FinitePosets.FinitePosetsModule

This package deals with finite posets.

There are two types of posets. A "canonical poset" or CPoset is on the elements 1:n where n=length(P). A Poset is on a given list of elements which can be of any type. A Poset internally contains aCPoset which works on the indices of the elements, which is more efficient than working with the elements themselves. For efficiency, many functions work on the internal CPoset by transforming their input to indices and their output to elements.

A CPoset p contains one of the following data:

• hasse(p): a list representing the Hasse diagram of the poset: the i-th entry is the list of elements which cover (are immediate successors of) i, that is the list of j such that i<j and there is no k such that i<k<j.

• incidence(p): a boolean matrix such that incidence[i,j]==true iff i<=j. This is sometimes called the ζ-matrix of the poset.

Some computations work better on the incidence matrix, and some others on the Hasse diagram. If missing for a computation, one of the above data is computed from the other. This may take some substantial time for large posets.

There are several ways of defining a poset. By entering the Hasse diagram:

julia> p=CPoset([[2,3],[4],[4],Int[]])
1<2,3<4

As seen above, p is shown as a list of covering maximal chains; elements which are equivalent for the poset are printed together separated by commas.

julia> length(p) # the number of elements of p
4

julia> incidence(p)
4×4 Matrix{Bool}:
1  1  1  1
0  1  0  1
0  0  1  1
0  0  0  1

julia> linear_extension(p) # a total order compatible with p
4-element Vector{Int64}:
1
2
3
4

A Poset is constructed from a CPoset and a list of elements

julia> P=Poset(p,[:a,:b,:c,:d])
a<b,c<d

julia> P.C # the CPoset attached to P
1<2,3<4

A convenient constructor for Posets takes a function representing isless for the poset and the list of elements and constructs the poset from the incidence matrix, computed by applying the function to each pair of elements. For isless one can give either a function implementing < or a function implementing ≤ (it is or-ed with == in any case).

julia> l=vec(collect(Iterators.product(1:2,1:2)))
4-element Vector{Tuple{Int64, Int64}}:
(1, 1)
(2, 1)
(1, 2)
(2, 2)

julia> P=Poset((x,y)->all(map(<=,x,y)),l)
(1, 1)<(2, 1),(1, 2)<(2, 2)

julia> eltype(P) # the type of the elements of P
Tuple{Int64, Int64}

julia> summary(P) # useful for big posets
"Poset{Tuple{Int64, Int64}} of length 4"

A poset can also be constructed from an incidence matrix so the last example could also be entered as

julia> P=Poset(CPoset([all(map(<=,x,y)) for x in l, y in l]),l)
(1, 1)<(2, 1),(1, 2)<(2, 2)

Flexibility on printing a Poset is obtained by setting the function show_element which takes as arguments an IO, the poset, and the index of the element to print:

julia> P.show_element=(io,p,n)->join(io,p.elements[n],".");

julia> P
1.1<2.1,1.2<2.2

julia> delete!(P,:show_element); # back to default

The above fancy printing applies only when printing at the REPL or in pluto or Jupyter. The default printing gives a form which can be input back in Julia

julia> print(P)
Poset(CPoset([[2, 3], [4], [4], Int64[]]),[(1, 1), (2, 1), (1, 2), (2, 2)])

A poset can be specified by a list of tuples specifying order relations. The transitive closure of these relations is computed, resulting in an incidence matrix from which the poset is constructed. The elements of the poset, if not specified separately, are all the elements that appear in the tuples.

julia> Poset([(:a,:b),(:c,:d)])
a<b
c<d

julia> CPoset([(1,3),(2,5)]) # the CPoset is on 1:maximum(entries)
4
1<3
2<5

To get the order relation ≤ of the poset p between elements i and j just call ≤(p,i,j).

julia> ≤(P,(1,1),(2,1))
true

julia> ≤(P.C,1,2) # the same
true

Intervals in a poset can be computed with strict or not bounds.

