m. Monoid m => (a -> m) -> m }

Types defined like this are automatically universal in the right sense. [1] The only thing we have to check is that FM a is actually a monoid over a. But that turns out to be easily witnessed:

embed :: a -> FM a
embed x = FM $ \k -> k x
instance Monoid (FM a) where
  mempty = FM $ \_ -> mempty
  mappend (FM e1) (FM e2) = FM $ \k -> e1 k <> e2 k

Demonstrating that the above is a proper monoid delegates to instances of Monoid being proper monoids. So as long as we trust that convention, we have a free monoid.

However, one might wonder what a free monoid would look like as something closer to a traditional data type. To construct that, first ignore the required equations, and consider only the generators; we get:

data FMG a = None | Single a | FMG a :<> FMG a

Now, the proper FM a is the quotient of this by the equations:

None :<> x = x = x :<> None
x :<> (y :<> z) = (x :<> y) :<> z

One way of mimicking this in Haskell is to hide the implementation in a module, and only allow elimination into Monoids (again, using the convention that Monoid ensures actual monoid structure) using the function:

unFMG :: forall a m. Monoid m => FMG a -> (a -> m) -> m
unFMG None _ = mempty
unFMG (Single x) k = k x
unFMG (x :<> y) k = unFMG x k <> unFMG y k

This is actually how quotients can be thought of in richer languages; the quotient does not eliminate any of the generated structure internally, it just restricts the way in which the values can be consumed. Those richer languages just allow us to prove equations, and enforce properties by proof obligations, rather than conventions and structure hiding. Also, one should note that the above should look pretty similar to our encoding of FM a using universal quantification earlier.

Now, one might look at the above and have some objections. For one, we'd normally think that the quotient of the above type is just [a]. Second, it seems like the type is revealing something about the associativity of the operations, because defining recursive values via left nesting is different from right nesting, and this difference is observable by extracting into different monoids. But aren't monoids supposed to remove associativity as a concern? For instance:

ones1 = embed 1 <> ones1
ones2 = ones2 <> embed 1

Shouldn't we be able to prove these are the same, becuase of an argument like:

ones1 = embed 1 <> (embed 1 <> ...)
      ... reassociate ...
      = (... <> embed 1) <> embed 1
      = ones2

The answer is that the equation we have only specifies the behavior of associating three values:

x <> (y <> z) = (x <> y) <> z

And while this is sufficient to nail down the behavior of finite values, and finitary reassociating, it does not tell us that infinitary reassociating yields the same value back. And the "... reassociate ..." step in the argument above was decidedly infinitary. And while the rules tell us that we can peel any finite number of copies of embed 1 to the front of ones1 or the end of ones2, it does not tell us that ones1 = ones2. And in fact it is vital for FM a to have distinct values for these two things; it is what makes it the free monoid when we're dealing with domains of lazy values.

Finally, we can come back to Foldable. If we look at foldMap:

foldMap :: (Foldable f, Monoid m) => (a -> m) -> f a -> m

we can rearrange things a bit, and get the type:

Foldable f => f a -> (forall m. Monoid m => (a -> m) -> m)

And thus, the most fundamental operation of Foldable is not toList, but toFreeMonoid, and lists are not free monoids in Haskell.

[1]: What we are doing here is noting that (co)limits are objects that internalize natural transformations, but the natural transformations expressible by quantification in GHC are already automatically internalized using quantifiers. However, one has to be careful that the quantifiers are actually enforcing the relevant naturality conditions. In many simple cases they are.

A couple of weeks back one of my coworkers brought to my attention a several hour long workshop in Japan to go over and describe a number of my libraries, hosted by TANAKA Hideyuki — not the voice actor, I checked!

I was incredibly honored and I figured that if that many people (they had 30 or so registered attendees and 10 presentations) were going to spend that much time going over software that I had written, I should at least offer to show up!

I'd like to apologize for any errors in the romanization of people's names or misunderstandings I may have in the following text. My grasp of Japanese is very poor! Please feel free to send me corrections or additions!


Sadly, my boss's immediate reaction to hearing that there was a workshop in Japan about my work was to quip that "You're saying you're huge in Japan?" With him conspicuously not offering to fly me out here, I had to settle for surprising the organizers and attending via Google Hangout.

