- Generic Zipper and its applications
- LogicT
- backtracking monad transformer with fair operations and pruning

The LogicT code includes solving the Missionaries and Cannibals problem, and playing Tic-Tac-Toe with minimax search and heuristics - lambda to SKI: the first compositional translation
- Finally, the embedding of postfix languages one can really use
- Incremental, Linear Pretty-printing
- Secure Counting of members of a subset without revealing their identities
- Normal-order evaluation of lambda-terms as a bottom-up parsing
- Provably perfect random shuffling and its pure functional implementations
- Iterated zipping puzzles
- Annotating trees post factum
- Breadth-first labeling
- Eratosthenes and other number sieves
- How to
take a
`tail`

of a functional stream - Dynamic epistemic logic puzzles
- Total stream processors and their applications to all infinite streams
- Folding in Parallel
- Optimal justification of text with Dynamic Programming
- Deriving fast functions to compute all subsets of size N
- How to zip folds: A complete library of fold-represented lists
- Representing knowledge about knowledge: ``Mr.S and Mr.P'' puzzle
- Left fold vs. right fold: theory vs. practice
- Generating efficient median filters by an applicative-order term rewriting system
- Accumulating tree traversals, a better tree fold with applications to XML parsing
- Grasping `all-the-apples-at-once'
- Recursively enumerating binary arithmetic relations -- from addition to logarithm -- as conjunctions and disjunctions
- Intersection testing of two segments of a circle or of two georectangles, with a correctness proof
- Arithmetic compression
- Towards the best collection traversal interface: from enumerator to cursor
- On parent pointers in trees
- Printing the outline of a pruned tree
- Cross-product of sequences
- Surprisingly simple fast typed-aligned queues
- Proving complexity of one particular implementation of priority queues
- Treaps: An ordered dictionary data structure, based on randomized search trees
- Selecting a random node from a tree in one pass: from proof to code
- Pure functional, mutation-free, efficient double-linked lists
- Pure-functional transformations of cyclic graphs: viral flattening, and the Credit Card Transform
- Chess Tournament Scheduling
- Generating interesting lambda-terms

- We describe a concise Haskell solution to the ``Mr.S and Mr.P''
puzzle. We rely on the straightforward encoding of multiple-world
semantics of modalities.
The problem was posed by John McCarthy as follows. We pick two numbers

`a`

and`b`

, so that`a>=b`

and both numbers are within the range [2,99]. We give Mr.P the product`a*b`

and give Mr.S the sum`a+b`

. The following dialog takes place:- Mr.P: I don't know the numbers
- Mr.S: I knew you didn't know. I don't know either
- Mr.P: Now I know the numbers
- Mr.S: Now I know them too

`a`

and`b`

?The following Haskell code demonstrates a generic method of encoding facts, and the knowledge about facts, and the knowledge of the knowledge, etc. Incidentally, compared to the notation in McCarthy's paper, the Haskell notation is notably concise.

Chung-chieh Shan commented: ``The basic idea is to think of a set of possible worlds. Corresponding to each person (whose knowledge is being modeled) is a partition of this set of possible worlds; each partition contains one or more worlds that this person cannot distinguish. For someone to know a fact is for all of that person's indistinguishable possible worlds to verify that fact. For Alice to know that Bob doesn't know the weather, is for all of Alice's possible worlds (relative to the real world) to reside within a Bob-partition in which the weather is not consistent across all worlds.''

**Version**- The current version is 1.3, Jun 23, 2006
**References**- John McCarthy: Formalization of two Puzzles Involving Knowledge. 1987

<http://www-formal.stanford.edu/jmc/puzzles.html>Mr-S-P.lhs [4K]

Complete literate Haskell98 code

It was first mentioned in the message posted on Lambda-the-Ultimate on Jan 27, 2003. The present version adds a straightforward memoization.Hans P. van Ditmarsch, Ji Ruan and Rineke Verbrugge: Sum and Product in Dynamic Epistemic Logic

Journal of Logic and Computation, 2008, v18, N4, pp.563--588.

The paper discusses at great extent the history of the puzzle, its modeling in modal `public announcement logic', and solving using epistemic model checkers.

