JuMP.jl

#  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
#############################################################################

VERSION < v"0.7.0-beta2.199" && __precompile__()

module JuMP

using Compat
using Compat.LinearAlgebra
using Compat.SparseArrays

import MathOptInterface
const MOI = MathOptInterface
const MOIU = MOI.Utilities

import Calculus
import DataStructures.OrderedDict
import ForwardDiff
include("Derivatives/Derivatives.jl")
using .Derivatives

export
    Model, VariableRef, AffExpr, QuadExpr,
    with_optimizer,
    NonlinearConstraint,
    ConstraintRef,
    SecondOrderCone, RotatedSecondOrderCone, PSDCone,
    optimize,
    setname,
    setlowerbound, setupperbound,
    setstartvalue,
    linearterms,

# Macros and support functions
    @LinearConstraint, @LinearConstraints, @QuadConstraint, @QuadConstraints,
    @SOCConstraint, @SOCConstraints,
    @expression, @expressions, @NLexpression, @NLexpressions,
    @variable, @variables, @constraint, @constraints,
    @NLconstraint, @NLconstraints,
    @SDconstraint, @SDconstraints,
    @objective, @NLobjective,
    @NLparameter, @constraintref


include("utils.jl")

const MOIVAR = MOI.VariableIndex
const MOICON{F,S} = MOI.ConstraintIndex{F,S}
const MOILB = MOICON{MOI.SingleVariable,MOI.GreaterThan{Float64}}
const MOIUB = MOICON{MOI.SingleVariable,MOI.LessThan{Float64}}
const MOIFIX = MOICON{MOI.SingleVariable,MOI.EqualTo{Float64}}
const MOIINT = MOICON{MOI.SingleVariable,MOI.Integer}
const MOIBIN = MOICON{MOI.SingleVariable,MOI.ZeroOne}

@MOIU.model JuMPMOIModel (ZeroOne, Integer) (EqualTo, GreaterThan, LessThan, Interval) (Zeros, Nonnegatives, Nonpositives, SecondOrderCone, RotatedSecondOrderCone, GeometricMeanCone, PositiveSemidefiniteConeTriangle, PositiveSemidefiniteConeSquare, RootDetConeTriangle, RootDetConeSquare, LogDetConeTriangle, LogDetConeSquare) () (SingleVariable,) (ScalarAffineFunction,ScalarQuadraticFunction) (VectorOfVariables,) (VectorAffineFunction,)

"""
    OptimizerFactory

User-friendly closure that creates new MOI models. New `OptimizerFactory`s are
created with [`with_optimizer`](@ref) and new models are created from the
optimizer factory `optimizer_factory` with `optimizer_factory()`.

## Examples

The following construct an optimizer factory and then use it to create two
independent `IpoptOptimizer`s:
```julia
optimizer_factory = with_optimizer(IpoptOptimizer, print_level=0)
optimizer1 = optimizer_factory()
optimizer2 = optimizer_factory()
```
"""
struct OptimizerFactory
    # The constructor can be
    # * `Function`: a function, or
    # * `DataType`: a type, or
    # * `UnionAll`: a type with missing parameters.
    constructor
    args::Tuple
    kwargs # type changes from Julia v0.6 to v0.7 so we leave it untyped for now
end

"""
    with_optimizer(constructor, args...; kwargs...)

Return an `OptimizerFactory` that creates optimizers using the constructor
`constructor` with positional arguments `args` and keyword arguments `kwargs`.

## Examples

The following returns an optimizer factory that creates `IpoptOptimizer`s using
the constructor call `IpoptOptimizer(print_level=0)`:
```julia
with_optimizer(IpoptOptimizer, print_level=0)
```
"""
function with_optimizer(constructor,
                        args...; kwargs...)
    if !applicable(constructor, args...)
        error("$constructor is does not have any method with arguments $args.",
              "The first argument of `with_optimizer` should be callable with",
              " the other argument of `with_optimizer`.")
    end
    return OptimizerFactory(constructor, args, kwargs)
end

function (optimizer_factory::OptimizerFactory)()
    return optimizer_factory.constructor(optimizer_factory.args...;
                                         optimizer_factory.kwargs...)
end

###############################################################################
# Model

