bump everything (#2)

* bump everything

* switch "solver" to "optimizer"

Project.toml

@@ -1,7 +1,7 @@
 name = "GraphsMatching"
 uuid = "c3af3a8c-b79e-4b01-bf44-c718d7e0e0d6"
 authors = ["JuliaGraphs"]
-version = "0.1.0"
+version = "0.2.0"
 
 [deps]
 BlossomV = "6c721016-9dae-5d90-abf6-67daaccb2332"
@@ -14,10 +14,10 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 BlossomV = "0.4"
-Graphs = "1.4.0"
-Hungarian = "0.4"
-JuMP = "0.20"
-MathOptInterface = "0.9"
+Graphs = "1.4, 1.7"
+Hungarian = "0.4, 0.6"
+JuMP = "1"
+MathOptInterface = "1"
 julia = "1"
 
 [extras]

src/lp.jl

@@ -1,8 +1,8 @@
-function maximum_weight_maximal_matching_lp(g::Graph, solver::JuMP.OptimizerFactory, w::AbstractMatrix{T}, cutoff::R) where {T<:Real, R<:Real}
-    return maximum_weight_maximal_matching_lp(g, solver, cutoff_weights(w, cutoff))
+function maximum_weight_maximal_matching_lp(g::Graph, optimizer, w::AbstractMatrix{T}, cutoff::R) where {T<:Real, R<:Real}
+    return maximum_weight_maximal_matching_lp(g, optimizer, cutoff_weights(w, cutoff))
 end
 
-function maximum_weight_maximal_matching_lp(g::Graph, solver::JuMP.OptimizerFactory, w::AbstractMatrix{T}) where {T<:Real}
+function maximum_weight_maximal_matching_lp(g::Graph, optimizer, w::AbstractMatrix{T}) where {T<:Real}
 # TODO support for graphs with zero degree nodes
 # TODO apply separately on each connected component
     bpmap = bipartite_map(g)
@@ -26,7 +26,7 @@ function maximum_weight_maximal_matching_lp(g::Graph, solver::JuMP.OptimizerFact
         end
     end
 
-    model = Model(solver)
+    model = Model(optimizer)
     @variable(model, x[1:length(w)] >= 0)
 
     for i in v1

src/maximum_weight_matching.jl

@@ -22,10 +22,10 @@ Returns MatchingResult containing:
 function maximum_weight_matching end
 
 function maximum_weight_matching(g::Graph,
-          solver::JuMP.OptimizerFactory,
+          optimizer,
           w::AbstractMatrix{U} = default_weights(g)) where {U <:Real}
 
-    model = Model(with_optimizer(solver))
+    model = Model(optimizer)
     n = nv(g)
     edge_list = collect(edges(g))
 
@@ -40,7 +40,7 @@ function maximum_weight_matching(g::Graph,
         end
       end
     end
-    
+
     if is_bipartite(g)
       @variable(model, x[edge_list] >= 0) # no need to enforce integrality
     else

src/maximum_weight_maximal_matching.jl

@@ -11,16 +11,13 @@ Forces the maximum_weight_maximal_matching function to use a linear programming
 struct LPAlgorithm <: AbstractMaximumWeightMaximalMatchingAlgorithm end
 
 function maximum_weight_maximal_matching(
-    g::Graph, 
-    w::AbstractMatrix{T}, 
-    algorithm::LPAlgorithm, 
-    solver = nothing
+    g::Graph,
+    w::AbstractMatrix{T},
+    algorithm::LPAlgorithm,
+    optimizer = nothing
 ) where {T<:Real}
-    if ! isa(solver, JuMP.OptimizerFactory)
-        error("The keyword argument solver must be an JuMP.OptimizerFactory, as accepted by JuMP.")
-    end
 
-    return maximum_weight_maximal_matching_lp(g, solver, w)
+    return maximum_weight_maximal_matching_lp(g, optimizer, w)
 end
 
