Efficient generation of combinatorial sequences
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Combinatorics.pm
Combinatorics.xs
MANIFEST
META.yml
Makefile.PL
README

README

NAME
    Algorithm::Combinatorics - Efficient generation of combinatorial
    sequences

SYNOPSIS
     use Algorithm::Combinatorics qw(permutations);

     my @data = qw(a b c);

     # scalar context gives an iterator
     my $iter = permutations(\@data);
     while (my $p = $iter->next) {
         # ...
     }

     # list context slurps
     my @all_permutations = permutations(\@data);

VERSION
    This documentation refers to Algorithm::Combinatorics version 0.26.

DESCRIPTION
    Algorithm::Combinatorics is an efficient generator of combinatorial
    sequences. Algorithms are selected from the literature (work in
    progress, see REFERENCES). Iterators do not use recursion, nor stacks,
    and are written in C.

    Tuples are generated in lexicographic order, except in `subsets()'.

SUBROUTINES
    Algorithm::Combinatorics provides these subroutines:

        permutations(\@data)
        circular_permutations(\@data)
        derangements(\@data)
        complete_permutations(\@data)
        variations(\@data, $k)
        variations_with_repetition(\@data, $k)
        tuples(\@data, $k)
        tuples_with_repetition(\@data, $k)
        combinations(\@data, $k)
        combinations_with_repetition(\@data, $k)
        partitions(\@data[, $k])
        subsets(\@data[, $k])

    All of them are context-sensitive:

    *   In scalar context subroutines return an iterator that responds to
        the `next()' method. Using this object you can iterate over the
        sequence of tuples one by one this way:

            my $iter = combinations(\@data, $k);
            while (my $c = $iter->next) {
                # ...
            }

        The `next()' method returns an arrayref to the next tuple, if any,
        or `undef' if the sequence is exhausted.

        Memory usage is minimal, no recursion and no stacks are involved.

    *   In list context subroutines slurp the entire set of tuples. This
        behaviour is offered for convenience, but take into account that the
        resulting array may be really huge:

            my @all_combinations = combinations(\@data, $k);

  permutations(\@data)

    The permutations of `@data' are all its reorderings. For example, the
    permutations of `@data = (1, 2, 3)' are:

        (1, 2, 3)
        (1, 3, 2)
        (2, 1, 3)
        (2, 3, 1)
        (3, 1, 2)
        (3, 2, 1)

    The number of permutations of `n' elements is:

        n! = 1,                  if n = 0
        n! = n*(n-1)*...*1,      if n > 0

    See some values at http://www.research.att.com/~njas/sequences/A000142.

  circular_permutations(\@data)

    The circular permutations of `@data' are its arrangements around a
    circle, where only relative order of elements matter, rather than their
    actual position. Think possible arrangements of people around a circular
    table for dinner according to whom they have to their right and left, no
    matter the actual chair they sit on.

    For example the circular permutations of `@data = (1, 2, 3, 4)' are:

        (1, 2, 3, 4)
        (1, 2, 4, 3)
        (1, 3, 2, 4)
        (1, 3, 4, 2)
        (1, 4, 2, 3)
        (1, 4, 3, 2)

    The number of circular permutations of `n' elements is:

            n! = 1,                      if 0 <= n <= 1
        (n-1)! = (n-1)*(n-2)*...*1,      if n > 1

    See a few numbers in a comment of
    http://www.research.att.com/~njas/sequences/A000142.

  derangements(\@data)

    The derangements of `@data' are those reorderings that have no element
    in its original place. In jargon those are the permutations of `@data'
    with no fixed points. For example, the derangements of `@data = (1, 2,
    3)' are:

        (2, 3, 1)
        (3, 1, 2)

    The number of derangements of `n' elements is:

        d(n) = 1,                       if n = 0
        d(n) = n*d(n-1) + (-1)**n,      if n > 0

    See some values at http://www.research.att.com/~njas/sequences/A000166.

  complete_permutations(\@data)

    This is an alias for `derangements', documented above.

  variations(\@data, $k)

    The variations of length `$k' of `@data' are all the tuples of length
    `$k' consisting of elements of `@data'. For example, for `@data = (1, 2,
    3)' and `$k = 2':

        (1, 2)
        (1, 3)
        (2, 1)
        (2, 3)
        (3, 1)
        (3, 2)

    For this to make sense, `$k' has to be less than or equal to the length
    of `@data'.