Commentary and Logs

@nushio was very helpful in getting me connected, and while the speakers gave their talks I sat on the #haskell-lens channel and Google Hangout and answered questions and provided a running commentary with more details and references. Per freenode policy the fact that we were logging the channel was announced -- well, at least before things got too far underway.

Here is the IRC session log as a gist. IKEGAMI Daisuke @ikegami__ (ikeg in the IRC log) tried to keep up a high-level running commentary about what was happening in the video to the log, which may be helpful if you are trying to follow along through each retroactively.

Other background chatter and material is strewn across twitter under the #ekmett_conf hash tag and on a japanese twitter aggregator named togetter


Recently, a fellow in category land discovered a fact that we in Haskell land have actually known for a while (in addition to things most of us probably don't). Specifically, given two categories $\mathcal{C}$ and $\mathcal{D}$, a functor $G : \mathcal{C} \rightarrow \mathcal{D}$, and provided some conditions in $\mathcal{D}$ hold, there exists a monad $T^G$, the codensity monad of $G$.

In category theory, the codensity monad is given by the rather frightening expression:

$ T^G(a) = \int_r \left[\mathcal{D}(a, Gr), Gr\right] $


Andrej Bauer recently gave a really nice talk on how you can exploit side-effects to make a faster version of Martin Escardo's pseudo-paradoxical combinators.

A video of his talk is available over on his blog, and his presentation is remarkably clear, and would serve as a good preamble to the code I'm going to present below.

Andrej gave a related invited talk back at MSFP 2008 in Iceland, and afterwards over lunch I cornered him (with Dan Piponi) and explained how you could use parametricity to close over the side-effects of monads (or arrows, etc) but I think that trick was lost in the chaos of the weekend, so I've chosen to resurrect it here, and improve it to handle some of his more recent performance enhancements, and show that you don't need side-effects to speed up the search after all!


Today I hope to start a new series of posts exploring constructive abstract algebra in Haskell.

In particular, I want to talk about a novel encoding of linear functionals, polynomials and linear maps in Haskell, but first we're going to have to build up some common terminology.

Having obtained the blessing of Wolfgang Jeltsch, I replaced the algebra package on hackage with something... bigger, although still very much a work in progress.


Today I'll show that you can derive a Monad from any old Comonad you have lying around.


Last time, I started exploring whether or not Codensity was necessary to improve the asymptotic performance of free monads.

This time I'll show that the answer is no; we can get by with something smaller.


I've uploaded a package named rad to Hackage for handling reverse-mode automatic differentiation in Haskell.

Two concepts come up when talking about information retrieval in most standard documentation, Precision and Recall. Precision is a measure that tells you if your result set contains only results that are relevant to the query, and recall tells you if your result set contains everything that is relevant to the query.

The formula for classical precision is:

Precision Formula

However, I would argue that the classical notion of Precision is flawed, in that it doesn't model anything we tend to care about. Rarely are we interested in binary classification, instead we want a ranked classification of relevance.


I was asked to give two talks at the Boston Area Haskell User Group for this past Tuesday. The first was pitched at a more introductory level and the second was to go deeper into what I have been using monoids for lately.

The first talk covers an introduction to the mathematical notion of a monoid, introduces some of the features of my Haskell monoids library on hackage, and starts to motivate the use of monoidal parallel/incremental parsing, and the modification use of compression algorithms to recycle monoidal results.

The second talk covers a way to generate a locally-context sensitive parallel/incremental parser by modifying Iteratees to enable them to drive a Parsec 3 lexer, and then wrapping that in a monoid based on error productions in the grammar before recycling these techniques at a higher level to deal with parsing seemingly stateful structures, such as Haskell layout.

  1. Introduction To Monoids (PDF)
  2. Iteratees, Parsec and Monoids: A Parsing Trifecta (PDF)

Due to a late start, I was unable to give the second talk. However, I did give a quick run through to a few die-hards who stayed late and came to the Cambridge Brewing Company afterwards. As I promised some people that I would post the slides after the talk, here they are.

The current plan is to possibly give the second talk in full at either the September or October Boston Haskell User Group sessions, depending on scheduling and availability.

[ Iteratee.hs ]

Some people have requested my slides from the short talk I gave about monoids and monoidal parsing at Hac Phi. So, here they are.