 """
@@ -30,10 +27,10 @@ Forces the maximum_weight_maximal_matching function to use the Hungarian algorit
 struct HungarianAlgorithm <: AbstractMaximumWeightMaximalMatchingAlgorithm end
 
 function maximum_weight_maximal_matching(
-    g::Graph, 
-    w::AbstractMatrix{T}, 
-    algorithm::HungarianAlgorithm, 
-    solver = nothing
+    g::Graph,
+    w::AbstractMatrix{T},
+    algorithm::HungarianAlgorithm,
+    optimizer = nothing
 ) where {T<:Real}
     return maximum_weight_maximal_matching_hungarian(g, w)
 end
@@ -50,34 +47,34 @@ Edges in `g` not present in `w` will not be considered for the matching.
 A `cutoff` keyword argument can be given, to reduce computational times
 excluding edges with weights lower than the cutoff.
 
-Finally, a specific algorithm can be chosen (`algorithm` keyword argument); 
-each algorithm has specific dependencies. For instance: 
+Finally, a specific algorithm can be chosen (`algorithm` keyword argument);
+each algorithm has specific dependencies. For instance:
 
-- If `algorithm=HungarianAlgorithm()` (the default), the package Hungarian.jl is used. 
-  This algorithm is always polynomial in time, with complexity O(n³). 
-- If `algorithm=LPAlgorithm()`, the package JuMP.jl and one of its supported solvers is required. 
+- If `algorithm=HungarianAlgorithm()` (the default), the package Hungarian.jl is used.
+  This algorithm is always polynomial in time, with complexity O(n³).
+- If `algorithm=LPAlgorithm()`, the package JuMP.jl and one of its supported solvers is required.
   In this case, the algorithm relies on a linear relaxation on of the matching problem, which is
-  guaranteed to have integer solution on bipartite graphs. A solver must be provided with 
-  the `solver` keyword parameter. 
+  guaranteed to have integer solution on bipartite graphs. A solver must be provided with
+  the `optimizer` keyword parameter.
 
 The returned object is of type `MatchingResult`.
 """
 function maximum_weight_maximal_matching(
-        g::Graph, 
-        w::AbstractMatrix{T}; 
+        g::Graph,
+        w::AbstractMatrix{T};
         cutoff = nothing,
-        algorithm::AbstractMaximumWeightMaximalMatchingAlgorithm = HungarianAlgorithm(), 
-        solver = nothing
+        algorithm::AbstractMaximumWeightMaximalMatchingAlgorithm = HungarianAlgorithm(),
+        optimizer = nothing
     ) where {T<:Real}
 
     if cutoff != nothing && ! isa(cutoff, Real)
         error("The cutoff value must be of type Real or nothing.")
     end
 
     if cutoff != nothing
-        return maximum_weight_maximal_matching(g, cutoff_weights(w, cutoff), algorithm, solver)
+        return maximum_weight_maximal_matching(g, cutoff_weights(w, cutoff), algorithm, optimizer)
     else
-        return maximum_weight_maximal_matching(g, w, algorithm, solver)
+        return maximum_weight_maximal_matching(g, w, algorithm, optimizer)
     end
 end
 
@@ -96,4 +93,4 @@ function cutoff_weights(w::AbstractMatrix{T}, cutoff::R) where {T<:Real, R<:Real
     wnew
 end
 
-@deprecate maximum_weight_maximal_matching(g::Graph, solver::JuMP.OptimizerFactory, w::AbstractMatrix{T}, cutoff::R) where {T<:Real, R<:Real} maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), cutoff=cutoff, solver=solver)
+@deprecate maximum_weight_maximal_matching(g::Graph, optimizer, w::AbstractMatrix{T}, cutoff::R) where {T<:Real, R<:Real} maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), cutoff=cutoff, optimizer=optimizer)

test/runtests.jl

@@ -14,7 +14,7 @@ using LinearAlgebra: I
          1 1 1
          3 1 1
         ]
-    match = maximum_weight_matching(g, with_optimizer(Cbc.Optimizer, logLevel=0), w)
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w)
     @test match.mate[1] == 3
     @test match.weight ≈ 3
 