    Note that

        permutations(\@data);

    is equivalent to

        variations(\@data, scalar @data);

    The number of variations of `n' elements taken in groups of `k' is:

        v(n, k) = 1,                        if k = 0
        v(n, k) = n*(n-1)*...*(n-k+1),      if 0 < k <= n

  variations_with_repetition(\@data, $k)

    The variations with repetition of length `$k' of `@data' are all the
    tuples of length `$k' consisting of elements of `@data', including
    repetitions. For example, for `@data = (1, 2, 3)' and `$k = 2':

        (1, 1)
        (1, 2)
        (1, 3)
        (2, 1)
        (2, 2)
        (2, 3)
        (3, 1)
        (3, 2)
        (3, 3)

    Note that `$k' can be greater than the length of `@data'. For example,
    for `@data = (1, 2)' and `$k = 3':

        (1, 1, 1)
        (1, 1, 2)
        (1, 2, 1)
        (1, 2, 2)
        (2, 1, 1)
        (2, 1, 2)
        (2, 2, 1)
        (2, 2, 2)

    The number of variations with repetition of `n' elements taken in groups
    of `k >= 0' is:

        vr(n, k) = n**k

  tuples(\@data, $k)

    This is an alias for `variations', documented above.

  tuples_with_repetition(\@data, $k)

    This is an alias for `variations_with_repetition', documented above.

  combinations(\@data, $k)

    The combinations of length `$k' of `@data' are all the sets of size `$k'
    consisting of elements of `@data'. For example, for `@data = (1, 2, 3,
    4)' and `$k = 3':

        (1, 2, 3)
        (1, 2, 4)
        (1, 3, 4)
        (2, 3, 4)

    For this to make sense, `$k' has to be less than or equal to the length
    of `@data'.

    The number of combinations of `n' elements taken in groups of `0 <= k <=
    n' is:

        n choose k = n!/(k!*(n-k)!)

  combinations_with_repetition(\@data, $k);

    The combinations of length `$k' of an array `@data' are all the bags of
    size `$k' consisting of elements of `@data', with repetitions. For
    example, for `@data = (1, 2, 3)' and `$k = 2':

        (1, 1)
        (1, 2)
        (1, 3)
        (2, 2)
        (2, 3)
        (3, 3)

    Note that `$k' can be greater than the length of `@data'. For example,
    for `@data = (1, 2, 3)' and `$k = 4':

        (1, 1, 1, 1)
        (1, 1, 1, 2)
        (1, 1, 1, 3)
        (1, 1, 2, 2)
        (1, 1, 2, 3)
        (1, 1, 3, 3)
        (1, 2, 2, 2)
        (1, 2, 2, 3)
        (1, 2, 3, 3)
        (1, 3, 3, 3)
        (2, 2, 2, 2)
        (2, 2, 2, 3)
        (2, 2, 3, 3)
        (2, 3, 3, 3)
        (3, 3, 3, 3)

    The number of combinations with repetition of `n' elements taken in
    groups of `k >= 0' is:

        n+k-1 over k = (n+k-1)!/(k!*(n-1)!)

  partitions(\@data[, $k])

    A partition of `@data' is a division of `@data' in separate pieces.
    Technically that's a set of subsets of `@data' which are non-empty,
    disjoint, and whose union is `@data'. For example, the partitions of
    `@data = (1, 2, 3)' are:

        ((1, 2, 3))
        ((1, 2), (3))
        ((1, 3), (2))
        ((1), (2, 3))
        ((1), (2), (3))

    This subroutine returns in consequence tuples of tuples. The top-level
    tuple (an arrayref) represents the partition itself, whose elements are
    tuples (arrayrefs) in turn, each one representing a subset of `@data'.

    The number of partitions of a set of `n' elements are known as Bell
    numbers, and satisfy the recursion:

        B(0) = 1
        B(n+1) = (n over 0)B(0) + (n over 1)B(1) + ... + (n over n)B(n)

    See some values at http://www.research.att.com/~njas/sequences/A000110.

    If you pass the optional parameter `$k', the subroutine generates only
    partitions of size `$k'. This uses an specific algorithm for partitions
    of known size, which is more efficient than generating all partitions
    and filtering them by size.

    Note that in that case the subsets themselves may have several sizes, it
    is the number of elements *of the partition* which is `$k'. For instance
    if `@data' has 5 elements there are partitions of size 2 that consist of
    a subset of size 2 and its complement of size 3; and partitions of size
    2 that consist of a subset of size 1 and its complement of size 4. In
    both cases the partitions have the same size, they have two elements.

