diff --git a/Project.toml b/Project.toml
index 5783ce8..a559d4b 100644
--- a/Project.toml
+++ b/Project.toml
@@ -11,6 +11,7 @@ JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+WatchJuliaBurn = "952999c0-2bc0-44cb-b633-dc1e299b187b"
 
 [compat]
 BlossomV = "0.4"
@@ -18,6 +19,7 @@ Graphs = "1.4, 1.7"
 Hungarian = "0.4, 0.6"
 JuMP = "1"
 MathOptInterface = "1"
+WatchJuliaBurn = "0.2.0"
 julia = "1"
 
 [extras]
diff --git a/src/blossomv.jl b/src/blossomv.jl
index da1e2d4..29e2bfc 100644
--- a/src/blossomv.jl
+++ b/src/blossomv.jl
@@ -1,19 +1,12 @@
 """
-    minimum_weight_perfect_matching(g, w::Dict{Edge,Real})
-    minimum_weight_perfect_matching(g, w::Dict{Edge,Real}, cutoff)
+    minimum_weight_perfect_matching(g, w::AbstractMatrix{U})
 
-Given a graph `g` and an edgemap `w` containing weights associated to edges,
-returns a matching with the mimimum total weight among the ones containing
-exactly `nv(g)/2` edges.
-
-Edges in `g` not present in `w` will not be considered for the matching.
+Given a graph `g` and weights `w`, returns a matching with the mimimum total
+weight among the ones containing exactly `nv(g)/2` edges.
 
 This function relies on the BlossomV.jl package, a julia wrapper
 around Kolmogorov's BlossomV algorithm.
 
-Eventually a `cutoff` argument can be given, to the reduce computational time
-excluding edges with weights higher than the cutoff.
-
 The returned object is of type `MatchingResult`.
 
 In case of error try to change the optional argument `tmaxscale` (default is `tmaxscale=10`).
@@ -68,3 +61,22 @@ function minimum_weight_perfect_matching(g::Graph, w::Dict{E,U}) where {U<:Integ
     end
     return MatchingResult(totweight, mate)
 end
+
+function minimum_weight_perfect_matching(g::Graph, w::AbstractMatrix{U}, cutoff, kws...) where {U <: Real}
+	wnew = Dict{Edge, U}()
+	for e in edges(g)
+		c = w[src(e), dst(e)]
+		if c <= cutoff
+			wnew[e] = c
+		end
+	end
+	return minimum_weight_perfect_matching(g, wnew, kws...)
+end
+
+function minimum_weight_perfect_matching(g::Graph, w::AbstractMatrix{U} = default_weights(g), kws...) where {U <: Real}
+	wnew = Dict{Edge, U}()
+	for e in edges(g)
+		wnew[e] = w[src(e), dst(e)]
+	end
+	return minimum_weight_perfect_matching(g, wnew, kws...)
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index 0beb14f..31dc01b 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -6,255 +6,357 @@ using JuMP
 using LinearAlgebra: I
 
