This is documentation for a `Permutation`

data type for Julia. We only
consider permutations of sets of the form `{1,2,3,...,n}`

where `n`

is
a positive integer.

A `Permutation`

object is created from a one-dimensional arry of
integers containing each of the values `1`

through `n`

exactly once.

```
julia> a = [4,1,3,2,6,5];
julia> p = Permutation(a)
(1,4,2)(3)(5,6)
```

Observe the `Permutation`

is printed in disjoint cycle format.

The number of elements in a `Permutation`

is determined using the
`length`

function:

```
julia> length(p)
6
```

A `Permutation`

can be converted to an array (equal to the array used
to construct the `Permutation`

in the first place) or can be presented
as a two-row matrix as follows:

```
julia> p.data
6-element Array{Int64,1}:
4
1
3
2
6
5
julia> two_row(p)
2x6 Array{Int64,2}:
1 2 3 4 5 6
4 1 3 2 6 5
```

The evaluation of a `Permutation`

on a particular element is performed
using square bracket or parenthesis notation:

```
julia> p[2]
1
julia> p(2)
1
```

Of course, bad things happen if an inappropriate element is given:

```
julia> p[7]
ERROR: BoundsError()
in getindex at ....
```

Composition is denoted by `*`

:

```
julia> q = Permutation([1,6,2,3,4,5])
(1)(2,6,5,4,3)
julia> p*q
(1,4,3)(2,5)(6)
julia> q*p
(1,3,2)(4,6)(5)
```

Repeated composition is calculated using `^`

, like this: `p^n`

.
The exponent can be negative.

The inverse of a `Permutation`

is computed using `inv`

or as `p'`

:

```
julia> q = inv(p)
(1,2,4)(3)(5,6)
julia> p*q
(1)(2)(3)(4)(5)(6)
```

To get the cycle structure of a `Permutation`

(not as a character string,
but as an array of arrays), use `cycles`

:

```
julia> cycles(p)
3-element Array{Array{Int64,1},1}:
[1,4,2]
[3]
[5,6]
```

The `sqrt`

function returns a compositional square root of the permutation.
That is, if `q=sqrt(p)`

then `q*q==p`

. Note that not all permutations have
square roots and square roots are not unique.

```
julia> p
(1,12,7,4)(2,8,3)(5,15,11,14)(6,10,13)(9)
julia> q = sqrt(p)
(1,5,12,15,7,11,4,14)(2,3,8)(6,13,10)(9)
julia> q*q == p
true
```

The function `Matrix`

converts a permutation `p`

to a square matrix
whose `i,j`

-entry is `1`

when `i == p[j]`

and `0`

otherwise.

```
julia> p = RandomPermutation(6)
(1,5,2,6)(3)(4)
julia> Matrix(p)
6×6 Array{Int64,2}:
0 0 0 0 0 1
0 0 0 0 1 0
0 0 1 0 0 0
0 0 0 1 0 0
1 0 0 0 0 0
0 1 0 0 0 0
```

**Note**: `sparse`

method has been removed during transition from
Julia 0.6 to 0.7.

The sign of a `Permutation`

is `+1`

for an even permutation and `-1`

for an odd permutation.

```
julia> p = Permutation([2,3,4,1])
(1,2,3,4)
julia> sign(p)
-1
julia> sign(p*p)
1
```

If one thinks of a permutation as a sequence, then applying `reverse`

to that permutation returns a new permutation based on the reversal of
that sequence. Here's an example:

```
julia> p = RandomPermutation(8)
(1,5,8,4,6)(2,3)(7)
julia> two_row(p)
2x8 Array{Int64,2}:
1 2 3 4 5 6 7 8
5 3 2 6 8 1 7 4
julia> two_row(reverse(p))
2x8 Array{Int64,2}:
1 2 3 4 5 6 7 8
4 7 1 8 6 2 3 5
```

For convenience, identity and random permutations can be constructed like this:

```
julia> Permutation(10)
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)
julia> RandomPermutation(10)
(1,7,6,10,3,2,8,4)(5,9)
```

In addition, we can use `Permutation(n,k)`

to create the
`k`

'th permutation of the set `{1,2,...,n}`

. Of course,
this requires `k`

to be between `1`

and `n!`

.

```
julia> Permutation(6,701)
(1,6,3)(2,5)(4)
```

A *fixed point* of a permutation `p`

is a value `k`

such that
`p[k]==k`

. The function `fixed_points`

returns a list of the fixed
points of a given permutation.

```
julia> p = RandomPermutation(20)
(1,15,10,9,11,13,12,8,5,7,18,6,2)(3)(4,16,17,19)(14)(20)
julia> fixed_points(p)
3-element Array{Int64,1}:
3
14
20
```

The function `longest_increasing`

finds a subsequence of a permutation
whose elements are in increasing order. Likewise, `longest_decreasing`

finds a longest decreasing subsequence.
For example:

```
julia> p = RandomPermutation(10)
(1,3,10)(2)(4)(5,6)(7)(8)(9)
julia> two_row(p)
2x10 Array{Int64,2}:
1 2 3 4 5 6 7 8 9 10
3 2 10 4 6 5 7 8 9 1
julia> longest_increasing(p)
6-element Array{Int64,1}:
3
4
6
7
8
9
julia> longest_decreasing(p)
4-element Array{Int64,1}:
10
6
5
1
```

`Dict`

For a permutation `p`

, calling `dict(p)`

returns a dictionary that behaves
just like `p`

.

```
julia> p = RandomPermutation(12)
(1,11,6)(2,8,7)(3)(4,5,9,12,10)
julia> d = dict(p)
Dict{Int64,Int64} with 12 entries:
2 => 8
11 => 6
7 => 2
9 => 12
10 => 4
8 => 7
6 => 1
4 => 5
3 => 3
5 => 9
12 => 10
1 => 11
```

Every permutation can be expressed as a product of transpositions. In
a *Coxeter decomposition* the permutation is the product of transpositions
of the form `(j,j+1)`

.
Given a permutation `p`

, we get this form
with `CoxeterDecomposition(p)`

:

```
julia> p = Permutation([2,4,3,5,1,6,8,7])
(1,2,4,5)(3)(6)(7,8)
julia> pp = CoxeterDecomposition(p)
Permutation of 1:8: (1,2)(2,3)(3,4)(2,3)(4,5)(7,8)
```

The original permutation can be recovered like this:

```
julia> Permutation(pp)
(1,2,4,5)(3)(6)(7,8)
```

07/25/2014

4 months ago

82 commits