# Model has three modes:
# 1) Automatic: moi_backend field holds a LazyBridgeOptimizer{CachingOptimizer} in Automatic mode.
# 2) Manual: moi_backend field holds a LazyBridgeOptimizer{CachingOptimizer} in Manual mode.
# 3) Direct: moi_backend field holds an AbstractOptimizer. No extra copy of the model is stored. The moi_backend must support addconstraint! etc.
# Methods to interact with the CachingOptimizer are defined in solverinterface.jl.
@enum ModelMode Automatic Manual Direct

abstract type AbstractModel end
# All `AbstractModel`s must define methods for these functions:
# num_variables, object_dictionary

"""
    Model

A mathematical model of an optimization problem.
"""
mutable struct Model <: AbstractModel
    # Special variablewise properties that we keep track of:
    # lower bound, upper bound, fixed, integrality, binary
    variable_to_lower_bound::Dict{MOIVAR, MOILB}
    variable_to_upper_bound::Dict{MOIVAR, MOIUB}
    variable_to_fix::Dict{MOIVAR, MOIFIX}
    variable_to_integrality::Dict{MOIVAR, MOIINT}
    variable_to_zero_one::Dict{MOIVAR, MOIBIN}
    # In Manual and Automatic modes, LazyBridgeOptimizer{CachingOptimizer}.
    # In Direct mode, will hold an AbstractOptimizer.
    moi_backend::MOI.AbstractOptimizer
    # Hook into a solve call...function of the form f(m::Model; kwargs...),
    # where kwargs get passed along to subsequent solve calls.
    optimize_hook
    # TODO: Document.
    nlp_data
    # Dictionary from variable and constraint names to objects.
    obj_dict::Dict{Symbol, Any}
    # Number of times we add large expressions. Incremented and checked by
    # the `operator_warn` method.
    operator_counter::Int
    # Enable extensions to attach arbitrary information to a JuMP model by
    # using an extension-specific symbol as a key.
    ext::Dict{Symbol, Any}
end

"""
    Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
            bridge_constraints::Bool=true)

Return a new JuMP model without any optimizer; the model is stored the model in
a cache. The mode of the `CachingOptimizer` storing this cache is
`caching_mode`. The optimizer can be set later in the [`JuMP.optimize`](@ref)
call. If `bridge_constraints` is true, constraints that are not supported by the
optimizer are automatically bridged to equivalent supported constraints when
an appropriate is defined in the `MathOptInterface.Bridges` module or is
defined in another module and is explicitely added.
"""
function Model(; caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
                 bridge_constraints::Bool=true)
    universal_fallback = MOIU.UniversalFallback(JuMPMOIModel{Float64}())
    caching_opt = MOIU.CachingOptimizer(universal_fallback,
                                        caching_mode)
    if bridge_constraints
        backend = MOI.Bridges.fullbridgeoptimizer(caching_opt,
                                                  Float64)
    else
        backend = caching_opt
    end
    return direct_model(backend)
end

"""
    Model(optimizer_factory::OptimizerFactory;
          caching_mode::MOIU.CachingOptimizerMode=MOIU.Automatic,
          bridge_constraints::Bool=true)

Return a new JuMP model using the optimizer factory `optimizer_factory` to
create the optimizer. The optimizer factory can be created by the
[`with_optimizer`](@ref) function.

## Examples

The following creates a model using the optimizer
`IpoptOptimizer(print_level=0)`:
```julia
model = JuMP.Model(with_optimizer(IpoptOptimizer, print_level=0))
```
"""
function Model(optimizer_factory::OptimizerFactory; kwargs...)
    model = Model(; kwargs...)
    optimizer = optimizer_factory()
    MOIU.resetoptimizer!(model, optimizer)
    return model
end

"""
    direct_model(backend::MOI.ModelLike)

Return a new JuMP model using `backend` to store the model and solve it. As
opposed to the [`Model`](@ref) constructor, no cache of the model is stored
outside of `backend` and no bridges are automatically applied to `backend`.
The absence of cache reduces the memory footprint but it is important to bear
in mind the following implications of creating models using this *direct* mode:

* When `backend` does not support an operation such as adding
  variables/constraints after solver or modifying constraints, an error is
  thrown. With models created using the [`Model`](@ref) constructor, such
  situations can be dealt with by storing the modifications in a cache and
  loading them into the optimizer when `JuMP.optimize` is called.
* No constraint bridging is supported by default.
* The optimizer used cannot be changed the model is constructed.
* The model created cannot be copied.
"""
function direct_model(backend::MOI.ModelLike)
    @assert MOI.isempty(backend)
    return Model(Dict{MOIVAR, MOILB}(),
                 Dict{MOIVAR, MOIUB}(),
                 Dict{MOIVAR, MOIFIX}(),
                 Dict{MOIVAR, MOIINT}(),
                 Dict{MOIVAR, MOIBIN}(),
                 backend,
                 nothing,
                 nothing,
                 Dict{Symbol, Any}(),
                 0,
                 Dict{Symbol, Any}())
end

if VERSION >= v"0.7-"
    Base.broadcastable(model::Model) = Ref(model)
end


# In Automatic and Manual mode, `model.moi_backend` is either directly the
# `CachingOptimizer` if `bridge_constraints=false` was passed in the constructor
# or it is a `LazyBridgeOptimizer` and the `CachingOptimizer` is stored in the
# `model` field
function caching_optimizer(model::Model)
    if model.moi_backend isa MOIU.CachingOptimizer
        return model.moi_backend
    elseif (model.moi_backend isa
            MOI.Bridges.LazyBridgeOptimizer{<:MOIU.CachingOptimizer})
        return model.moi_backend.model
    else
        error("The function `caching_optimizer` cannot be called on a model " *
              "in `Direct` mode.")
    end
end

"""
    mode(model::Model)

Return mode (Direct, Automatic, Manual) of model.
"""
function mode(model::Model)
    if !(model.moi_backend isa MOI.Bridges.LazyBridgeOptimizer{<:MOIU.CachingOptimizer} ||
         model.moi_backend isa MOIU.CachingOptimizer)
        return Direct
    elseif caching_optimizer(model).mode == MOIU.Automatic
        return Automatic
    else
        return Manual
    end
end

"""
    num_variables(model::Model)

Returns number of variables in `model`.
"""
num_variables(model::Model) = MOI.get(model, MOI.NumberOfVariables())

"""
    numnlconstr(model::Model)

Returns the number of nonlinear constraints associated with the `model`.
"""
function numnlconstr(model::Model)
    return model.nlp_data !== nothing ? length(model.nlp_data.nlconstr) : 0
end

"""
    objectivebound(model::Model)

Return the best known bound on the optimal objective value after a call to
`optimize(model)`.
"""
objectivebound(model::Model) = MOI.get(model, MOI.ObjectiveBound())

"""
    objectivevalue(model::Model)

Return the objective value after a call to `optimize(model)`.
"""
objectivevalue(model::Model) = MOI.get(model, MOI.ObjectiveValue())

"""
    objectivesense(model::Model)

Return the objective sense, `:Min`, `:Max`, or `:Feasibility`.
"""
function objectivesense(model::Model)
    moisense = MOI.get(model, MOI.ObjectiveSense())
    if moisense == MOI.MinSense
        return :Min
    elseif moisense == MOI.MaxSense
        return :Max
    else
        @assert moisense == MOI.FeasibilitySense
        return :Feasibility
    end
end

# TODO(IainNZ): Document these too.
# TODO(#1381): Implement Base.copy for Model.
object_dictionary(model::Model) = model.obj_dict
terminationstatus(m::Model) = MOI.get(m, MOI.TerminationStatus())
primalstatus(m::Model) = MOI.get(m, MOI.PrimalStatus())
dualstatus(m::Model) = MOI.get(m, MOI.DualStatus())
set_optimize_hook(m::Model, f) = (m.optimize_hook = f)


#############################################################################
# AbstractConstraint
# Abstract base type for all constraint types
abstract type AbstractConstraint end
# Abstract base type for all scalar types
abstract type AbstractJuMPScalar end


@static if VERSION >= v"0.7-"
    # These are required to create symmetric containers of AbstractJuMPScalars.
    Compat.LinearAlgebra.symmetric_type(::Type{T}) where T <: AbstractJuMPScalar = T
    Compat.LinearAlgebra.symmetric(scalar::AbstractJuMPScalar, ::Symbol) = scalar
    # This is required for linear algebra operations involving transposes.
    Compat.LinearAlgebra.adjoint(scalar::AbstractJuMPScalar) = scalar
end

"""
    owner_model(s::AbstractJuMPScalar)

Return the model owning the scalar `s`.
"""
function owner_model end

if VERSION < v"0.7-"
    Base.start(::AbstractJuMPScalar) = false
    Base.next(x::AbstractJuMPScalar, state) = (x, true)
    Base.done(::AbstractJuMPScalar, state) = state
else
    Base.iterate(x::AbstractJuMPScalar) = (x, true)
    Base.iterate(::AbstractJuMPScalar, state) = nothing
end
Base.isempty(::AbstractJuMPScalar) = false