julia> interval(P,≤,(1,2)) # elements below (1,2)
2-element Vector{Tuple{Int64, Int64}}:
(1, 1)
(1, 2)

julia> interval(P,≥,(1,2)) # elements above (1,2)
2-element Vector{Tuple{Int64, Int64}}:
(1, 2)
(2, 2)

julia> interval(P,<,(1,2)) # elements strictly below (1,2)
1-element Vector{Tuple{Int64, Int64}}:
(1, 1)

julia> interval(P,≥,(2,1),≤,(2,2)) # elements between (2,1) and (2,2)
2-element Vector{Tuple{Int64, Int64}}:
(2, 1)
(2, 2)

julia> interval(P,>,(1,1),<,(2,2)) # elements strictly between
2-element Vector{Tuple{Int64, Int64}}:
(2, 1)
(1, 2)
julia> interval(P.C,>,1,<,4) # in terms of indices
2-element Vector{Int64}:
2
3

A sample of other functions available on posets:

julia> maximal_chains(P)
2-element Vector{Vector{Tuple{Int64, Int64}}}:
[(1, 1), (2, 1), (2, 2)]
[(1, 1), (1, 2), (2, 2)]

julia> height(P) # the length of a maximal chain
3

julia> moebiusmatrix(P)
4×4 Matrix{Int64}:
1  -1  -1   1
0   1   0  -1
0   0   1  -1
0   0   0   1

julia> minima(P)
1-element Vector{Tuple{Int64, Int64}}:
(1, 1)

julia> maxima(P)
1-element Vector{Tuple{Int64, Int64}}:
(2, 2)

julia> Q=CPoset(:chain,3)
1<2<3

julia> P1=Poset(Q) # transformed to a Poset with elements 1:3
1<2<3

julia> P⊕ P1 # the ordinal sum
(1, 1)<(2, 1),(1, 2)<(2, 2)<1<2<3

julia> P1*P1
(1, 1)<(2, 1)<(3, 1)<(3, 2)<(3, 3)
(1, 1)<(1, 2)<(2, 2)<(3, 2)
(2, 1)<(2, 2)
(1, 2)<(1, 3)<(2, 3)<(3, 3)
(2, 2)<(2, 3)

julia> P1⊗ P1 # the ordinal product
(1, 1)<(1, 2)<(1, 3)<(2, 1)<(2, 2)<(2, 3)<(3, 1)<(3, 2)<(3, 3)

Finally showpic(p) where p is a CPoset or a Poset gives a graphical display of the poset provided you have the command dot of graphviz installed. It then uses the xdg "open" command to open the resulting .png file. This works on Linux and MacOs but I could not try it on Windows.

see the on-line help on ⊕, ⊗, +, *, chains, chainpoly, covering_chains, coxetermatrix, dual, hasse, height, incidence, induced, interval, isjoinlattice, ismeetlattice, linear_extension, maxima, maximal_chains, minima, moebius, moebiusmatrix, partition, showpic, transitive_closure for more information

FinitePosets.CPosetMethod

CPoset(f::Function,n::integer)

creates the Poset on 1:n with order given by function f.

julia> CPoset((x,y)->y%x==0,8)  # the divisibility poset
1<5,7
1<2<4<8
1<3<6
2<6
FinitePosets.CPosetMethod

CPoset(m::Matrix{Bool})

Creates a poset from an incidence matrix m, that is m[i,j]==true if and only if i≤j in the poset,

julia> CPoset(Bool[1 1 1 1 1;0 1 0 1 1;0 0 1 1 1;0 0 0 1 0;0 0 0 0 1])
1<2,3<4,5
FinitePosets.CPosetMethod
• CPoset(:chain,n) a chain on 1:n
• CPoset(:antichain,n) an antichain on 1:n
• CPoset(:diamond,n) a diamond poset on 1:n
FinitePosets.CPosetMethod

CPoset(h::Vector{<:Vector{<:Integer}})

Creates a poset from a Hasse diagram given as a Vector whose i-th entry is the list of indices which are immediate successors (covers) of the i-th element, that is h[i] is the list of j such that i<j in the poset and such that there is no k such that i<k<j.

julia> CPoset([[2,3],[4,5],[4,5],Int[],Int[]])
1<2,3<4,5
FinitePosets.CPosetMethod

CPoset(covers::Vector{Tuple{Int,Int}})

creates a poset representing the transitive closure of the given relations. The poset is on 1:n where n is the maximum number which appears in the relations.

julia> CPoset([(6,2),(5,1)])
3,4
5<1
6<2
FinitePosets.PosetMethod

Poset(f::Function,e::AbstractVector)

creates a Poset with elements e and order between two elements given by function f.

