update

Author: GiggleLiu

examples/Coloring.jl

@@ -58,12 +58,14 @@ is_good_vertex_coloring(graph, single_solution.c.data)
 
 vertex_color_map = Dict(0=>"red", 1=>"green", 2=>"blue")
 
-show_graph(graph; locs=locations, vertex_colors=[vertex_color_map[Int(c)] for c in single_solution.c.data])
+show_graph(graph; locs=locations, vertex_colors=[vertex_color_map[Int(c)]
+     for c in single_solution.c.data])
 
 # Let us try to solve the same issue on its line graph, a graph that generated by mapping an edge to a vertex and two edges sharing a common vertex will be connected.
 linegraph = line_graph(graph)
 
-show_graph(linegraph; locs=[0.5 .* (locations[e.src] .+ locations[e.dst]) for e in edges(graph)])
+show_graph(linegraph; locs=[0.5 .* (locations[e.src] .+ locations[e.dst])
+     for e in edges(graph)])
 
 # Let us construct the tensor network and see if there are solutions.
 lineproblem = Coloring{3}(linegraph);

examples/IndependentSet.jl

@@ -86,8 +86,8 @@ max_config = solve(problem, SingleConfigMax(; bounded=false))[]
 # The return value contains a bit string, and one should read this bit string from left to right.
 # Having value 1 at i-th bit means vertex ``i`` is in the maximum independent set.
 # One can visualize this MIS with the following function.
-show_graph(graph; locs=locations, vertex_colors=[iszero(max_config.c.data[i]) ? "white" : "red"
-                                 for i=1:nv(graph)])
+show_graph(graph; locs=locations, vertex_colors=
+    [iszero(max_config.c.data[i]) ? "white" : "red" for i=1:nv(graph)])
 
 # ##### enumeration of all MISs
 # There are two approaches to enumerate all best-K solutions.
@@ -99,11 +99,12 @@ using Compose
 m = length(all_max_configs.c)
 
 imgs = ntuple(k->show_graph(graph;
-                            locs=locations, scale=0.25,
-                            vertex_colors=[iszero(all_max_configs.c[k][i]) ? "white" : "red"
-                            for i=1:nv(graph)]), m);
+                    locs=locations, scale=0.25,
+                    vertex_colors=[iszero(all_max_configs.c[k][i]) ? "white" : "red"
+                    for i=1:nv(graph)]), m);
 
-Compose.set_default_graphic_size(18cm, 4cm); Compose.compose(context(), ntuple(k->(context((k-1)/m, 0.0, 1.2/m, 1.0), imgs[k]), m)...)
+Compose.set_default_graphic_size(18cm, 4cm); Compose.compose(context(),
+     ntuple(k->(context((k-1)/m, 0.0, 1.2/m, 1.0), imgs[k]), m)...)
 
 # ##### enumeration of all IS configurations
 all_independent_sets = solve(problem, ConfigsAll())[]

examples/Matching.jl

@@ -69,6 +69,7 @@ matching_poly = solve(problem, GraphPolynomial())[]
 match_config = solve(problem, SingleConfigMax())[]
 
 # Let us show the result by coloring the matched edges to red
-show_graph(graph; locs=locations, edge_colors=[isone(match_config.c.data[i]) ? "red" : "black" for i=1:ne(graph)])
+show_graph(graph; locs=locations, edge_colors=
+    [isone(match_config.c.data[i]) ? "red" : "black" for i=1:ne(graph)])
 
 # where we use edges with red color to related pairs of matched vertices.

examples/MaximalIS.jl

@@ -62,11 +62,12 @@ max_config = solve(problem, GraphPolynomial())[]
 maximal_configs = solve(problem, ConfigsAll())[]
 
 imgs = ntuple(k->show_graph(graph;
-                            locs=locations, scale=0.25,
-                            vertex_colors=[iszero(maximal_configs[k][i]) ? "white" : "red"
-                            for i=1:nv(graph)]), length(maximal_configs));
+                locs=locations, scale=0.25,
+                vertex_colors=[iszero(maximal_configs[k][i]) ? "white" : "red"
+                for i=1:nv(graph)]), length(maximal_configs));
 
-Compose.set_default_graphic_size(18cm, 12cm); Compose.compose(context(), ntuple(k->(context((mod1(k,5)-1)/5, ((k-1)÷5)/3, 1.2/5, 1.0/3), imgs[k]), 15)...)
+Compose.set_default_graphic_size(18cm, 12cm); Compose.compose(context(),
+     ntuple(k->(context((mod1(k,5)-1)/5, ((k-1)÷5)/3, 1.2/5, 1.0/3), imgs[k]), 15)...)
 
 # This result should be consistent with that given by the [Bron Kerbosch algorithm](https://en.wikipedia.org/wiki/Bron%E2%80%93Kerbosch_algorithm) on the complement of Petersen graph.
 cliques = maximal_cliques(complement(graph))