# Check if two arrays of AbstractJuMPScalars are equal. Useful for testing.
function isequal_canonical(x::AbstractArray{<:JuMP.AbstractJuMPScalar},
                           y::AbstractArray{<:JuMP.AbstractJuMPScalar})
    return size(x) == size(y) && all(JuMP.isequal_canonical.(x, y))
end

"""
    AbstractShape

Abstract vectorizable shape. Given a flat vector form of an object of shape
`shape`, the original object can be obtained by [`reshape`](@ref).
"""
abstract type AbstractShape end

"""
    dual_shape(shape::AbstractShape)::AbstractShape

Returns the shape of the dual space of the space of objects of shape `shape`. By
default, the `dual_shape` of a shape is itself. See the examples section below
for an example for which this is not the case.

## Examples

Consider polynomial constraints for which the dual is moment constraints and
moment constraints for which the dual is polynomial constraints. Shapes for
polynomials can be defined as follows:
```julia
struct Polynomial
    coefficients::Vector{Float64}
    monomials::Vector{Monomial}
end
struct PolynomialShape <: JuMP.AbstractShape
    monomials::Vector{Monomial}
end
JuMP.reshape(x::Vector, shape::PolynomialShape) = Polynomial(x, shape.monomials)
```
and a shape for moments can be defined as follows:
```julia
struct Moments
    coefficients::Vector{Float64}
    monomials::Vector{Monomial}
end
struct MomentsShape <: JuMP.AbstractShape
    monomials::Vector{Monomial}
end
JuMP.reshape(x::Vector, shape::MomentsShape) = Moments(x, shape.monomials)
```
The `dual_shape` allows to define the shape of the dual of polynomial and moment
constraints:
```julia
dual_shape(shape::PolynomialShape) = MomentsShape(shape.monomials)
dual_shape(shape::MomentsShape) = PolynomialShape(shape.monomials)
```
"""
dual_shape(shape::AbstractShape) = shape

"""
    reshape(vectorized_form::Vector, shape::AbstractShape)

Return an object in it original shape `shape` given its vectorized form
`vectorized_form`.

## Examples

Given a [`SymmetricMatrixShape`](@ref) of vectorized form `[1, 2, 3]`, the
following code retrieve the matrix `Symmetric(Matrix[1 2; 2 3])`:
```julia
reshape([1, 2, 3], SymmetricMatrixShape(2))
```
"""
function reshape end

"""
    shape(c::AbstractConstraint)::AbstractShape

Return the shape of the constraint `c`.
"""
function shape end

"""
    ScalarShape

Shape of scalar constraints.
"""
struct ScalarShape <: AbstractShape end
reshape(α, ::ScalarShape) = α

"""
    VectorShape

Vector for which the vectorized form corresponds exactly to the vector given.
"""
struct VectorShape <: AbstractShape end
reshape(vectorized_form, ::VectorShape) = vectorized_form

##########################################################################
# Constraint
# Holds the index of a constraint in a Model.
# TODO: Rename "m" field (breaks style guidelines).
struct ConstraintRef{M <: AbstractModel, C, Shape <: AbstractShape}
    m::M
    index::C
    shape::Shape
end

if VERSION >= v"0.7-"
    Base.broadcastable(cref::ConstraintRef) = Ref(cref)
end

# TODO: should model be a parameter here?
function MOI.delete!(m::Model, cr::ConstraintRef{Model})
    @assert m === cr.m
    MOI.delete!(m.moi_backend, index(cr))
end

MOI.isvalid(m::Model, cr::ConstraintRef{Model}) = cr.m === m && MOI.isvalid(m.moi_backend, cr.index)

"""
    addconstraint(m::Model, c::AbstractConstraint, name::String="")

Add a constraint `c` to `Model m` and sets its name.
"""
function addconstraint(m::Model, c::AbstractConstraint, name::String="")
    f, s = moi_function_and_set(c)
    if !MOI.supportsconstraint(m.moi_backend, typeof(f), typeof(s))
        if m.moi_backend isa MOI.Bridges.LazyBridgeOptimizer
            bridge_message = " and there are no bridges that can reformulate it into supported constraints."
        else
            bridge_message = ", try using `bridge_constraints=true` in the `JuMP.Model` constructor if you believe the constraint can be reformulated to constraints supported by the solver."
        end
        error("Constraints of type $(typeof(f))-in-$(typeof(s)) are not supported by the solver" * bridge_message)
    end
    cindex = MOI.addconstraint!(m.moi_backend, f, s)
    cref = ConstraintRef(m, cindex, shape(c))
    if !isempty(name)
        setname(cref, name)
    end
    return cref
end

include("variables.jl")

