Merge pull request #85 from StructJuMP/kkim

Update JuMP to v1.0.0 and others

Project.toml

@@ -1,7 +1,7 @@
 name = "StructJuMP"
 uuid = "34f15cae-5318-50c9-93d3-9feadd34e321"
 repo = "https://github.com/StructJuMP/StructJuMP.jl.git"
-version = "0.2.0"
+version = "0.3.0"
 
 [deps]
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
@@ -10,10 +10,10 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 ParameterJuMP = "774612a8-9878-5177-865a-ca53ae2495f9"
 
 [compat]
-JuMP = "0.21.3"
-MathOptInterface = "0.9.14"
-ParameterJuMP = "0.2"
-julia = "1"
+JuMP = "0.23, 1"
+MathOptInterface = "1"
+ParameterJuMP = "0.4.1"
+julia = "1.6"
 
 [extras]
 ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"

examples/dcap.jl

@@ -0,0 +1,47 @@
+using StructJuMP
+using Random
+
+function DCAP(nR::Int, nN::Int, nT::Int, nS::Int, seed::Int=1)::StructuredModel
+
+    # set random seed (default=1)
+    Random.seed!(seed)
+
+    # generate & store instance data
+    ## sets
+    R = 1:nR
+    N = 1:nN
+    T = 1:nT
+    S = 1:nS
+
+    ## parameters
+    a = rand(nR, nT) * 5 .+ 5
+    b = rand(nR, nT) * 40 .+ 10
+    c = rand(nR, nN, nT, nS) * 5 .+ 5
+    c0 = rand(nN, nT, nS) * 500 .+ 500
+    d = rand(nN, nT, nS) .+ 0.5
+    Pr = ones(nS)/nS
+
+    # construct JuMP.Model
+    model = StructuredModel(num_scenarios = nS)
+
+    ## 1st stage
+    @variable(model, x[i=R,t=T] >= 0)
+    @variable(model, u[i=R,t=T], Bin)
+    @objective(model, Min, sum(a[i,t]*x[i,t] + b[i,t]*u[i,t] for i in R for t in T))
+    @constraint(model, [i=R,t=T], x[i,t] - u[i,t] <= 0)
+
+    ## 2nd stage
+    for s in S
+        sb = StructuredModel(parent=model, id = s, prob = Pr[s])
+        # @variable(sb, y[i=R, j=N, t=T], Bin)
+        @variable(sb, 0 <= y[i=R, j=N, t=T] <= 1)
+        #@variable(sb, z[j=N,t=T] >= 0) # originally implemented variable (continuous)
+        # @variable(sb, z[j=N,t=T], Bin)  # modify as SIPLIB 1.0
+        @variable(sb, 0 <= z[j=N,t=T] <= 1)
+        @objective(sb, Min, sum(c[i,j,t,s]*y[i,j,t] for i in R for j in N for t in T) + sum(c0[j,t,s]*z[j,t] for j in N for t in T))
+        @constraint(sb, [i=R, t=T], -sum(x[i,tau] for tau in 1:t) + sum(d[j,t,s]*y[i,j,t] for j in N) <= 0)
+        @constraint(sb, [j=N, t=T], sum(y[i,j,t] for i in R) + z[j,t] == 1)
+    end
+
+    return model
+end

examples/parmodel1.jl

@@ -6,7 +6,7 @@ firststage = StructuredModel()
 @constraint(firststage, x[1] * x[2] == 10)
 
 for scen in 1:2
-    bl = StructuredModel(parent=firststage, id=scen)
+    local bl = StructuredModel(parent=firststage, id=scen)
     @variable(bl, y)
     @constraint(bl, x[2]^2 + x[1]*y ≤ 5)
     @objective(bl, Min, (x[1]+x[2])*y)

examples/parmodel2.jl

@@ -6,7 +6,7 @@ firststage = StructuredModel()
 @objective(firststage, Min, x[1]^2 + x[2]^2)
 
 for scen in 1:2
-    bl = StructuredModel(parent=firststage, id=scen)
+    local bl = StructuredModel(parent=firststage, id=scen)
     @variable(bl, y[1:2])
     @constraint(bl, x[3-scen] + sum(y) ≥  0)
     @constraint(bl, x[3-scen] + sum(y) ≤ 50)

examples/parmodel3.jl

@@ -6,7 +6,7 @@ firststage = StructuredModel()
 @objective(firststage, Min, x[1]^2 + x[2]^2 + x[1]*x[2])
 
 for scen in 1:2
-    bl = StructuredModel(parent=firststage, id=scen)
+    local bl = StructuredModel(parent=firststage, id=scen)
     @variable(bl, y[1:2])
     @constraint(bl, x[3-scen] + sum(y) ≥  0)
     @constraint(bl, x[3-scen] + sum(y) ≤ 50)