There will be more to come at the next Boston Haskell User Group in August, where it looks like I'll be giving two short talks covering monoids. I may use the monoidal parsing engine from Kata as an example for the advanced talk if I have time and will start to cover parsing larger classes of grammars in general (regular languages, CFGs/TIGs, TAGs, PEGs, LALR, attribute-grammars, etc.)

About a year back I posted a field guide of recursion schemes on this blog and then lost it a few months later when I lost a couple of months of blog entries to a crash. I recently recovered the table of recursion schemes from the original post thanks to Google Reader's long memory and the help of Jeff Cutsinger.

The following recursion schemes can be found in category-extras, along with variations on the underlying themes, so this should work as a punch-list.

Scheme Code Description
catamorphism Cata tears down a structure level by level
paramorphism*† Para tears down a structure with primitive recursion
zygomorphism*† Zygo tears down a structure with the aid of a helper function
histomorphism† Histo tears down a structure with the aid of the previous answers it has given.
prepromorphism*† Prepro tears down a structure after repeatedly applying a natural transformation
Scheme Code Description
anamorphism† Ana builds up a structure level by level
apomorphism*† Apo builds up a structure opting to return a single level or an entire branch at each point
futumorphism† Futu builds up a structure multiple levels at a time
postpromorphism*† Postpro builds up a structure and repeatedly transforms it with a natural transformation
Scheme Code Description
hylomorphism† Hylo builds up and tears down a virtual structure
chronomorphism† Chrono builds up a virtual structure with a futumorphism and tears it down
with a histomorphism
synchromorphism Synchro a high level transformation between data structures using a third data structure to queue intermediate results
exomorphism Exo a high level transformation between data structures from a trialgebra to a bialgebraga
metamorphism Erwig a hylomorphism expressed in terms of bialgebras
metamorphism Gibbons A fold followed by an unfold; change of representation
dynamorphism† Dyna builds up a virtual structure with an anamorphism and tears it down with a histomorphism
Elgot algebra Elgot builds up a structure and tears it down but may shortcircuit the process during construction
Elgot coalgebra Elgot builds up a structure and tears it down but may shortcircuit the process during deconstruction

* This gives rise to a family of related recursion schemes, modeled in category-extras with distributive law combinators
† The scheme can be generalized to accept one or more F-distributive (co)monads.

Grant B. asked me to post the derivation for the right and left Kan extension formula used in previous Kan Extension posts (1,2). For that we can turn to the definition of Kan extensions in terms of ends, but first we need to take a couple of steps back to find a way to represent (co)ends in Haskell.


I think I may spend a post or two talking about Kan extensions.

They appear to be black magic to Haskell programmers, but as Saunders Mac Lane said in Categories for the Working Mathematician:

All concepts are Kan extensions.

So what is a Kan extension? They come in two forms: right- and left- Kan extensions.

First I'll talk about right Kan extensions, since Haskell programmers have a better intuition for them.


> import Control.Arrow ((|||),(&&&),left)
> newtype Mu f = InF { outF :: f (Mu f) }

I want to talk about a novel recursion scheme that hasn't received a lot of attention from the Haskell community and its even more obscure dual -- which is necessarily more obscure because I believe this is the first time anyone has talked about it.

Jiri Adámek, Stefan Milius and Jiri Velebil have done a lot of work on Elgot algebras. Here I'd like to translate them into Haskell, dualize them, observe that the dual can encode primitive recursion, and provide some observations.

You can kind of think an Elgot algebra as a hylomorphism that cheats.

> elgot :: Functor f => (f b -> b) -> (a -> Either b (f a)) -> a -> b
> elgot phi psi = h where h = (id ||| phi . fmap h) . psi


Ok, I decided to take a step back from my flawed approach in the last post and play with the idea of power series of functors from a different perspective.

I dusted off my copy of Herbert Wilf's generatingfunctionology and switched goals to try to see some well known recursive functors or species as formal power series. It appears that we can pick a few things out about the generating functions of polynomial functors.

As an example:

Maybe x = 1 + x

Ok. We're done. Thank you very much. I'll be here all week. Try the veal...

For a more serious example, the formal power series for the list [x] is just a geometric series:


The post below will only compile on a version of GHC >= 6.9, since it uses type families.