option for maximum spanning tree

src/spanningtrees/kruskal.jl

@@ -1,12 +1,15 @@
 """
-    kruskal_mst(g, distmx=weights(g))
-Return a vector of edges representing the minimum spanning tree of a connected, undirected graph `g` with optional
+    kruskal_mst(g, distmx=weights(g); minimum=true)
+Return a vector of edges representing the minimum (by default) spanning tree of a connected, undirected graph `g` with optional
 distance matrix `distmx` using [Kruskal's algorithm](https://en.wikipedia.org/wiki/Kruskal%27s_algorithm).
+
+### Optional Arguments
+- `minimize=true`: if set to `false`, calculate the maximum spanning tree.
 """
 function kruskal_mst end
 # see https://github.com/mauro3/SimpleTraits.jl/issues/47#issuecomment-327880153 for syntax
 @traitfn function kruskal_mst(g::AG::(!IsDirected),
-    distmx::AbstractMatrix{T}=weights(g)) where {T <: Real, U, AG <: AbstractGraph{U}}
+    distmx::AbstractMatrix{T}=weights(g); minimize=true) where {T <: Real, U, AG <: AbstractGraph{U}}
 
     connected_vs = IntDisjointSets(nv(g))
 
@@ -20,7 +23,7 @@ function kruskal_mst end
         push!(weights, distmx[src(e), dst(e)])
     end
 
-    for e in edge_list[sortperm(weights)]
+    for e in edge_list[sortperm(weights; rev=!minimize)]
         if !in_same_set(connected_vs, src(e), dst(e))
             union!(connected_vs, src(e), dst(e))
             push!(mst, e)

test/spanningtrees/kruskal.jl

@@ -9,10 +9,12 @@
     ]
 
     vec_mst = Vector{Edge}([Edge(1, 2), Edge(3, 4), Edge(2, 3)])
+    max_vec_mst = Vector{Edge}([Edge(2, 4), Edge(1, 4), Edge(1, 3)])
     for g in testgraphs(g4)
     # Testing Kruskal's algorithm
         mst = @inferred(kruskal_mst(g, distmx))
         @test mst == vec_mst
+	@test @inferred(kruskal_mst(g, distmx, minimize=false)) == max_vec_mst
     end
     #second test
     distmx_sec = [
@@ -28,8 +30,10 @@
 
     gx = SimpleGraph(distmx_sec)
     vec2 = Vector{Edge}([Edge(1, 8), Edge(3, 4), Edge(2, 8), Edge(1, 3), Edge(6, 8), Edge(5, 6), Edge(3, 7)])
+    max_vec2 = Vector{Edge}([Edge(5, 7), Edge(1, 7), Edge(4, 7), Edge(3, 7), Edge(5, 8), Edge(2, 3), Edge(5, 6)])
     for g in testgraphs(gx)
         mst2 = @inferred(kruskal_mst(g, distmx_sec))
         @test mst2 == vec2
+	@test @inferred(kruskal_mst(g, distmx_sec, minimize=false)) == max_vec2
     end
 end