examples/parmodel4.jl

@@ -6,7 +6,7 @@ firststage = StructuredModel()
 @objective(firststage, Min, x[1]^2 + x[2]^2 + x[1]*x[2])
 
 for scen in 1:100
-    bl = StructuredModel(parent=firststage, id=scen)
+    local bl = StructuredModel(parent=firststage, id=scen)
     @variable(bl, y[1:2])
     idx = (isodd(scen) ? 1 : 2)
     @constraint(bl, x[idx] + sum(y) ≥  0)

examples/transportation.jl

@@ -40,7 +40,7 @@ for i in factories
 end
 
 for (s, elem) in enumerate(scenarios)
-    bl = StructuredModel(parent=m, id=s)
+    local bl = StructuredModel(parent=m, id=s)
     @variable(bl, 0 <= salesw[i=centers] <= demand[i,s])
     @variable(bl, wastew[centers] >= 0)
     @objective(bl, Max, sum(price*prob[s]*salesw[j] for j=centers) - sum(wastecost*prob[s]*wastew[j] for j=centers))

src/BendersBridge.jl

@@ -82,11 +82,7 @@ struct ParametrizedModel
     θ::Dict{Int, JuMP.VariableRef}
 end
 function ParametrizedModel(structured_model::StructuredModel, args...; kwargs...)
-    if structured_model.parent === nothing
-        model = Model(args...; kwargs...)
-    else
-        model = ModelWithParams(args...; kwargs...)
-    end
+    model = Model(args...; kwargs...)
     variable_map = Dict{Int, JuMP.VariableRef}()
     for (index, var) in structured_model.variables
         name = structured_model.varnames[index]

src/Benders_pmap.jl

@@ -47,7 +47,7 @@ function set_parent_solution!(model::ParametrizedModel, parent::ParametrizedMode
     for (index, parameter) in model.parameter_map
         vref = parent.variable_map[index]
         value = parent_solution.variable_value[vref]
-        JuMP.fix(parameter, value)
+        JuMP.set_value(parameter, value)
     end
 end
 
@@ -66,7 +66,7 @@ function add_cutting_planes(master_model, master_solution, sub_models, sub_solut
         if sol.feasible
             JuMP.add_to_expression!(aff, -1.0, master_model.θ[id])
             # Check if the cut is useful
-            if JuMP.value(aff, vref -> master_solution.variable_value[vref]) - TOL < 0
+            if JuMP.value(vref -> master_solution.variable_value[vref], aff) - TOL < 0
                 continue
             end
         else

src/StructJuMP.jl

@@ -88,7 +88,6 @@ Base.copy(v::StructuredVariableRef) = v
 Base.:(==)(v::StructuredVariableRef, w::StructuredVariableRef) = v.model === w.model && v.idx == w.idx
 JuMP.owner_model(v::StructuredVariableRef) = v.model
 JuMP.isequal_canonical(v::StructuredVariableRef, w::StructuredVariableRef) = v == w
-JuMP.variable_type(::StructuredModel) = StructuredVariableRef
 function JuMP.add_variable(m::StructuredModel, v::JuMP.AbstractVariable, name::String="")
     m.nextvaridx += 1
     vref = StructuredVariableRef(m, m.nextvaridx)
@@ -231,7 +230,6 @@ struct StructuredConstraintRef
     model::StructuredModel # `model` owning the constraint
     idx::Int       # Index in `model.constraints`
 end
-JuMP.constraint_type(::StructuredModel) = StructuredConstraintRef
 function JuMP.add_constraint(m::StructuredModel, c::JuMP.AbstractConstraint, name::String="")
     m.nextconidx += 1
     cref = StructuredConstraintRef(m, m.nextconidx)

test/printhook.jl

@@ -27,16 +27,10 @@ Subject to
  x + y = 1.0
 """
     @test sprint(show, parent) == """
-A JuMP Model
+An Abstract JuMP Model
 Minimization problem with:
 Variables: 2
-Objective function type: GenericQuadExpr{Float64,StructJuMP.StructuredVariableRef}
+Objective function type: GenericQuadExpr{Float64, StructJuMP.StructuredVariableRef}
 Constraint: 1
 Names registered in the model: x, y"""
-    @test sprint(show, "text/latex", parent) == """
-\$\$ \\begin{alignat*}{1}\\min\\quad & x^2 + y\\\\
-\\text{Subject to} \\quad & x + y = 1.0\\\\
-\\end{alignat*}
- \$\$"""
-    #@test occursin("Child", str)
 end