    The number of partitions of size `k' of a set of `n' elements are known
    as Stirling numbers of the second kind, and satisfy the recursion:

        S(0, 0) = 1
        S(n, 0) = 0 if n > 0
        S(n, 1) = S(n, n) = 1
        S(n, k) = S(n-1, k-1) + kS(n-1, k)

  subsets(\@data[, $k])

    This subroutine iterates over the subsets of data, which is assumed to
    represent a set. If you pass the optional parameter `$k' the iteration
    runs over subsets of data of size `$k'.

    The number of subsets of a set of `n' elements is

      2**n

    See some values at http://www.research.att.com/~njas/sequences/A000079.

CORNER CASES
    Since version 0.05 subroutines are more forgiving for unsual values of
    `$k':

    *   If `$k' is less than zero no tuple exists. Thus, the very first call
        to the iterator's `next()' method returns `undef', and a call in
        list context returns the empty list. (See DIAGNOSTICS.)

    *   If `$k' is zero we have one tuple, the empty tuple. This is a
        different case than the former: when `$k' is negative there are no
        tuples at all, when `$k' is zero there is one tuple. The rationale
        for this behaviour is the same rationale for n choose 0 = 1: the
        empty tuple is a subset of `@data' with `$k = 0' elements, so it
        complies with the definition.

    *   If `$k' is greater than the size of `@data', and we are calling a
        subroutine that does not generate tuples with repetitions, no tuple
        exists. Thus, the very first call to the iterator's `next()' method
        returns `undef', and a call in list context returns the empty list.
        (See DIAGNOSTICS.)

    In addition, since 0.05 empty `@data's are supported as well.

EXPORT
    Algorithm::Combinatorics exports nothing by default. Each of the
    subroutines can be exported on demand, as in

        use Algorithm::Combinatorics qw(combinations);

    and the tag `all' exports them all:

        use Algorithm::Combinatorics qw(:all);

DIAGNOSTICS
  Warnings

    The following warnings may be issued:

    Useless use of %s in void context
        A subroutine was called in void context.

    Parameter k is negative
        A subroutine was called with a negative k.

    Parameter k is greater than the size of data
        A subroutine that does not generate tuples with repetitions was
        called with a k greater than the size of data.

  Errors

    The following errors may be thrown:

    Missing parameter data
        A subroutine was called with no parameters.

    Missing parameter k
        A subroutine that requires a second parameter k was called without
        one.

    Parameter data is not an arrayref
        The first parameter is not an arrayref (tested with "reftype()" from
        Scalar::Util.)

DEPENDENCIES
    Algorithm::Combinatorics is known to run under perl 5.6.2. The
    distribution uses Test::More and FindBin for testing, Scalar::Util for
    `reftype()', and XSLoader for XS.

BUGS
    Please report any bugs or feature requests to
    `bug-algorithm-combinatorics@rt.cpan.org', or through the web interface
    at
    http://rt.cpan.org/NoAuth/ReportBug.html?Queue=Algorithm-Combinatorics.

SEE ALSO
    Math::Combinatorics is a pure Perl module that offers similar features.

    List::PowerSet offers a fast pure-Perl generator of power sets that
    Algorithm::Combinatorics copies and translates to XS.

BENCHMARKS
    There are some benchmarks in the benchmarks directory of the
    distribution.

REFERENCES
    [1] Donald E. Knuth, *The Art of Computer Programming, Volume 4,
    Fascicle 2: Generating All Tuples and Permutations*. Addison Wesley
    Professional, 2005. ISBN 0201853930.

    [2] Donald E. Knuth, *The Art of Computer Programming, Volume 4,
    Fascicle 3: Generating All Combinations and Partitions*. Addison Wesley
    Professional, 2005. ISBN 0201853949.

    [3] Michael Orlov, *Efficient Generation of Set Partitions*,
    http://www.informatik.uni-ulm.de/ni/Lehre/WS03/DMM/Software/partitions.p
    df.

AUTHOR
    Xavier Noria (FXN), <fxn@cpan.org>

COPYRIGHT & LICENSE
    Copyright 2005-2012 Xavier Noria, all rights reserved.

    This program is free software; you can redistribute it and/or modify it
    under the same terms as Perl itself.