julia> Poset((x,y)->all(x.≤y),vec(collect(Iterators.product(1:2,1:3))))
(1, 1)<(2, 1)<(2, 2)<(2, 3)
(1, 1)<(1, 2)<(2, 2)
(1, 2)<(1, 3)<(2, 3)
FinitePosets.PosetMethod
• Poset(:chain,e) a chain with elements e
• Poset(:antichain,e) an antichain with elements e
• Poset(:powerset,n::Integer) the powerset of the set 1:n with inclusion
julia> p=Poset(:powerset,3);p.show_element=(io,p,n)->join(io,p.elements[n]);

julia> p
<1<12<123
<2<12
<3<13<123
1<13
2<23<123
3<23
• Poset(:powerset,e) the powerset of the set e with inclusion
• Poset(:partitionsdominance,n) the poset of partitions of n with dominance order
julia> Poset(:partitionsdominance,5)
[1, 1, 1, 1, 1]<[2, 1, 1, 1]<[2, 2, 1]<[3, 1, 1]<[3, 2]<[4, 1]<[5]
FinitePosets.PosetMethod

Poset(covers::Vector{Tuple{T,T}}) where T

creates a poset representing the transitive closure of the given relations. The poset is on the elements which appear in the relations.

julia> Poset([(:a,:b),(:d,:c)])
a<b
d<c
FinitePosets.PosetMethod

Poset(p::CPoset,e::AbstractVector=1:length(p))

creates a Poset with order specified by p and elements e.

julia> Poset(CPoset([[2,3],[4],[4],Int[]]),[:a,:b,:c,:d])
a<b,c<d

with no second argument transforms a CPoset into a Poset.

Base.:*Method

P*Q returns the product of two CPosets or of two Posets.

julia> CPoset(:chain,2)*CPoset(:chain,3)
1<2<3<6
1<4<5<6
2<5

julia> Poset(:chain,[1,2])*Poset(:chain,[:a,:b,:c])
(1, :a)<(2, :a)<(1, :b)<(2, :c)
(1, :a)<(2, :b)<(1, :c)<(2, :c)
(2, :a)<(1, :c)
Base.:+Method

P+Q returns the sum of two CPosets or of two Posets.

julia> CPoset(:chain,2)+CPoset(:chain,3)
1<2
3<4<5

julia> Poset(:chain,[1,2])+Poset(:chain,[:a,:b,:c])
1<2
a<b<c
Combinat.moebiusFunction

moebius(P::CPoset,y=first(maxima(P)))

the vector of values μ(x,y) of the Moebius function of P for x varying. Here is an example giving the ususal Moebius function on integers.

julia> p=CPoset((i,j)->i%j==0,1:8)
5,7<1
6<2<1
6<3<1
8<4<2

julia> moebius(p)
8-element Vector{Int64}:
1
-1
-1
0
-1
1
-1
0
FinitePosets.:⊕Method

P⊕ Q returns the ordinal sum of two CPosets or of two Posets.

julia> CPoset(:chain,2)⊕ CPoset(:chain,3)
1<2<3<4<5

julia> Poset(:chain,[1,2])⊕ Poset(:chain,[:a,:b,:c])
1<2<a<b<c
FinitePosets.:⊗Method

P⊗ Q returns the ordinal product of two CPosets or of two Posets.

julia> CPoset(:chain,2)⊗ CPoset(:chain,3)
1<3<5<2<4<6

julia> Poset(:chain,[1,2])⊗ Poset(:chain,[:a,:b,:c])
(1, :a)<(1, :b)<(1, :c)<(2, :a)<(2, :b)<(2, :c)
FinitePosets.chainpolyMethod

chainspoly(P) the chain polynomial of the Poset or CPoset, returned as the list of its coefficients.

julia> chainpoly(Poset(:powerset,3))
5-element Vector{Int64}:
1
8
19
18
6
FinitePosets.chainsMethod

chains(P) the chains of the Poset or CPoset.

julia> chains(CPoset(:chain,3))
8-element Vector{Vector{Int64}}:
[]
[1]
[2]
[3]
[1, 2]
[1, 3]
[2, 3]
[1, 2, 3]
FinitePosets.coxetermatrixMethod

coxetermatrix(p) the Coxeter matrix of the Poset or CPoset, defined as -m*transpose(inv(m)) where m is the ζ or incidence matrix.

julia> coxetermatrix(CPoset(:diamond,5))
5×5 Matrix{Int64}:
0  -1  -1  -1  -2
0   0   1   1   1
0   1   0   1   1
0   1   1   0   1
-1  -1  -1  -1  -1
FinitePosets.dotMethod

dot(p) gives a rendering of the Hasse diagram of the Poset or CPoset in the graphical language dot.