 @testset "GraphsMatching" begin
-
-@testset "maximum_weight_matching" begin
-    g = complete_graph(3)
-    w = [
-         1 2 1
-         1 1 1
-         3 1 1
+    @testset "maximum_weight_matching" begin
+        g = complete_graph(3)
+        w = [
+            1 2 1
+            1 1 1
+            3 1 1
         ]
-    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w)
-    @test match.mate[1] == 3
-    @test match.weight ≈ 3
-
-    g = complete_graph(3)
-    w = zeros(3,3)
-    w[1,2] = 1
-    w[3,2] = 1
-    w[1,3] = 1
-    match = maximum_weight_matching(g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0),w)
-    @test match.weight ≈ 1
-
-
-    g = Graph(4)
-    add_edge!(g, 1,3)
-    add_edge!(g, 1,4)
-    add_edge!(g, 2,4)
-
-    w =zeros(4,4)
-    w[1,3] = 1
-    w[1,4] = 3
-    w[2,4] = 1
-
-    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
-    @test match.mate[3] == -1
-    @test match.mate[4] == 1
-
-    g = Graph(4)
-    add_edge!(g, 1,2)
-    add_edge!(g, 2,3)
-    add_edge!(g, 3,1)
-    add_edge!(g, 3,4)
-    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
-    @test match.mate[3] == 4
-    @test match.mate[4] == 3
-
-    w = zeros(4,4)
-    w[1,2] = 1
-    w[2,3] = 1
-    w[1,3] = 1
-    w[3,4] = 1
-
-    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
-    @test match.mate[3] == 4
-    @test match.mate[4] == 3
-
-    w = zeros(4,4)
-    w[1,2] = 1
-    w[2,3] = 1
-    w[1,3] = 5
-    w[3,4] = 1
-
-    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
-    @test match.mate[3] == 1
-    @test match.mate[4] == -1
-end
-
-
-@testset "maximum_weight_maximal_matching" begin
-
-    g = complete_bipartite_graph(2,2)
-    w = zeros(4,4)
-    w[1,3] = 10.
-    w[1,4] = 1.
-    w[2,3] = 2.
-    w[2,4] = 11.
-    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
-    @test match.mate[2] == 4
-    @test match.mate[4] == 2
-
-    g =complete_bipartite_graph(2,4)
-    w =zeros(6,6)
-    w[1,3] = 10
-    w[1,4] = 0.5
-    w[2,3] = 11
-    w[2,4] = 1
-    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
-    @test match.mate[2] == 3
-    @test match.mate[3] == 2
-
-    g =complete_bipartite_graph(2,6)
-    w =zeros(8,8)
-    w[1,3] = 10
-    w[1,4] = 0.5
-    w[2,3] = 11
-    w[2,4] = 1
-    w[2,5] = -1
-    w[2,6] = -1
-    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
-    @test match.mate[2] == 3
-    @test match.mate[3] == 2
-
-    g =complete_bipartite_graph(4,2)
-    w = zeros(6,6)
-    w[3,5] = 10
-    w[3,6] = 0.5
-    w[2,5] = 11
-    w[1,6] = 1
-    w[1,5] = -1
-
-    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.
-    w[1, 4] = 1.
-    w[2, 3] = 2.
-    w[2, 4] = 11.
-    match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-    @test match.weight ≈ 21
-    @test match.mate[1] == 3
-    @test match.mate[3] == 1
-    @test match.mate[2] == 4
-    @test match.mate[4] == 2
-
-    g = complete_graph(3)
-    w = zeros(3, 3)
-    @test ! is_bipartite(g)
-    @test_throws ErrorException maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-
-    g = complete_bipartite_graph(2, 4)
-    w = zeros(6, 6)
-    w[1, 3] = 10
-    w[1, 4] = 0.5
-    w[2, 3] = 11
-    w[2, 4] = 1
-    match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-    @test match.weight ≈ 11.5
-
-    g = Graph(4)
-    add_edge!(g, 1, 3)
-    add_edge!(g, 1, 4)
-    add_edge!(g, 2, 4)
-    w = zeros(4, 4)
-    w[1, 3] = 1
-    w[1, 4] = 3
-    w[2, 4] = 1
-    match = maximum_weight_maximal_matching(g, w, algorithm=HungarianAlgorithm())
-    @test match.weight ≈ 2
-
-end
-
-
-
-@testset "minimum_weight_perfect_matching" begin
-
-    w = Dict(Edge(1,2)=> 500)
-    g =Graph(2)
-    add_edge!(g,1,2)
-    match = minimum_weight_perfect_matching(g, w)
-    @test match.mate[1] == 2
-
-
-    w=Dict( Edge(1,2)=>500,
-           Edge(1,3)=>600,
-           Edge(2,3)=>700,
-           Edge(3,4)=>100,
-           Edge(2,4)=>1000)
-
-    g = complete_graph(4)
-    match = minimum_weight_perfect_matching(g, w)
-    @test match.mate[1] == 2
-    @test match.mate[2] == 1
-    @test match.mate[3] == 4
-    @test match.mate[4] == 3
-    @test match.weight ≈ 600
-
-    w = Dict(
-             Edge(1, 2) => 500,
-             Edge(1, 3) => 400,
-             Edge(2, 3) => 300,
-             Edge(3, 4) => 1000,
-             Edge(2, 4) => 1000
-            )
-    g = complete_graph(4)
-    match = minimum_weight_perfect_matching(g, w)
-    @test match.mate[1] == 3
