This post is another gruop of fun algorithms (and data structures). These particular techniques are fun because they are a core concept that can be applied to many different situations by simply changing some structure that the algorithm is parameterized by. I won't go into much detail here, but rather provide links to articles with more depth.


The first one is the Guass-Jordan-Floyd-Warshall-McNaughton-Yamada algorithm. This algorithm solves a variety of problems, including finding shortest (max capcacity, most reliable, etc) pathes in a graph, finding an automata for a regular expression, and solving linear equations. I like the linked article because it takes some bits of abstract algebra and frames these problems in a general way, and then shows how you can see each problem as a special case of a single concept (asteration of a matrix). It also shows how these positive ring structures appear to be common in computer science. They also appear in systems of algebraic data structures, so naturally I wonder if these algorithm can be used to solve any problems in that realm.

Finger Trees

The next algorithm is the fingertree, which is parameterized by a monoid. One introduction can be found here. This data structure has good asymptotics for a range of operations, and can be used for a wide range of applications. The implementation and uses are also described here. The thing I have used this structure for is simply as a sequence structure that supports log(n) update to an index, and log(n) splitting and concatenation, but there is more to it then just that.

The core ability that this structure gives you is that it takes a computation that you would like to do over a data set, and performs your calculation incrementally. This can be the calculation of indices, as when you use it as a sequence, but can also be for statistics on data that is updated over time, without recalculating over the whole data set. It can be used to get constant time access and log time update to properties of your tree, like its size, depth, the value of a predicate over its leaves, the greatest or least element (as in a priority queue), incremental regular expression matching

A couple other notes- there is a Haskell implementation here for the general structure, and the specific use as a random-access sequence here. There is also a Haskell package implementing a tree that accumulates both upwards (from the leaves) and downwards (from the root) found here.


The concept of a lens is a fascinating exploration into structure and computation, but there are plenty of resources on lenses, and I won't be able to do it justice here. The implementation here is the main one to look at, although there are a number of others, usually much simplier then the lens package. There are also implementations in other languages of course, I'm just more familiar with Haskell. One particularly good introduction starts here.

To tie this into the common thread in this post, the properties of a lens depends on the choice of constraints on the type- in the type Lens s t a b = forall f. (Functor f) => (a -> f b) -> s -> f t the type constructor "f" must be a functor, and this gives you a lens. If you constrain this type with Applicative, you get a traversal, and so on.

You can even take this further and go up to the Optic type of this library, Optic p f s t a b = p a (f b) -> p s (f t), and look at what structure you get with a different profunctor. This can give you back lens when p is the function arrow, or Prisms with it is constrained by Choice, for example. This can lead you to different universes of lens- I once used this to create lens that could pass data between each other, although I admit I abandended that approach as too complex. This might be easier with profunctor lenses, I'm not sure.

I find this interesting because it seems like all of these universes of structures have their place, you just have to discover them.

Monads From Types

This deserves its own post, but this is another situation where you can get a lot of different systems out of a single concept. In this case, you can take many simple types, and determine how they can form a monad, and it gets you a variety of forms of computation. For example, sum types give you the Either monad for computations that can fail, the product type gives you the writer monad (requiring a monoid for one of the types), and the function arrow gives you the Reader monad. Any type (of kind * ) also gives you the Identity Monad, trivially. One thing that is really cool here is to explore the duality between types, and then the duality between the forms of computation that they give rise to (sum vs product, monad vs comonad) creating a web of different concepts that also deserves its own post. Look at the Env comonad vs the Writer monad, the traced comonad vs the Reader monad. For some reason there doesn't appear to be a comonad for sum types. It seems like you need a constraint on the type argument in order to implement extract, perhaps excluding them from normal use.

I like this because it shows one way in which data and computation are related- the building block of data structures each give a form of computation. This is common in Haskell programming, where complex structures and algorithms come out of simple data types, set up just right. This reveals interesting subtlies in these definitions, where small variations can have consequences in use, performance, or generality. One such example is shown here.

Interestingly, combinations of these can give you different forms of computation that are not the same as composing the resulting monads. Products and arrow gives you the state monad, or the Store comonad, depending on the order you compose them. See here for more variations on this concept. This also shows how sometimes contraints are necessary to get the correct structure, as in the Monoid constraint in the Writer monad, or the .


I think that is enough for now. These algorithms/structures show how sometimes a choice of type or algebraic structure can either formulate a problem in a generic way, or can give rise to a landscape of interesting objects, each with its own personality. I always enjoy seeing these different landscapes- there is something enjoyable about learning that there are whole alternate universes of thought based on a different fundemental choice, each with different uses that bend ones mind to new worlds.