Base.zero(::Type{V}) where V<:AbstractVariableRef = zero(GenericAffExpr{Float64, V})
Base.zero(v::AbstractVariableRef) = zero(typeof(v))
Base.one(::Type{V}) where V<:AbstractVariableRef = one(GenericAffExpr{Float64, V})
Base.one(v::AbstractVariableRef) = one(typeof(v))

mutable struct VariableNotOwnedError <: Exception
    context::String
end
function Base.showerror(io::IO, ex::VariableNotOwnedError)
    print(io, "VariableNotOwnedError: Variable not owned by model present in $(ex.context)")
end

function verify_ownership(m::Model, vec::Vector{VariableRef})
    n = length(vec)
    @inbounds for i in 1:n
        vec[i].m !== m && return false
    end
    return true
end

Base.copy(v::VariableRef, new_model::Model) = VariableRef(new_model, v.index)
Base.copy(x::Nothing, new_model::Model) = nothing
# TODO: Replace with vectorized copy?
Base.copy(v::AbstractArray{VariableRef}, new_model::Model) = (var -> VariableRef(new_model, var.index)).(v)

function optimizerindex(v::VariableRef)
    if mode(v.m) == Direct
        return index(v)
    else
        @assert caching_optimizer(v.m).state == MOIU.AttachedOptimizer
        return caching_optimizer(v.m).model_to_optimizer_map[index(v)]
    end
end

function optimizerindex(cr::ConstraintRef{Model})
    if mode(cr.m) == Direct
        return index(cr)
    else
        @assert caching_optimizer(cr.m).state == MOIU.AttachedOptimizer
        return caching_optimizer(cr.m).model_to_optimizer_map[index(cr)]
    end
end

index(cr::ConstraintRef) = cr.index

function hasresultdual(m::Model, REF::Type{<:ConstraintRef{Model, T}}) where {T <: MOICON}
    MOI.canget(m, MOI.ConstraintDual(), REF)
end

"""
    resultdual(cr::ConstraintRef)

Get the dual value of this constraint in the result returned by a solver.
Use `hasresultdual` to check if a result exists before asking for values.
Replaces `getdual` for most use cases.
"""
function resultdual(cr::ConstraintRef{Model, <:MOICON})
    reshape(MOI.get(cr.m, MOI.ConstraintDual(), cr), dual_shape(cr.shape))
end

"""
    name(v::ConstraintRef)

Get a constraint's name.
"""
name(cr::ConstraintRef{Model,<:MOICON}) = MOI.get(cr.m, MOI.ConstraintName(), cr)

setname(cr::ConstraintRef{Model,<:MOICON}, s::String) = MOI.set!(cr.m, MOI.ConstraintName(), cr, s)

"""
    canget(m::JuMP.Model, attr::MathOptInterface.AbstractModelAttribute)::Bool

Return `true` if one may query the attribute `attr` from the model's MOI backend.
false if not.
"""
MOI.canget(m::Model, attr::MOI.AbstractModelAttribute) = MOI.canget(m.moi_backend, attr)
MOI.canget(m::Model, attr::MOI.AbstractVariableAttribute, ::Type{VariableRef}) = MOI.canget(m.moi_backend, attr, MOIVAR)
function MOI.canget(model::Model, attr::MOI.AbstractConstraintAttribute,
                    ::Type{<:ConstraintRef{Model, T}}) where {T <: MOICON}
    return MOI.canget(model.moi_backend, attr, T)
end

"""
    get(m::JuMP.Model, attr::MathOptInterface.AbstractModelAttribute)

Return the value of the attribute `attr` from model's MOI backend.
"""
MOI.get(m::Model, attr::MOI.AbstractModelAttribute) = MOI.get(m.moi_backend, attr)
function MOI.get(m::Model, attr::MOI.AbstractVariableAttribute, v::VariableRef)
    @assert m === v.m
    MOI.get(m.moi_backend, attr, index(v))
end
function MOI.get(m::Model, attr::MOI.AbstractConstraintAttribute, cr::ConstraintRef)
    @assert m === cr.m
    MOI.get(m.moi_backend, attr, index(cr))
end

