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CBLS

Documentation for CBLS.

CBLS.AllDifferent — Type

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

@constraint(model, X in AllDifferent())
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CBLS.AllEqual — Type

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

@constraint(model, X in AllEqual())
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CBLS.AllEqualParam — Type

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

@constraint(model, X in AllEqualParam(param))
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CBLS.AlwaysTrue — Type

Always return true. Mainly used for testing purpose.

@constraint(model, X in AlwaysTrue())
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CBLS.DistDifferent — Type

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

@constraint(model, X in DistDifferent())
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CBLS.Eq — Type

Equality between two variables.

@constraint(model, X in Eq())
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CBLS.Error — Type
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))
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CBLS.LessThanParam — Type

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

@constraint(model, x in LessThanParam(param))
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CBLS.MOIAllEqualParam — Type
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
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CBLS.MOIError — Type
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
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CBLS.MOILessThanParam — Type
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
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CBLS.MOIMinusEqualParam — Type
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
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CBLS.MOIPredicate — Type
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
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CBLS.MOISumEqualParam — Type
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
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CBLS.MinusEqualParam — Type

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

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

DOCSTRING

Arguments:

  • solver::Solver: DESCRIPTION
  • status::MOI.TerminationStatusCode: DESCRIPTION
  • options::Options: DESCRIPTION
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CBLS.Ordered — Type

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

@constraint(model, X in Ordered())
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CBLS.Predicate — Type
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))
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CBLS.ScalarFunction — Type
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))
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CBLS.SequentialTasks — Type

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

@constraint(model, X in SequentialTasks())
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CBLS.SumEqualParam — Type

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

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

DOCSTRING

Arguments:

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

DOCSTRING

Arguments:

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

DOCSTRING

Arguments:

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

DOCSTRING

Arguments:

  • ``: DESCRIPTION
  • ``: DESCRIPTION
  • bool: DESCRIPTION
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MathOptInterface.set — Method
MOI.set(model::Optimizer, ::MOI.TimeLimitSec, value::Union{Nothing,Float64})

Set the time limit

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