Enable modification of objective function coefficients for DualOptimi…

…zer (#100)

Project.toml

@@ -1,7 +1,7 @@
 name = "Dualization"
 uuid = "191a621a-6537-11e9-281d-650236a99e60"
 authors = ["guilhermebodin <[email protected]>"]
-version = "0.3.3"
+version = "0.3.4"
 
 [deps]
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

src/MOI_wrapper.jl

@@ -80,7 +80,7 @@ end
 
 function MOI.supports_constraint(
     optimizer::DualOptimizer{T},
-    F::Type{<:Union{MOI.VectorOfVariables, MOI.VectorAffineFunction{T}}},
+    ::Type{<:Union{MOI.VectorOfVariables, MOI.VectorAffineFunction{T}}},
     S::Type{<:MOI.AbstractVectorSet}) where T
     D = _dual_set_type(S)
     if D === nothing
@@ -89,6 +89,21 @@ function MOI.supports_constraint(
     return MOI.supports_add_constrained_variables(optimizer.dual_problem.dual_model, D)
 end
 
+function MOI.modify(optimizer::DualOptimizer{T},
+                    ::MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}},
+                    obj_change::MOI.ScalarCoefficientChange{T}) where T
+    # We must find the constraint corresponding to the variable in the objective
+    # function and change its coefficient on the constraint.
+    ci_to_change = optimizer.dual_problem.primal_dual_map.primal_var_dual_con[obj_change.variable]
+    sense_change = MOI.get(optimizer.dual_problem.dual_model, 
+                           MOI.ObjectiveSense()) == MOI.MAX_SENSE ? one(T) : -one(T)
+    MOI.set(optimizer.dual_problem.dual_model, 
+                MOI.ConstraintSet(), 
+                ci_to_change, 
+                MOI.EqualTo(sense_change * obj_change.new_coefficient))
+    return
+end
+
 # TODO add this when constrained variables are implemented
 #function MOI.supports_add_constrained_variables(
 #    optimizer::DualOptimizer{T}, S::Type{MOI.Reals}) where T

src/dual_equality_constraints.jl

@@ -15,15 +15,14 @@ function add_dual_equality_constraints(dual_model::MOI.ModelLike, primal_model::
         all_variables, con_types, T)
 
     # get RHS from objective coeficients
-    scalar_terms = get_scalar_terms(primal_model,
-        all_variables, primal_objective)
+    scalar_terms = get_scalar_terms(primal_objective)
 
     # Add terms from objective:
     # Terms from quadratic part
-    add_scalar_affine_terms_from_quad_obj(scalar_affine_terms, primal_model,
+    add_scalar_affine_terms_from_quad_obj(scalar_affine_terms,
         primal_dual_map.primal_var_dual_quad_slack, primal_objective)
     # terms from mixing variables and parameters
-    add_scalar_affine_terms_from_quad_params(scalar_affine_terms, primal_model,
+    add_scalar_affine_terms_from_quad_params(scalar_affine_terms,
         primal_dual_map.primal_parameter, primal_objective)
 
     for primal_vi in restricted_variables
@@ -44,7 +43,6 @@ end
 
 function add_scalar_affine_terms_from_quad_obj(
     scalar_affine_terms::Dict{VI,Vector{MOI.ScalarAffineTerm{T}}},
-    primal_model::MOI.ModelLike,
     primal_var_dual_quad_slack::Dict{VI, VI},
     primal_objective::PrimalObjective{T}) where T
     for term in primal_objective.obj.quadratic_terms
@@ -68,7 +66,6 @@ end
 
 function add_scalar_affine_terms_from_quad_params(
     scalar_affine_terms::Dict{VI,Vector{MOI.ScalarAffineTerm{T}}},
-    primal_model::MOI.ModelLike,
     primal_parameter::Dict{VI, VI},
     primal_objective::PrimalObjective{T}) where T
     for (key,val) in primal_objective.quad_cross_parameters
@@ -86,9 +83,7 @@ function set_dual_constraint_name(dual_model::MOI.ModelLike, primal_model::MOI.M
     return
 end
 