MOI.set!(m::Model, attr::MOI.AbstractModelAttribute, value) = MOI.set!(m.moi_backend, attr, value)
function MOI.set!(m::Model, attr::MOI.AbstractVariableAttribute, v::VariableRef, value)
    @assert m === v.m
    MOI.set!(m.moi_backend, attr, index(v), value)
end
function MOI.set!(m::Model, attr::MOI.AbstractConstraintAttribute, cr::ConstraintRef, value)
    @assert m === cr.m
    MOI.set!(m.moi_backend, attr, index(cr), value)
end

###############################################################################
# GenericAffineExpression, AffExpr, AffExprConstraint
include("affexpr.jl")



###############################################################################
# GenericQuadExpr, QuadExpr
# GenericQuadConstraint, QuadConstraint
include("quadexpr.jl")

##########################################################################
# SOSConstraint  (special ordered set constraints)
# include("sos.jl")

include("sets.jl")

##########################################################################
# SDConstraint
include("sd.jl")

# handle dictionary of variables
function registervar(m::AbstractModel, varname::Symbol, value)
    registerobject(m, varname, value, "A variable or constraint named $varname is already attached to this model. If creating variables programmatically, use the anonymous variable syntax x = @variable(m, [1:N], ...).")
end
registervar(m::AbstractModel, varname, value) = error("Invalid variable name $varname")

function registercon(m::AbstractModel, conname::Symbol, value)
    registerobject(m, conname, value, "A variable or constraint named $conname is already attached to this model. If creating constraints programmatically, use the anonymous constraint syntax con = @constraint(m, ...).")
end
registercon(m::AbstractModel, conname, value) = error("Invalid constraint name $conname")

function registerobject(m::AbstractModel, name::Symbol, value, errorstring::String)
    obj_dict = object_dictionary(m)
    if haskey(obj_dict, name)
        error(errorstring)
        obj_dict[name] = nothing
    else
        obj_dict[name] = value
    end
    return value
end


"""
    Base.getindex(m::JuMP.AbstractModel, name::Symbol)

To allow easy accessing of JuMP tVariables and Constraints via `[]` syntax.
Returns the variable, or group of variables, or constraint, or group of constraints, of the given name which were added to the model. This errors if multiple variables or constraints share the same name.
"""
function Base.getindex(m::JuMP.AbstractModel, name::Symbol)
    obj_dict = object_dictionary(m)
    if !haskey(obj_dict, name)
        throw(KeyError("No object with name $name"))
    elseif obj_dict[name] === nothing
        error("There are multiple variables and/or constraints named $name that are already attached to this model. If creating variables programmatically, use the anonymous variable syntax x = @variable(m, [1:N], ...). If creating constraints programmatically, use the anonymous constraint syntax con = @constraint(m, ...).")
    else
        return obj_dict[name]
    end
end

"""
    Base.setindex!(m::JuMP.Model, value, name::Symbol)

stores the object `value` in the model `m` using so that it can be accessed via `getindex`.  Can be called with `[]` syntax.
"""
function Base.setindex!(m::JuMP.Model, value, name::Symbol)
    # if haskey(m.obj_dict, name)
    #     warn("Overwriting the object $name stored in the model. Consider using anonymous variables and constraints instead")
    # end
    m.obj_dict[name] = value
end

"""
    operator_warn(model::AbstractModel)
    operator_warn(model::Model)

This function is called on the model whenever two affine expressions are added
together without using `destructive_add!`, and at least one of the two
expressions has more than 50 terms.

For the case of `Model`, if this function is called more than 20,000 times then
a warning is generated once.
"""
function operator_warn(::AbstractModel) end
function operator_warn(model::Model)
    model.operator_counter += 1
    if model.operator_counter > 20000
        Base.warn_once(
            "The addition operator has been used on JuMP expressions a large " *
            "number of times. This warning is safe to ignore but may " *
            "indicate that model generation is slower than necessary. For " *
            "performance reasons, you should not add expressions in a loop. " *
            "Instead of x += y, use add_to_expression!(x,y) to modify x in " *
            "place. If y is a single variable, you may also use " *
            "add_to_expression!(x, coef, y) for x += coef*y.")
    end
end

##########################################################################
# Types used in the nonlinear code
struct NonlinearExpression
    m::Model
    index::Int
end

struct NonlinearParameter <: AbstractJuMPScalar
    m::Model
    index::Int
end

##########################################################################
include("containers.jl")
include("operators.jl")
include("macros.jl")
include("optimizerinterface.jl")
include("nlp.jl")
include("print.jl")


##########################################################################
end