Merge 61bba93 into b6f7de8

src/JuMP.jl

@@ -317,60 +317,6 @@ function num_nl_constraints(model::Model)
     return model.nlp_data !== nothing ? length(model.nlp_data.nlconstr) : 0
 end
 
-"""
-    objective_bound(model::Model)
-
-Return the best known bound on the optimal objective value after a call to
-`optimize!model)`.
-"""
-objective_bound(model::Model) = MOI.get(model, MOI.ObjectiveBound())
-
-"""
-    objective_value(model::Model)
-
-Return the objective value after a call to `optimize!model)`.
-"""
-objective_value(model::Model) = MOI.get(model, MOI.ObjectiveValue())
-
-"""
-    objective_sense(model::Model)::MathOptInterface.OptimizationSense
-
-Return the objective sense.
-"""
-function objective_sense(model::Model)
-    return MOI.get(model, MOI.ObjectiveSense())
-end
-
-"""
-    set_objective_sense(model::Model, sense::MathOptInterface.OptimizationSense)
-
-Sets the objective sense of the model to the given sense. See
-[`set_objective_function`](@ref) to set the objective function. These are
-low-level functions; the recommended way to set the objective is with the
-[`@objective`](@ref) macro.
-"""
-function set_objective_sense(model::Model, sense::MOI.OptimizationSense)
-    MOI.set(model, MOI.ObjectiveSense(), sense)
-end
-
-"""
-    set_objective_function(model::Model,
-                           func::MathOptInterface.AbstractScalarFunction)
-
-Sets the objective function of the model to the given function. See
-[`set_objective_sense`](@ref) to set the objective sense. These are low-level
-functions; the recommended way to set the objective is with the
-[`@objective`](@ref) macro.
-"""
-function set_objective_function(model::Model, func::MOI.AbstractScalarFunction)
-    attr = MOI.ObjectiveFunction{typeof(func)}()
-    if !MOI.supports(model.moi_backend, attr)
-        error("The solver does not support an objective function of type ",
-              typeof(func), ".")
-    end
-    MOI.set(model, attr, func)
-end
-
 # TODO(IainNZ): Document these too.
 object_dictionary(model::Model) = model.obj_dict
 termination_status(model::Model) = MOI.get(model, MOI.TerminationStatus())
@@ -416,6 +362,7 @@ end
 
 include("constraints.jl")
 include("variables.jl")
+include("objective.jl")
 
 Base.zero(::Type{V}) where V<:AbstractVariableRef = zero(GenericAffExpr{Float64, V})
 Base.zero(v::AbstractVariableRef) = zero(typeof(v))

src/aff_expr.jl

@@ -295,14 +295,6 @@ function MOI.VectorAffineFunction(affs::Vector{AffExpr})
 end
 moi_function(a::Vector{<:GenericAffExpr}) = MOI.VectorAffineFunction(a)
 
-function set_objective(model::Model, sense::MOI.OptimizationSense, a::AffExpr)
-    set_objective_sense(model, sense)
-    set_objective_function(model, MOI.ScalarAffineFunction(a))
-    # Keeping the explicit `return` is helpful for type inference because we
-    # don't know what `MOI.set` will return.
-    return
-end
-
 """
     objective_function(m::Model, ::Type{AffExpr})
 

src/objective.jl

@@ -0,0 +1,80 @@
+#  Copyright 2017, Iain Dunning, Joey Huchette, Miles Lubin, and contributors
+#  This Source Code Form is subject to the terms of the Mozilla Public
+#  License, v. 2.0. If a copy of the MPL was not distributed with this
+#  file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#############################################################################
+# JuMP
+# An algebraic modeling language for Julia
+# See http://github.com/JuliaOpt/JuMP.jl
+#############################################################################
+# This file contains objective-related functions
+
+"""
+    objective_bound(model::Model)
+
+Return the best known bound on the optimal objective value after a call to
+`optimize!model)`.
+"""
+objective_bound(model::Model) = MOI.get(model, MOI.ObjectiveBound())
+
+"""
+    objective_value(model::Model)
+
+Return the objective value after a call to `optimize!model)`.
+"""
+objective_value(model::Model) = MOI.get(model, MOI.ObjectiveValue())
+
+"""
+    objective_sense(model::Model)::MathOptInterface.OptimizationSense
+
+Return the objective sense.
+"""
+function objective_sense(model::Model)
+    return MOI.get(model, MOI.ObjectiveSense())
+end
+
+"""
+    set_objective_sense(model::Model, sense::MathOptInterface.OptimizationSense)
+
+Sets the objective sense of the model to the given sense. See
+[`set_objective_function`](@ref) to set the objective function. These are
+low-level functions; the recommended way to set the objective is with the
+[`@objective`](@ref) macro.
+"""
+function set_objective_sense(model::Model, sense::MOI.OptimizationSense)
+    MOI.set(model, MOI.ObjectiveSense(), sense)
+end
+
+"""
+    set_objective_function(model::Model,
+                           func::Union{AbstractJuMPScalar,
+                                       MathOptInterface.AbstractScalarFunction})
+
+Sets the objective function of the model to the given function. See
+[`set_objective_sense`](@ref) to set the objective sense. These are low-level
+functions; the recommended way to set the objective is with the
+[`@objective`](@ref) macro.
+"""
+function set_objective_function end
+
+function set_objective_function(model::Model, func::MOI.AbstractScalarFunction)
+    attr = MOI.ObjectiveFunction{typeof(func)}()
+    if !MOI.supports(model.moi_backend, attr)
+        error("The solver does not support an objective function of type ",
+              typeof(func), ".")
+    end
+    MOI.set(model, attr, func)
+    # Keeping the explicit `return` is helpful for type inference because we
+    # don't know what `MOI.set` will return.
+    return
+end
+
+function set_objective_function(model::Model, func::AbstractJuMPScalar)
+    set_objective_function(model, moi_function(func))
+end
+
+function set_objective(model::Model, sense::MOI.OptimizationSense,
+                       func::AbstractJuMPScalar)
+    set_objective_sense(model, sense)
+    set_objective_function(model, func)
+end

src/quad_expr.jl

@@ -221,14 +221,6 @@ function jump_function(model::AbstractModel, aff::MOI.ScalarQuadraticFunction)
     return QuadExpr(model, aff)
 end
 
-function set_objective(model::Model, sense::MOI.OptimizationSense, a::QuadExpr)
-    set_objective_sense(model, sense)
-    set_objective_function(model, MOI.ScalarQuadraticFunction(a))
-    # Keeping the explicit `return` is helpful for type inference because we
-    # don't know what `MOI.set` will return.
-    return
-end
-
 """
     objective_function(m::Model, ::Type{QuadExpr})
 

src/variables.jl

@@ -244,15 +244,6 @@ function jump_function(model::AbstractModel, variable::MOI.SingleVariable)
     return VariableRef(model, variable)
 end
 
-function set_objective(model::Model, sense::MOI.OptimizationSense,
-                       x::VariableRef)
-    set_objective_sense(model, sense)
-    set_objective_function(model, MOI.SingleVariable(x))
-    # Keeping the explicit `return` is helpful for type inference because we
-    # don't know what `MOI.set` will return.
-    return
-end
-
 """
     objective_function(m::Model, ::Type{VariableRef})