-function get_scalar_terms(primal_model::MOI.ModelLike,
-    variables::Vector{VI},
-    primal_objective::PrimalObjective{T}) where T
+function get_scalar_terms(primal_objective::PrimalObjective{T}) where T
 
     scalar_terms = Dict{VI,T}()
     for term in get_affine_terms(primal_objective)
@@ -196,6 +191,6 @@ function set_dot(i::Integer, s::MOI.AbstractVectorSet, T::Type)
     vec[i] = one(T)
     return MOIU.set_dot(vec, vec, s)
 end
-function set_dot(i::Integer, s::MOI.AbstractScalarSet, T::Type)
+function set_dot(::Integer, ::MOI.AbstractScalarSet, T::Type)
     return one(T)
 end

test/Tests/test_modify.jl

@@ -0,0 +1,59 @@
+@testset "modify" begin
+    model = Model(GLPK_DUAL_FACTORY)
+    @variable(model, x[1:2] >= 0)
+    @constraint(model, 2x[1] + x[2] <= 4)
+    @constraint(model, x[1] + 2x[2] <= 4)
+    @objective(model, Max, 4x[1] + 3x[2])
+    optimize!(model)
+    @test objective_value(model) ≈ 28/3
+    set_objective_coefficient(model, x[1], 5.0)
+    optimize!(model)
+    @test objective_value(model) ≈ 10.6666666666
+
+    model = Model(GLPK_DUAL_FACTORY)
+    @variable(model, x[1:2] >= 0)
+    @constraint(model, 2x[1] + x[2] <= 4)
+    @constraint(model, x[1] + 2x[2] <= 4)
+    @objective(model, Min, -4x[1] + -3x[2])
+    optimize!(model)
+    @test objective_value(model) ≈ -28/3
+    set_objective_coefficient(model, x[1], -5.0)
+    optimize!(model)
+    @test objective_value(model) ≈ -10.6666666666
+
+    model = Model(SCS_DUAL_FACTORY)
+    @variable(model, x[1:2] >= 0)
+    @constraint(model, 2x[1] + x[2] <= 4)
+    @constraint(model, x[1] + 2x[2] <= 4)
+    @objective(model, Max, 4x[1] + 3x[2])
+    optimize!(model)
+    @test objective_value(model) ≈ 28/3 atol=1e-3
+    set_objective_coefficient(model, x[1], 5.0)
+    optimize!(model)
+    @test objective_value(model) ≈ 10.6666666666 atol=1e-3
+
+    model = Model(SCS_DUAL_FACTORY)
+    @variable(model, x[1:2] >= 0)
+    @constraint(model, 2x[1] + x[2] <= 4)
+    @constraint(model, x[1] + 2x[2] <= 4)
+    @objective(model, Min, -4x[1] + -3x[2])
+    optimize!(model)
+    @test objective_value(model) ≈ -28/3 atol=1e-3
+    set_objective_coefficient(model, x[1], -5.0)
+    optimize!(model)
+    @test objective_value(model) ≈ -10.6666666666 atol=1e-3
+
+    model = Model(SCS_DUAL_FACTORY)
+    @variable(model, x)
+    @variable(model, y)
+    @variable(model, z)
+    @constraint(model, x + 2y + 3z >= 4)
+    @constraint(model, x + y >= 1)
+    @objective(model, Min, x^2 + x*y + y^2 + y*z + z^2)
+    optimize!(model)
+    @test objective_value(model) ≈ 1.8571 atol=1e-3
+    # Now the objective is @objective(model, Min, x^2 + x*y + y^2 + y*z + z^2 - 5x)
+    set_objective_coefficient(model, x, -5.0)
+    optimize!(model)
+    @test objective_value(model) ≈ -9.15 atol=1e-3
+end

test/runtests.jl

@@ -93,3 +93,4 @@ include("Solvers/scs_test.jl")
 
 include("Tests/test_JuMP_dualize.jl")
 include("Tests/test_MOI_wrapper.jl")
+include("Tests/test_modify.jl")