Polyhedral: add hash method for Cone + Polyhedron

docs/src/PolyhedralGeometry/linear_programs.md

@@ -101,6 +101,7 @@ julia> V = optimal_vertex(LP)
 
 ```@docs
 feasible_region(lp::LinearProgram)
+ambient_dim(lp::LinearProgram)
 objective_function(lp::LinearProgram{T}; as::Symbol = :pair) where T<:scalar_types
 solve_lp(LP::LinearProgram)
 optimal_value(lp::LinearProgram{T}) where T<:scalar_types

docs/src/PolyhedralGeometry/mixed_integer_linear_programs.md

@@ -28,6 +28,8 @@ mixed_integer_linear_program
 
 ## Functions
 ```@docs
+feasible_region(milp::MixedIntegerLinearProgram)
+ambient_dim(milp::MixedIntegerLinearProgram)
 optimal_value(milp::MixedIntegerLinearProgram{T}) where T<:scalar_types
 optimal_solution
 solve_milp

src/PolyhedralGeometry/Cone/constructors.jl

@@ -84,13 +84,18 @@ cone(f::scalar_type_or_field, x...) = positive_hull(f, x...)
 
 
 function ==(C0::Cone{T}, C1::Cone{T}) where T<:scalar_types
-    # TODO: Remove the following 3 lines, see #758
-    for pair in Iterators.product([C0, C1], ["RAYS", "FACETS"])
-        Polymake.give(pm_object(pair[1]),pair[2])
-    end
-    return Polymake.polytope.equal_polyhedra(pm_object(C0), pm_object(C1))
+    return Polymake.polytope.equal_polyhedra(pm_object(C0), pm_object(C1))::Bool
 end
 
+# For a proper hash function for cones we should use a "normal form",
+# which would require a potentially expensive convex hull computation
+# (and even that is not enough). But hash methods should be fast, so we
+# just consider the ambient dimension and the precise type of the cone.
+function Base.hash(x::T, h::UInt) where {T <: Cone}
+  h = hash(ambient_dim(x), h)
+  h = hash(T, h)
+  return h
+end
 
 @doc raw"""
     cone_from_inequalities([::Union{Type{T}, Field} = QQFieldElem,] I::AbstractCollection[LinearHalfspace] [, E::AbstractCollection[LinearHyperplane]]; non_redundant::Bool = false)

src/PolyhedralGeometry/Polyhedron/constructors.jl

@@ -137,6 +137,16 @@ function ==(P0::Polyhedron{T}, P1::Polyhedron{T}) where T<:scalar_types
     Polymake.polytope.equal_polyhedra(pm_object(P0), pm_object(P1))
 end
 
+# For a proper hash function for cones we should use a "normal form",
+# which would require a potentially expensive convex hull computation
+# (and even that is not enough). But hash methods should be fast, so we
+# just consider the ambient dimension and the precise type of the polyhedron.
+function Base.hash(x::T, h::UInt) where {T <: Polyhedron}
+  h = hash(ambient_dim(x), h)
+  h = hash(T, h)
+  return h
+end
+
 ### Construct polyhedron from V-data, as the convex hull of points, rays and lineality.
 @doc raw"""
     convex_hull([::Union{Type{T}, Field} = QQFieldElem,] V [, R [, L]]; non_redundant::Bool = false)

src/PolyhedralGeometry/linear_program.jl

@@ -207,3 +207,10 @@ Return a pair `(m,v)` where the optimal value `m` of the objective
  `nothing`.
 """
 solve_lp(lp::LinearProgram) = optimal_value(lp), optimal_vertex(lp)
+
+@doc raw"""
+    ambient_dim(LP::LinearProgram)
+
+Return the ambient dimension of the feasible reagion of `LP`.
+"""
+ambient_dim(lp::LinearProgram) = ambient_dim(feasible_region(lp))

src/PolyhedralGeometry/mixed_integer_linear_program.jl

@@ -248,3 +248,10 @@ julia> solve_milp(milp)
 ```
 """
 solve_milp(milp::MixedIntegerLinearProgram) = optimal_value(milp), optimal_solution(milp)
+
+@doc raw"""
+    ambient_dim(MILP::MixedIntegerLinearProgram)
+
+Return the ambient dimension of the feasible reagion of `MILP`.
+"""
+ambient_dim(milp::MixedIntegerLinearProgram) = ambient_dim(feasible_region(milp))

test/PolyhedralGeometry/cone.jl

@@ -78,6 +78,9 @@
     @test is_fulldimensional(Cone2)
     @test Cone2 == Cone3
     @test Cone4 != Cone2
+
+    @test length(unique([Cone2, Cone3, Cone4])) == 2
+
     @test dim(Cone4) == 2
     @test dim(Cone2) == 3
     @test ambient_dim(Cone2) == 3

test/PolyhedralGeometry/linear_program.jl

@@ -24,6 +24,8 @@
     @test LP2 isa LinearProgram{T}
     @test LP3 isa LinearProgram{T}
 
+    @test ambient_dim(LP1) == 2
+
     @test solve_lp(LP1)==(4,[1,1])
     @test solve_lp(LP2)==(-1,[-1,-1])
     if T == QQFieldElem
@@ -43,6 +45,8 @@
       @test MILP2 isa MixedIntegerLinearProgram{T}
       @test MILP3 isa MixedIntegerLinearProgram{T}
 
+      @test ambient_dim(MILP3) == 3
+
       @test solve_milp(MILP1)==(11//2,[1,3//2])
       @test solve_milp(MILP2)==(-1,[-1,-1])
       @test string(solve_milp(MILP3))==string("(inf, nothing)")

test/PolyhedralGeometry/polyhedron.jl

@@ -183,6 +183,7 @@
     @test vertex_sizes(Q1)[1] == 2
     @test length(vertex_sizes(Q2)) == 0
 
+    @test length(unique([cube(2), cube(2), simplex(2), simplex(2)])) == 2
   end
 
   @testset "volume" begin
@@ -221,12 +222,10 @@
       @test upper_bound_h_vector(4,8) == [1, 4, 10, 4 ,1]
       A = archimedean_solid("cuboctahedron")
       @test count(F -> nvertices(F) == 3, faces(A, 2)) == 8
-      # due to GLIBCXX issues with the outdated julia-shipped libstdc++
-      # we run this only where recent CompilerSupportLibraries are available
-      if VERSION >= v"1.6"
-        C = catalan_solid("triakis_tetrahedron")
-        @test count(F -> nvertices(F) == 3, faces(C, 2)) == 12
-      end
+
+      C = catalan_solid("triakis_tetrahedron")
+      @test count(F -> nvertices(F) == 3, faces(C, 2)) == 12
+
       @test polyhedron(facets(A)) == A
       b1 = birkhoff_polytope(3)
       b2 = birkhoff_polytope(3, even = true)