FinitePosets.dualMethod

dual(P)

the dual poset to the Poset or CPoset (the order relation is reversed).

julia> p=CPoset((i,j)->i%4<j%4,8)
4,8<1,5<2,6<3,7

julia> dual(p)
3,7<2,6<1,5<4,8
FinitePosets.hasseMethod

hasse(m::Matrix{Bool}) Given an incidence matrix for a poset returns the corresponding Hasse diagram.

julia> m=incidence(CPoset(:diamond,5))
5×5 Matrix{Bool}:
1  1  1  1  1
0  1  0  0  1
0  0  1  0  1
0  0  0  1  1
0  0  0  0  1

julia> hasse(m)
5-element Vector{Vector{Int64}}:
[2, 3, 4]
[5]
[5]
[5]
[]
FinitePosets.hasseMethod

hasse(P::CPoset)

the Hasse diagram of P.

julia> p=CPoset((i,j)->j%i==0,5)
1<3,5
1<2<4

julia> hasse(p)
5-element Vector{Vector{Int64}}:
[2, 3, 5]
[4]
[]
[]
[]       

hasse(P::Poset) returns hasse(P.C).

FinitePosets.incidenceMethod

incidence(P::CPoset)

returns the incidence matrix (also called the ζ matrix) of P.

julia> p=CPoset([i==6 ? Int[] : [i+1] for i in 1:6])
1<2<3<4<5<6

julia> incidence(p)
6×6 Matrix{Bool}:
1  1  1  1  1  1
0  1  1  1  1  1
0  0  1  1  1  1
0  0  0  1  1  1
0  0  0  0  1  1
0  0  0  0  0  1

incidence(P::Poset) returns incidence(P.C).

FinitePosets.inducedMethod

induced(P,S)

returns the subposet induced by P on S, a sublist of P.elements if P isa Poset or a subset of 1:length(P) if P isa CPoset. Note that the sublist S does not have to be in the same order as P.elements, so this can be just used to renumber the elements of P.

julia> p=CPoset((i,j)->i%4<j%4,8)
4,8<1,5<2,6<3,7

julia> induced(p,2:6) # indices are renumbered
3<4<1,5<2

julia> induced(Poset(p),2:6) # elements are kept
4<5<2,6<3
FinitePosets.intervalMethod

interval(P,f::Function,a) interval(P,f::Function,a,g::Function,b)

returns an interval in the Poset or CPoset given by P. The function f must be one of the comparison functions ≤, <, ≥, >. In the first form it returns the interval between a and one end (or the other, depending on the comparison function). In the second form it returns the intersection of the intervals interval(P,f,a) and interval(P,g,b).

julia> l=vec(collect(Iterators.product(1:2,1:2)))
4-element Vector{Tuple{Int64, Int64}}:
(1, 1)
(2, 1)
(1, 2)
(2, 2)

julia> P=Poset((x,y)->all(map(<=,x,y)),l)
(1, 1)<(2, 1),(1, 2)<(2, 2)

julia> interval(P,≤,(1,2)) # elements below (1,2)
2-element Vector{Tuple{Int64, Int64}}:
(1, 1)
(1, 2)

julia> interval(P,≥,(1,2)) # elements above (1,2)
2-element Vector{Tuple{Int64, Int64}}:
(1, 2)
(2, 2)

julia> interval(P,<,(1,2)) # elements strictly below (1,2)
1-element Vector{Tuple{Int64, Int64}}:
(1, 1)

julia> interval(P,≥,(2,1),≤,(2,2)) # elements between (2,1) and (2,2)
2-element Vector{Tuple{Int64, Int64}}:
(2, 1)
(2, 2)

julia> interval(P,>,(1,1),<,(2,2)) # elements strictly between
2-element Vector{Tuple{Int64, Int64}}:
(2, 1)
(1, 2)
julia> interval(P.C,>,1,<,4) # in terms of indices
2-element Vector{Int64}:
2
3
FinitePosets.isjoinlatticeMethod

isjoinlattice(P::CPoset)

returns true if P is a join semilattice, that is any two elements of P have a unique smallest upper bound; returns false otherwise.

