Add tests

Author: odow

src/algorithms/TambyVanderpooten.jl

@@ -26,19 +26,22 @@ reformulation is solved using one of the defining points as a starting solution.
 struct TambyVanderpooten <: AbstractAlgorithm end
 
 function _update_search_region(
-    U_N::Dict{Vector{Float64}, Vector{Vector{Vector{Float64}}}}, 
-    y::Vector{Float64}, 
-    yN::Vector{Float64}, 
+    U_N::Dict{Vector{Float64},Vector{Vector{Vector{Float64}}}},
+    y::Vector{Float64},
+    yN::Vector{Float64},
 )
     p = length(y)
     bounds_to_remove = Vector{Float64}[]
-    bounds_to_add = Dict{Vector{Float64}, Vector{Vector{Vector{Float64}}}}()
+    bounds_to_add = Dict{Vector{Float64},Vector{Vector{Vector{Float64}}}}()
     for u in keys(U_N)
         if all(y .< u)
             push!(bounds_to_remove, u)
             for l in 1:p
                 u_l = _get_child(u, y, l)
-                N = [k == l ? [y] : [yi for yi in U_N[u][k] if yi[l] < y[l]] for k in 1:p]
+                N = [
+                    k == l ? [y] : [yi for yi in U_N[u][k] if yi[l] < y[l]]
+                    for k in 1:p
+                ]
                 if all(!isempty(N[k]) for k in 1:p if k != l && u_l[k] != yN[k])
                     bounds_to_add[u_l] = N
                 end
@@ -60,17 +63,20 @@ end
 
 function _get_child(u::Vector{Float64}, y::Vector{Float64}, k::Int)
     @assert length(u) == length(y)
-    return vcat(u[1:k-1], y[k], u[k+1:length(y)])
+    return vcat(u[1:(k-1)], y[k], u[(k+1):length(y)])
 end
 
 function _select_search_zone(
     U_N::Dict{Vector{Float64},Vector{Vector{Vector{Float64}}}},
-    yI::Vector{Float64}, 
-    yN::Vector{Float64}, 
+    yI::Vector{Float64},
+    yN::Vector{Float64},
 )
     upper_bounds = collect(keys(U_N))
     p = length(yI)
-    hvs = [u[k] == yN[k] ? 0. : prod(_project(u, k) .- _project(yI, k)) for k in 1:p, u in upper_bounds]
+    hvs = [
+        u[k] == yN[k] ? 0.0 : prod(_project(u, k) .- _project(yI, k)) for
+        k in 1:p, u in upper_bounds
+    ]
     k_star, j_star = argmax(hvs).I
     return k_star, upper_bounds[j_star]
 end

test/algorithms/TambyVanderpooten.jl

@@ -621,7 +621,44 @@ function test_vector_of_variables_objective()
     MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
     MOI.add_constraint(model, sum(1.0 * xi for xi in x), MOI.GreaterThan(1.0))
     MOI.optimize!(model)
-    MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+    @test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMAL
+    return
+end
+
+function test_issue_105()
+    cost = [100.0, 120.0, 150.0, 110.0, 200.0, 170.0]
+    time = [8.0, 3.0, 4.0, 2.0, 5.0, 4.0]
+    capacity = [10.0, 8.0]
+    demand = [5.0, 8.0, 5.0]
+    m, n = 2, 3
+    model = MOI.instantiate(; with_bridge_type = Float64) do
+        return MOA.Optimizer(HiGHS.Optimizer)
+    end
+    MOI.set(model, MOA.Algorithm(), MOA.TambyVanderpooten())
+    MOI.set(model, MOI.Silent(), true)
+    x = MOI.add_variables(model, m * n)
+    MOI.add_constraint.(model, x, MOI.GreaterThan(0.0))
+    MOI.add_constraint.(model, x, MOI.Integer())
+    X = reshape(x, m, n)
+    for i in 1:m
+        f_i = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, X[i, :]), 0.0)
+        MOI.add_constraint(model, f_i, MOI.LessThan(capacity[i]))
+    end
+    for j in 1:n
+        f_j = MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, X[:, j]), 0.0)
+        MOI.add_constraint(model, f_j, MOI.EqualTo(demand[j]))
+    end
+    f = MOI.Utilities.operate(
+        vcat,
+        Float64,
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(cost, x), 0.0),
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(time, x), 0.0),
+        MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.(1.0, x), 0.0),
+    )
+    MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
+    MOI.set(model, MOI.ObjectiveFunction{typeof(f)}(), f)
+    MOI.optimize!(model)
+    @test MOI.get(model, MOI.ResultCount()) == 6
     return
 end