@@ -23,7 +23,7 @@ using LinearAlgebra: I
     w[1,2] = 1
     w[3,2] = 1
     w[1,3] = 1
-    match = maximum_weight_matching(g,with_optimizer(Cbc.Optimizer, logLevel=0),w)
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
     @test match.weight ≈ 1
 
 
@@ -37,7 +37,7 @@ using LinearAlgebra: I
     w[1,4] = 3
     w[2,4] = 1
 
-    match = maximum_weight_matching(g,with_optimizer(Cbc.Optimizer, logLevel=0),w)
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
     @test match.weight ≈ 3
     @test match.mate[1] == 4
     @test match.mate[2] == -1
@@ -49,7 +49,7 @@ using LinearAlgebra: I
     add_edge!(g, 2,3)
     add_edge!(g, 3,1)
     add_edge!(g, 3,4)
-    match = maximum_weight_matching(g,with_optimizer(Cbc.Optimizer, logLevel=0))
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
     @test match.weight ≈ 2
     @test match.mate[1] == 2
     @test match.mate[2] == 1
@@ -62,7 +62,7 @@ using LinearAlgebra: I
     w[1,3] = 1
     w[3,4] = 1
 
-    match = maximum_weight_matching(g,with_optimizer(Cbc.Optimizer, logLevel=0), w)
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w)
     @test match.weight ≈ 2
     @test match.mate[1] == 2
     @test match.mate[2] == 1
@@ -75,7 +75,7 @@ using LinearAlgebra: I
     w[1,3] = 5
     w[3,4] = 1
 
-    match = maximum_weight_matching(g,with_optimizer(Cbc.Optimizer, logLevel=0),w)
+    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
     @test match.weight ≈ 5
     @test match.mate[1] == 3
     @test match.mate[2] == -1
@@ -92,7 +92,7 @@ end
     w[1,4] = 1.
     w[2,3] = 2.
     w[2,4] = 11.
-    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), solver=with_optimizer(Cbc.Optimizer, logLevel=0))
+    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
     @test match.weight ≈ 21
     @test match.mate[1] == 3
     @test match.mate[3] == 1
@@ -105,7 +105,7 @@ end
     w[1,4] = 0.5
     w[2,3] = 11
     w[2,4] = 1
-    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), solver=with_optimizer(Cbc.Optimizer, logLevel=0))
+    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0))
     @test match.weight ≈ 11.5
     @test match.mate[1] == 4
     @test match.mate[4] == 1
@@ -120,7 +120,7 @@ end
     w[2,4] = 1
     w[2,5] = -1
     w[2,6] = -1
-    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), solver=with_optimizer(Cbc.Optimizer, logLevel=0), cutoff=0)
+    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), cutoff=0)
     @test match.weight ≈ 11.5
     @test match.mate[1] == 4
     @test match.mate[4] == 1
@@ -135,16 +135,16 @@ end
     w[1,6] = 1
     w[1,5] = -1
 
-    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), solver=with_optimizer(Cbc.Optimizer, logLevel=0), cutoff=0)
+    match = maximum_weight_maximal_matching(g, w, algorithm=LPAlgorithm(), optimizer=optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), cutoff=0)
     @test match.weight ≈ 12
     @test match.mate[1] == 6
     @test match.mate[2] == 5
     @test match.mate[3] == -1
     @test match.mate[4] == -1
     @test match.mate[5] == 2
     @test match.mate[6] == 1
-    
-    
+
+
     g = complete_bipartite_graph(2, 2)
     w = zeros(4, 4)
     w[1, 3] = 10.
@@ -178,10 +178,10 @@ end
     add_edge!(g, 2, 4)
     w = zeros(4, 4)
     w[1, 3] = 1
-    w[1, 4] = 3 
+    w[1, 4] = 3
     w[2, 4] = 1
     match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-    @test match.weight ≈ 2 
+    @test match.weight ≈ 2
 
 end