-    @test match.mate[2] == 4
-    @test match.mate[3] == 1
-    @test match.mate[4] == 2
-    @test match.weight ≈ 1400
-
-    g =complete_bipartite_graph(2,2)
-    w =Dict{Edge,Float64}()
-    w[Edge(1,3)] = -10
-    w[Edge(1,4)] = -0.5
-    w[Edge(2,3)] = -11
-    w[Edge(2,4)] = -1
-
-    match = minimum_weight_perfect_matching(g, w)
-    @test match.mate[1] == 4
-    @test match.mate[4] == 1
-    @test match.mate[2] == 3
-    @test match.mate[3] == 2
-    @test match.weight ≈ -11.5
-
-
-    g = complete_graph(4)
-    w = Dict{Edge,Float64}()
-    w[Edge(1,3)] = 10
-    w[Edge(1,4)] = 0.5
-    w[Edge(2,3)] = 11
-    w[Edge(2,4)] = 2
-    w[Edge(1,2)] = 100
-
-    match = minimum_weight_perfect_matching(g, w, 50)
-    @test match.mate[1] == 4
-    @test match.mate[4] == 1
-    @test match.mate[2] == 3
-    @test match.mate[3] == 2
-    @test match.weight ≈ 11.5
-end
-
-
+        match = maximum_weight_matching(
+            g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+        )
+        @test match.mate[1] == 3
+        @test match.weight ≈ 3
+
+        g = complete_graph(3)
+        w = zeros(3, 3)
+        w[1, 2] = 1
+        w[3, 2] = 1
+        w[1, 3] = 1
+        match = maximum_weight_matching(
+            g, optimizer_with_attributes(Cbc.Optimizer, "LogLevel" => 0), w
+        )
+        @test match.weight ≈ 1
+
+        g = Graph(4)
+        add_edge!(g, 1, 3)
+        add_edge!(g, 1, 4)
+        add_edge!(g, 2, 4)
+
+        w = zeros(4, 4)
+        w[1, 3] = 1
+        w[1, 4] = 3
+        w[2, 4] = 1
+
+        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
+        @test match.mate[3] == -1
+        @test match.mate[4] == 1
+
+        g = Graph(4)
+        add_edge!(g, 1, 2)
+        add_edge!(g, 2, 3)
+        add_edge!(g, 3, 1)
+        add_edge!(g, 3, 4)
+        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
+        @test match.mate[3] == 4
+        @test match.mate[4] == 3
+
+        w = zeros(4, 4)
+        w[1, 2] = 1
+        w[2, 3] = 1
+        w[1, 3] = 1
+        w[3, 4] = 1
+
+        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
+        @test match.mate[3] == 4
+        @test match.mate[4] == 3
+
+        w = zeros(4, 4)
+        w[1, 2] = 1
+        w[2, 3] = 1
+        w[1, 3] = 5
+        w[3, 4] = 1
+
+        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
+        @test match.mate[3] == 1
+        @test match.mate[4] == -1
+    end
+
+    @testset "maximum_weight_maximal_matching" begin
+        g = complete_bipartite_graph(2, 2)
+        w = zeros(4, 4)
+        w[1, 3] = 10.0
+        w[1, 4] = 1.0
+        w[2, 3] = 2.0
+        w[2, 4] = 11.0
+        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
+        @test match.mate[2] == 4
+        @test match.mate[4] == 2
+
+        g = complete_bipartite_graph(2, 4)
+        w = zeros(6, 6)
+        w[1, 3] = 10
+        w[1, 4] = 0.5
+        w[2, 3] = 11
+        w[2, 4] = 1
+        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
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+
+        g = complete_bipartite_graph(2, 6)
+        w = zeros(8, 8)
+        w[1, 3] = 10
+        w[1, 4] = 0.5
+        w[2, 3] = 11
+        w[2, 4] = 1
+        w[2, 5] = -1
+        w[2, 6] = -1
+        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
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+
+        g = complete_bipartite_graph(4, 2)
+        w = zeros(6, 6)
+        w[3, 5] = 10
+        w[3, 6] = 0.5
+        w[2, 5] = 11
+        w[1, 6] = 1
+        w[1, 5] = -1
+
+        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] = 10.0
+        w[1, 4] = 1.0
+        w[2, 3] = 2.0
+        w[2, 4] = 11.0
+        match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+        @test match.weight ≈ 21
+        @test match.mate[1] == 3
+        @test match.mate[3] == 1
+        @test match.mate[2] == 4
+        @test match.mate[4] == 2
+
+        g = complete_graph(3)
+        w = zeros(3, 3)
+        @test !is_bipartite(g)
+        @test_throws ErrorException maximum_weight_maximal_matching(
+            g, w, algorithm=HungarianAlgorithm()
+        )
+
+        g = complete_bipartite_graph(2, 4)
+        w = zeros(6, 6)
+        w[1, 3] = 10
+        w[1, 4] = 0.5
+        w[2, 3] = 11
+        w[2, 4] = 1
+        match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+        @test match.weight ≈ 11.5
+
+        g = Graph(4)