julia> p=CPoset((i,j)->j%i==0,8)
1<5,7
1<2<4<8
1<3<6
2<6

julia> isjoinlattice(p)
false

isjoinlattice(P::Poset) returns isjoinlattice(P.C)

FinitePosets.ismeetlatticeMethod

ismeetlattice(P)

returns true if P is a meet semilattice, that is any two elements of P have a unique highest lower bound; returns false otherwise.

julia> p=CPoset((i,j)->j%i==0,8)
1<5,7
1<2<4<8
1<3<6
2<6

julia> ismeetlattice(p)
true

ismeetlattice(P::Poset) returns ismeetlattice(P.C)

FinitePosets.linear_extensionMethod

linear_extension(P::CPoset)

returns a linear extension of the CPoset, that is a vector l containing a permutation of the integers 1:length(P) such that if i<j in P (that is incidence(P)[i,j] is true), then i is before j in l. This is also called a topological sort of P.

julia> p=CPoset((i,j)->j%i==0,6) # divisibility poset on 1:6
1<5
1<2<4
1<3<6
2<6

julia> linear_extension(p)
6-element Vector{Int64}:
1
2
3
5
4
6

linear_extension(P::Poset) returns a linear extension of P.C.

FinitePosets.maximaMethod

maxima(P) the maximal elements of the Poset or CPoset

julia> p=CPoset([[3],[3],[4,5],Int[],Int[]])
1,2<3<4,5

julia> maxima(p)
2-element Vector{Int64}:
4
5
FinitePosets.maximal_chainsMethod

maximal_chains(P) the maximal chains of the Poset or CPoset.

julia> p=Poset([(:a,:b),(:a,:c),(:b,:d),(:c,:d)])
a<b,c<d

julia> maximal_chains(p)
2-element Vector{Vector{Symbol}}:
[:a, :b, :d]
[:a, :c, :d]

julia> maximal_chains(p.C)
2-element Vector{Vector{Int64}}:
[1, 2, 4]
[1, 3, 4]
FinitePosets.minimaMethod

minima(P) the minimal elements of the Poset or CPoset

julia> p=CPoset([[3],[3],[4,5],Int[],Int[]])
1,2<3<4,5

julia> minima(p)
2-element Vector{Int64}:
1
2
FinitePosets.moebiusmatrixMethod

moebiusmatrix(P::CPoset) the matrix of the Moebius function μ(x,y) (the inverse of the ζ or incidence matrix)

julia> moebiusmatrix(CPoset(:diamond,5))
5×5 Matrix{Int64}:
1  -1  -1  -1   2
0   1   0   0  -1
0   0   1   0  -1
0   0   0   1  -1
0   0   0   0   1

moebiusmatrix(P::Poset) returns moebiusmatrix(P.C)

FinitePosets.partitionMethod

partition(P::CPoset)

returns the partition of 1:length(P) induced by the equivalence relation associated to P; that is, i and j are in the same part of the partition if the k such that i<k and j<k are the same as well as the k such that k<i and k<j.

julia> p=CPoset([i==j || i%4<j%4 for i in 1:8, j in 1:8])
4,8<1,5<2,6<3,7

julia> partition(p)
4-element Vector{Vector{Int64}}:
[4, 8]
[2, 6]
[3, 7]
[1, 5]

partition(P::Poset) returns partition(P.C)

FinitePosets.showpicMethod

showpic(p;opt...) display a graphical representation of the Hasse diagram of the Poset or CPoset using the commands dot and open. If p isa Poset it is possible to give as keyword aguments a list of IO properties which will be forwarded to the show_element method of p.

FinitePosets.transitive_closureMethod

transitive_closure(M) transitive_closure!(M)

M should be a square boolean matrix representing a relation; transitive_closure returns a boolean matrix representing the transitive closure of this relation; transitive_closure! modifies M in place, doing no allocations. The transitive closure is computed by the Floyd-Warshall algorithm, which is quite fast even for large matrices.

julia> m=[j-i in [0,1] for i in 1:5, j in 1:5]
5×5 Matrix{Bool}:
1  1  0  0  0
0  1  1  0  0
0  0  1  1  0
0  0  0  1  1
0  0  0  0  1

julia>transitive_closure(m)
5×5 Matrix{Bool}:
1  1  1  1  1
0  1  1  1  1
0  0  1  1  1
0  0  0  1  1
0  0  0  0  1