- Inspired by Hans van Ditmarsch's tutorial course on Dynamic Epistemic Logic
at NASSLLI 2010, we present a simplistic model-theoretic framework
to solve the puzzles like the following:
Anne, Bill and Cath each have a positive natural number written on their foreheads. They can only see the foreheads of others. One of the numbers is the sum of the other two. All the previous is common knowledge. The following truthful conversation takes place:
- Anne: I don't know my number.
- Bill: I don't know my number.
- Cath: I don't know my number.
- Anne: I now know my number, and it is 50.

The puzzle appeared as Problem 182 in the November 2004 issue of Math Horizons.We encode the statement of the problem as a filter on possible worlds. The possible worlds consistent with the statement of the problem are the solutions. `Agent

`A`

does not know proposition`phi`

' is interpreted as the statement that for all worlds consistent with the propositions that`A`

currently knows,`phi`

is true in some worlds but false in the others. **Version**- The current version is 1.1, June 2010
**References**- DynEpistemology.hs [10K]

The complete commented Haskell98 codeH.P. van Ditmarsch, W. van der Hoek, and B.P. Kooi: Dynamic Epistemic Logic and Knowledge Puzzles

Proc. 15th International Conference on Conceptual Structures (ICCS). LNCS 4604, pp. 45-58, Springer, 2007.Representing knowledge about knowledge: ``Mr.S and Mr.P'' puzzle

- We present a procedure that picks a uniformly distributed
random node from a tree. We traverse the tree only once and we do not
know beforehand the number of nodes in the tree. The provably correct
algorithm is an instance of a Reservoir sampling.
The procedure is written in the pure functional subset of R5RS Scheme and comes with the correctness proof. We must stress that the proof was developed not after the implementation but along with the implementation. In our experience, thinking about the proof and writing it down notably helped design and code the algorithm. Once the proof was written, the code followed immediately. The code worked on the first try.

**Version**- The current version is April 15, 2003
**References**- random-tree-node.scm [6K]

The complete source code, the proof of the algorithm, and the validation tests

The code was originally posted in the article Re: random node in tree on the newsgroup comp.lang.scheme on Tue, 15 Apr 2003 22:17:15 -0700

- This code demonstrates Dynamic Programming on the problem
of pretty printing a paragraph of text on a printer with fixed-width
fonts. The goal is to tightly arrange a given sequence of
`n`

words within page margins, maximizing the overall neatness. To be more precise, we wish to minimize the sum, over all lines except the last, of the cubes of the number of blank characters at the end of each line. See the comments in the code for more details.The algorithm has

`O(n^2)`

time and space complexities. **Version**- The current version is 2.0, April 2001
**References**- word_layout.cc [9K]

Commented C++ source code and sample output, with many annotations.Z. Galil, K. Park: A linear-time algorithm for concave one-dimensional dynamic programming

Information Processing Letters, v33, N6, 309-311, 1990.

David Eppstein, livejournal.com user 11011110, pointed out that the present problem is an instance of concave 1d dynamic programming, which admits a linear-time solution.

- The following two-part article attempts to design the fastest
solution to the problem of finding all subsets of a given size from a
given set. The precise problem is: given a set
`L`

and a number`N`

, return the set of all subsets of`L`

of cardinality`N`

. Sets are represented by lists. We will be using R5RS Scheme.In part 1, we start with the mathematical definition of the problem, which leads to a simple, correct, but inefficient solution. We then try to systematically optimize the function until we end up with the fastest function, which is notably faster than the other solutions proposed so far. The final solution is still pure functional. We also demonstrate that the choice of the Scheme interpreter does matter in relative performance of various algorithms.

In part 2, we again start with the mathematical definition of the problem, which leads to a simple, correct, and stunningly efficient solution. The final, so far the fastest solution is still pure functional. The key was to choose the right definition.

In the discussion, Doug Quale presented lazy stream implementations in Haskell and Scheme, and compared them with the above. Eli Barzilay described various memozied versions, which have even better performance. The two USENET threads contain the excellent discussion of the relative merits of memoization and laziness, contributed by Doug Quale and Eli Barzilay. The threads also include many timing comparisons.