+        add_edge!(g, 1, 3)
+        add_edge!(g, 1, 4)
+        add_edge!(g, 2, 4)
+        w = zeros(4, 4)
+        w[1, 3] = 1
+        w[1, 4] = 3
+        w[2, 4] = 1
+        match = maximum_weight_maximal_matching(g, w; algorithm=HungarianAlgorithm())
+        @test match.weight ≈ 2
+    end
+
+    @testset "minimum_weight_perfect_matching" begin
+        wmat = [
+            0 500
+            500 0
+        ]
+        wdict = Dict(Edge(1, 2) => 500)
+        g = Graph(2)
+        add_edge!(g, 1, 2)
+        match = minimum_weight_perfect_matching(g, wmat)
+        @test match.mate[1] == 2
+        match = minimum_weight_perfect_matching(g, wdict)
+        @test match.mate[1] == 2
+
+        wmat = [
+            0 500 600 0
+            500 0 700 1000
+            600 700 0 100
+            0 1000 100 0
+        ] 
+        g = Graph([
+            Edge(1, 2),
+            Edge(1, 3),
+            Edge(2, 3),
+            Edge(3, 4),
+            Edge(2, 4)
+        ])
+        match = minimum_weight_perfect_matching(g, wmat)
+        @test match.mate[1] == 2
+        @test match.mate[2] == 1
+        @test match.mate[3] == 4
+        @test match.mate[4] == 3
+        @test match.weight ≈ 600
+
+        wdict = Dict(
+            Edge(1, 2) => 500,
+            Edge(1, 3) => 600,
+            Edge(2, 3) => 700,
+            Edge(3, 4) => 100,
+            Edge(2, 4) => 1000,
+        )
+        g = Graph(4)
+        match = minimum_weight_perfect_matching(g, wdict)
+        @test match.mate[1] == 2
+        @test match.mate[2] == 1
+        @test match.mate[3] == 4
+        @test match.mate[4] == 3
+        @test match.weight ≈ 600
+
+        wmat = [
+            0 500 400 0
+            500 0 300 1000
+            400 300 0 1000
+            0 1000 1000 0
+        ]
+        g = Graph([
+            Edge(1, 2),
+            Edge(1, 3),
+            Edge(2, 3),
+            Edge(3, 4),
+            Edge(2, 4),
+        ])
+        match = minimum_weight_perfect_matching(g, wmat)
+        @test match.mate[1] == 3
+        @test match.mate[2] == 4
+        @test match.mate[3] == 1
+        @test match.mate[4] == 2
+        @test match.weight ≈ 1400
+
+        wdict = Dict(
+            Edge(1, 2) => 500,
+            Edge(1, 3) => 400,
+            Edge(2, 3) => 300,
+            Edge(3, 4) => 1000,
+            Edge(2, 4) => 1000,
+        )
+        g = complete_graph(4)
+        match = minimum_weight_perfect_matching(g, wdict)
+        @test match.mate[1] == 3
+        @test match.mate[2] == 4
+        @test match.mate[3] == 1
+        @test match.mate[4] == 2
+        @test match.weight ≈ 1400
+
+        wmat = Matrix{Float64}([
+            0 0 -10 -0.5
+            0 0 -11 -1
+            -10 -11 0 0
+            -0.5 -1 0 0
+        ])
+        wdict= Dict{Edge,Float64}(
+            Edge(1, 3) => -10,
+            Edge(1, 4) => -0.5,
+            Edge(2, 3) => -11,
+            Edge(2, 4) => -1
+        )
+        g = complete_bipartite_graph(2, 2)
+        match = minimum_weight_perfect_matching(g, wmat)
+        @test match.mate[1] == 4
+        @test match.mate[4] == 1
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+        @test match.weight ≈ -11.5
+        match = minimum_weight_perfect_matching(g, wdict)
+        @test match.mate[1] == 4
+        @test match.mate[4] == 1
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+        @test match.weight ≈ -11.5
+
+        wmat = Matrix{Float64}([
+            0 100 10 0.5
+            100 0 11 2
+            10 11 0 0
+            0.5 2 0 0
+        ])
+        g = Graph([            
+            Edge(1, 3),
+            Edge(1, 4),
+            Edge(2, 3),
+            Edge(2, 4),
+            Edge(1, 2)
+        ])
+        match = minimum_weight_perfect_matching(g, wmat, 50)
+        @test match.mate[1] == 4
+        @test match.mate[4] == 1
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+        @test match.weight ≈ 11.5
+
+        wdict = Dict{Edge,Float64}(
+            Edge(1, 3) => 10,
+            Edge(1, 4) => 0.5,
+            Edge(2, 3) => 11,
+            Edge(2, 4) => 2,
+            Edge(1, 2) => 100
+        )
+        g = complete_graph(4)
+        match = minimum_weight_perfect_matching(g, wdict, 50)
+        @test match.mate[1] == 4
+        @test match.mate[4] == 1
+        @test match.mate[2] == 3
+        @test match.mate[3] == 2
+        @test match.weight ≈ 11.5
+    end
 end