Conal Elliott

The zipper is an efficient and elegant data structure for purely functional editing of tree-like data structures, first published by Gérard Huet. Zippers maintain a location of focus in a tree and support navigation operations (up, down, left, right) and editing (replace current focus).

The original zipper type and operations are customized for a single type, but it’s not hard to see how to adapt to other tree-like types, and hence to regular data types. There have been many follow-up papers to The Zipper, including a polytypic version in the paper Type-indexed data types.

All of the zipper adaptations and generalizations I’ve seen so far maintain the original navigation interface. In this post, I propose an alternative interface that appears to significantly simplify matters. There are only two navigation functions instead of four, and each of the two is specified and implemented via a fairly simple one-liner.

I haven’t used this new zipper formulation in an application yet, so I do not know whether some usefulness has been lost in simplifying the interface.

The code in this blog post is taken from the Haskell library functor-combo and completes the Holey type class introduced in Differentiation of higher-order types.

Edits:

• 2010-07-29: Removed some stray Just applications in up definitions. (Thanks, illissius.)
• 2010-07-29: Augmented my complicated definition of tweak2 with a much simpler version from Sjoerd Visscher.
• 2010-07-29: Replaced fmap (first (:ds')) with (fmap.first) (:ds') in down definitions. (Thanks, Sjoerd.)

Continue reading ‘Another angle on zippers’ »

A “one-hole context” is a data structure with one piece missing. Conor McBride pointed out that the derivative of a regular type is its type of one-hole contexts. When a data structure is assembled out of common functor combinators, a corresponding type of one-hole contexts can be derived mechanically by rules that mirror the standard derivative rules learned in beginning differential calculus.

I’ve been playing with functor combinators lately. I was delighted to find that the data-structure derivatives can be expressed directly using the standard functor combinators and type families.

The code in this blog post is taken from the Haskell library functor-combo.

See also the Haskell Wikibooks page on zippers, especially the section called “Differentiation of data types”.

I mean this post not as new research, but rather as a tidy, concrete presentation of some of Conor’s delightful insight.

Continue reading ‘Differentiation of higher-order types’ »

In Non-strict memoization, I sketched out a means of memoizing non-strict functions. I gave the essential insight but did not show the details of how a nonstrict memoization library comes together. In this new post, I give details, which are a bit delicate, in terms of the implementation described in Elegant memoization with higher-order types.

Near the end, I run into some trouble with regular data types, which I don’t know how to resolve cleanly and efficiently.

Continue reading ‘Details for non-strict memoization, part 1’ »

A while back, I got interested in functional memoization, especially after seeing some code from Spencer Janssen using the essential idea of Ralf Hinze’s paper Generalizing Generalized Tries. The blog post Elegant memoization with functional memo tries describes a library, MemoTrie, based on both of these sources, and using associated data types. I would have rather used associated type synonyms and standard types, but I couldn’t see how to get the details to work out. Recently, while playing with functor combinators, I realized that they might work for memoization, which they do quite nicely.

This blog post shows how functor combinators lead to an even more elegant formulation of functional memoization. The code is available as part of the functor-combo package.

The techniques in this post are not so much new as they are ones that have recently been sinking in for me. See Generalizing Generalized Tries, as well as Generic programming with fixed points for mutually recursive datatypes.

Continue reading ‘Elegant memoization with higher-order types’ »

I have another paper draft for submission to ICFP 2009. This one is called Beautiful differentiation, The paper is a culmination of the several posts I’ve written on derivatives and automatic differentiation (AD). I’m happy with how the derivation keeps getting simpler. Now I’ve boiled extremely general higher-order AD down to a Functor and Applicative morphism.

I’d love to get some readings and feedback. I’m a bit over the page the limit, so I’ll have to do some trimming before submitting.

The abstract:

Automatic differentiation (AD) is a precise, efficient, and convenient method for computing derivatives of functions. Its implementation can be quite simple even when extended to compute all of the higher-order derivatives as well. The higher-dimensional case has also been tackled, though with extra complexity. This paper develops an implementation of higher-dimensional, higher-order differentiation in the extremely general and elegant setting of calculus on manifolds and derives that implementation from a simple and precise specification.

In order to motivate and discover the implementation, the paper poses the question “What does AD mean, independently of implementation?” An answer arises in the form of naturality of sampling a function and its derivative. Automatic differentiation flows out of this naturality condition, together with the chain rule. Graduating from first-order to higher-order AD corresponds to sampling all derivatives instead of just one. Next, the notion of a derivative is generalized via the notions of vector space and linear maps. The specification of AD adapts to this elegant and very general setting, which even simplifies the development.

You can get the paper and see current errata here.

The submission deadline is March 2, so comments before then are most helpful to me.

Enjoy, and thanks!

I’ve just finished a draft of a paper called Denotational design with type class morphisms, for submission to ICFP 2009. The paper is on a theme I’ve explored in several posts, which is semantics-based design, guided by type class morphisms.

I’d love to get some readings and feedback. Pointers to related work would be particularly appreciated, as well as what’s unclear and what could be cut. It’s an entire page over the limit, so I’ll have to do some trimming before submitting.

The abstract:

Type classes provide a mechanism for varied implementations of standard interfaces. Many of these interfaces are founded in mathematical tradition and so have regularity not only of types but also of properties (laws) that must hold. Types and properties give strong guidance to the library implementor, while leaving freedom as well. Some of the remaining freedom is in how the implementation works, and some is in what it accomplishes.

To give additional guidance to the what, without impinging on the how, this paper proposes a principle of type class morphisms (TCMs), which further refines the compositional style of denotational semantics. The TCM idea is simply that the instance’s meaning is the meaning’s instance. This principle determines the meaning of each type class instance, and hence defines correctness of implementation. In some cases, it also provides a systematic guide to implementation, and in some cases, valuable design feedback.

The paper is illustrated with several examples of type, meanings, and morphisms.

You can get the paper and see current errata here.

The submission deadline is March 2, so comments before then are most helpful to me.

Enjoy, and thanks!

The post Sequences, streams, and segments offered an answer to the the question of what’s missing in the following box:

I presented a simple type of function segments, whose representation contains a length (duration) and a function. This type implements most of the usual classes: Monoid, Functor, Zip, and Applicative, as well Comonad, but not Monad. It also implements a new type class, Segment, which generalizes the list functions length, take, and drop.

The function type is simple and useful in itself. I believe it can also serve as a semantic foundation for functional reactive programming (FRP), as I’ll explain in another post. However, the type has a serious performance problem that makes it impractical for some purposes, including as implementation of FRP.

Fortunately, we can solve the performance problem by adding a simple layer on top of function segments, to get what I’ll call “signals”. With this new layer, we have an efficient replacement for function segments that implements exactly the same interface with exactly the same semantics. Pleasantly, the class instances are defined fairly simply in terms of the corresponding instances on function segments.

You can download the code for this post.

Edits:

• 2008-12-06: dup [] = [] near the end (was [mempty]).
• 2008-12-09: Fixed take and drop default definitions (thanks to sclv) and added point-free variant.
• 2008-12-18: Fixed appl, thanks to sclv.

Continue reading ‘Sequences, segments, and signals’ »