**References**- subsets-size-n-part1.txt [13K]

Part 1 of the article

It was originally posted as Re: Subsets of a list on the newsgroup comp.lang.scheme on Sat, 12 Jan 2002 00:52:23 -0800subsets-size-n-part2.txt [9K]

Part 2 of the article

It was originally posted as The FASTEST subsets function [Was: Subsets of a list] on the newsgroup comp.lang.scheme on Sat, 12 Jan 2002 00:56:01 -0800

The article is updated with a more optimized solution, which should perform better when compiled.Discussion threads of the above titles, comp.lang.scheme, Jan 9-18, 2002.

- Computing prime numbers is an important practical task as
well as a common example for programming language tutorials. The
Eratosthenes sieve is probably the most familiar algorithm for
determining prime numbers. Alas, quite many implementations that
call themselves Eratosthenes sieve do not actually implement that algorithm.
For example, the classic Haskell code
primes = sieve [ 2.. ] where sieve (p:x) = p : sieve [ n | n <- x, n `mod` p > 0 ]

is not the Eratosthenes sieve. For one thing, it uses the division operation (or,`mod`

). The Eratosthenes sieve specifically avoids both division and multiplication, which were quite difficult in old times. Mainly, as Melissa O'Neill explains below, the code above tests*every*number for divisibility by all previously found primes. In contrast, the true Eratosthenes sieve affects only composite numbers. The prime numbers are `left out'; they are not checked by division or numeric comparison.Given below are several implementations of the true Eratosthenes sieve algorithm, in Scheme, Scheme macros, and Haskell. The algorithm is usually formulated in terms of marks and crossing off marks, suggesting the imperative implementation with mutable arrays. The Scheme code follows that suggestion, using two important optimizations kindly described by George Kangas.

Eratosthenes sieve, however, can be implemented purely functionally, as Scheme macros and Haskell code demonstrate. The Haskell implementation is not meant to be efficient -- rather, it is meant to be purely functional, insightful, minimalist, and generalizable to other number sieves, e.g., `lucky numbers'. Like other Haskell algorithms it produces a stream of prime numbers. The Haskell implementation stores only marks

*signifying*the numbers, but never the numbers themselves. Not only the implementation avoids multiplication, division or the remainder operations. We also avoid general addition and number comparison. We rely*exclusively*on the successor, predecessor and zero comparison. The predecessor can be easily eliminated. Thus the algorithm can be used with Church and Peano numerals, or members of Elliptic rings, where zero comparison and successor take constant time but other arithmetic operations are more involved. **Version**- The current version is 1.5, Feb 12, 2007
**References**- Melissa O'Neill: Re: Genuine Eratosthenes sieve

Messages explaining the sieve algorithm and its differences from impostors; posted on the Haskell-Cafe mailing list, February 2007.Eratosthenes-sieve.txt [6K]

Eratosthenes sieve and its optimal implementation

The explanation of the original Eratosthenes sieve and its optimizations.

The original article was posted as Re: arbitrary precision rationals on a newsgroup comp.lang.scheme on Tue, 13 Nov 2001 15:07:34 -0800number-sieve.lhs [4K]

The literate Haskell98 source code for pure functional, minimalist Eratosthenes and lucky number sieves

The code was originally posted in the article Even better Eratosthenes sieve and lucky numbers on the Haskell-Cafe mailing list on Mon, 12 Feb 2007 18:37:46 -0800 (PST)A stress test of the syntax-rule macro-expander

Eratosthenes sieve as a syntax-rule macro, to perform primality test of Church-Peano numerals at macro-expand timeLucky numbers: another number sieve

<http://mathworld.wolfram.com/LuckyNumber.html>

<http://www.research.att.com/~njas/sequences/A000959>

- This article shows a solution (in Scheme) to a problem of
computing a cross-product of
`n`

sequences. For example(cross-product '((0) (0 1 2 3) (1 2)))

evaluates to((0 0 1) (0 0 2) (0 1 1) (0 1 2) (0 2 1) (0 2 2) (0 3 1) (0 3 2))

The solution is especially elegant if we use the standard (SRFI-1)`append-map`

function. **Version**- The current version is 1.1, January 11, 2001
**References**- cross-product.txt [2K]

Re: combinatorial stuff ?? The article with the complete code and transcripts, posted on the newsgroup comp.lang.scheme on Thu, 11 Jan 2001 19:59:45 GMT