CBLS.AllDifferentType

Global constraint ensuring that all the values of a given configuration are unique.

@constraint(model, X in AllDifferent())
CBLS.AllEqualType

Global constraint ensuring that all the values of X are all equal.

@constraint(model, X in AllEqual())
CBLS.AllEqualParamType

Global constraint ensuring that all the values of X are all equal to a given parameter param.

@constraint(model, X in AllEqualParam(param))
CBLS.AlwaysTrueType

Always return true. Mainly used for testing purpose.

@constraint(model, X in AlwaysTrue())
CBLS.DistDifferentType

Local constraint ensuring that, given a vector X of size 4, |X[1] - X[2]| ≠ |X[3] - X[4]|).

@constraint(model, X in DistDifferent())
CBLS.EqType

Equality between two variables.

@constraint(model, X in Eq())
CBLS.ErrorType
Error{F <: Function} <: JuMP.AbstractVectorSet

The solver will compute a straightforward error function based on the concept. To run the solver efficiently, it is possible to provide an error function err instead of concept. err must return a nonnegative real number.

@constraint(model, X in Error(err))
CBLS.LessThanParamType

Constraint ensuring that the value of x is less than a given parameter param.

@constraint(model, x in LessThanParam(param))
CBLS.MOIAllEqualParamType
MOIAllEqualParam{T <: Number} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• param::T: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOIAllEqualParam(param, dim = 0) = begin #= none:5 =# new{typeof(param)}(param, dim) end: DESCRIPTION
CBLS.MOIErrorType
MOIError{F <: Function} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• f::F: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOIError(f, dim = 0) = begin #= none:5 =# new{typeof(f)}(f, dim) end: DESCRIPTION
CBLS.MOILessThanParamType
MOILessThanParam{T <: Number} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• param::T: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOILessThanParam(param, dim = 0) = begin #= none:5 =# new{typeof(param)}(param, dim) end: DESCRIPTION
CBLS.MOIMinusEqualParamType
MOIMinusEqualParam{T <: Number} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• param::T: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOIMinusEqualParam(param, dim = 0) = begin #= none:5 =# new{typeof(param)}(param, dim) end: DESCRIPTION
CBLS.MOIPredicateType
MOIPredicate{F <: Function} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• f::F: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOIPredicate(f, dim = 0) = begin #= none:5 =# new{typeof(f)}(f, dim) end: DESCRIPTION
CBLS.MOISumEqualParamType
MOISumEqualParam{T <: Number} <: MOI.AbstractVectorSet

DOCSTRING

Arguments:

• param::T: DESCRIPTION
• dimension::Int: DESCRIPTION
• MOISumEqualParam(param, dim = 0) = begin #= none:5 =# new{typeof(param)}(param, dim) end: DESCRIPTION
CBLS.MinusEqualParamType

Constraint ensuring that the value of x is less than a given parameter param.

@constraint(model, x in MinusEqualParam(param))
CBLS.OptimizerType
Optimizer <: MOI.AbstractOptimizer

DOCSTRING

Arguments:

• solver::Solver: DESCRIPTION
• status::MOI.TerminationStatusCode: DESCRIPTION
• options::Options: DESCRIPTION
CBLS.OrderedType

Global constraint ensuring that all the values of x are ordered.

@constraint(model, X in Ordered())
CBLS.PredicateType
Predicate{F <: Function} <: JuMP.AbstractVectorSet

Assuming X is a (collection of) variables, concept a boolean function over X, and that a model is defined. In JuMP syntax we can create a constraint based on concept as follows.

@constraint(model, X in Predicate(concept))
CBLS.ScalarFunctionType
ScalarFunction{F <: Function, V <: Union{Nothing, VOV}} <: MOI.AbstractScalarFunction

A container to express any function with real value in JuMP syntax. Used with the @objective macro.

Arguments:

• f::F: function to be applied to X
• X::V: a subset of the variables of the model.

Given a model, and some (collection of) variables X to optimize. an objective function f can be added as follows. Note that only Min for minimization us currently defined. Max will come soon.

# Applies to all variables in order of insertion.
# Recommended only when the function argument order does not matter.
@objective(model, ScalarFunction(f))

# Generic use
@objective(model, ScalarFunction(f, X))
CBLS.SequentialTasksType

Local constraint ensuring that, given a vector X of size 4, |X[1] - X[2]| ≠ |X[3] - X[4]|).

@constraint(model, X in SequentialTasks())
CBLS.SumEqualParamType

Global constraint ensuring that the sum of the values of X is equal to a given parameter param.

@constraint(model, X in SumEqualParam(param))
JuMP.build_variableMethod
JuMP.build_variable(::Function, info::JuMP.VariableInfo, set::T) where T <: MOI.AbstractScalarSet

DOCSTRING

Arguments:

• : DESCRIPTION
• info: DESCRIPTION
• set: DESCRIPTION
MathOptInterface.add_constraintMethod
MOI.add_constraint(optimizer::Optimizer, vars::MOI.VectorOfVariables, set::MOIError)

DOCSTRING

Arguments:

• optimizer: DESCRIPTION
• vars: DESCRIPTION
• set: DESCRIPTION
MathOptInterface.add_constraintMethod
MOI.add_constraint(optimizer::Optimizer, v::VI, set::DiscreteSet{T}) where T <: Number

DOCSTRING

Arguments:

• optimizer: DESCRIPTION
• v: DESCRIPTION
• set: DESCRIPTION
MathOptInterface.setFunction
MOI.set(::Optimizer, ::MOI.Silent, bool = true) = begin

DOCSTRING

Arguments:

• : DESCRIPTION
• : DESCRIPTION
• bool: DESCRIPTION
MathOptInterface.setMethod
MOI.set(model::Optimizer, p::MOI.RawOptimizerAttribute, value)

Set a RawOptimizerAttribute to value