From 94a5b550a6515fc503414dff42c42d899e41445e Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Mon, 20 May 2024 14:15:27 +0000 Subject: [PATCH] build based on bcc29c8 --- dev/.documenter-siteinfo.json | 2 +- dev/api/index.html | 40 +++++++++++++++++------------------ dev/index.html | 2 +- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index f220777..d90f4bb 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-20T14:07:16","documenter_version":"1.4.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-20T14:15:23","documenter_version":"1.4.1"}} \ No newline at end of file diff --git a/dev/api/index.html b/dev/api/index.html index e535c7d..04d7a07 100644 --- a/dev/api/index.html +++ b/dev/api/index.html @@ -1,85 +1,85 @@ -API · DisjunctiveProgramming.jl
GitHub

API

DisjunctiveProgramming.@disjunction — Macro
@disjunction(model, expr, kw_args...)

Add a disjunction described by the expression expr, which must be a Vector of LogicalVariableRefs.

@disjunction(model, ref[i=..., j=..., ...], expr, kw_args...)

Add a group of disjunction described by the expression expr parameterized by i, j, ..., which must be a Vector of LogicalVariableRefs.

In both of the above calls, a Disjunct tag can be added to create nested disjunctions.

The recognized keyword arguments in kw_args are the following:

  • base_name: Sets the name prefix used to generate constraint names. It corresponds to the constraint name for scalar constraints, otherwise, the constraint names are set to base_name[...] for each index ... of the axes axes.
  • container: Specify the container type.
  • exactly1: Specify a Bool whether a constraint should be added to only allow selecting one disjunct in the disjunction.

To create disjunctions without macros, see disjunction.

source
DisjunctiveProgramming.@disjunctions — Macro
@disjunctions(model, args...)

Adds groups of disjunctions at once, in the same fashion as the @disjunction macro.

The model must be the first argument, and multiple disjunctions can be added on multiple lines wrapped in a begin ... end block.

The macro returns a tuple containing the disjunctions that were defined.

Example

julia model = GDPModel(); @variable(model, w); @variable(model, x); @variable(model, Y[1:4], LogicalVariable); @constraint(model, [i=1:2], w == i, Disjunct(Y[i])); @constraint(model, [i=3:4], x == i, Disjunct(Y[i])); @disjunctions(model, begin [Y[1], Y[2]] [Y[3], Y[4]] end);`

source
DisjunctiveProgramming.binary_variable — Method
binary_variable(vref::LogicalVariableRef)::JuMP.AbstractVariableRef

Returns the underlying binary variable for the logical variable vref which is used in the reformulated model. This is helpful to embed logical variables in algebraic constraints.

source
DisjunctiveProgramming.disjunction — Function
disjunction(
+API · DisjunctiveProgramming.jl
GitHub
API
DisjunctiveProgramming.@disjunction — Macro
@disjunction(model, expr, kw_args...)
Add a disjunction described by the expression expr,  which must be a Vector of LogicalVariableRefs.
@disjunction(model, ref[i=..., j=..., ...], expr, kw_args...)
Add a group of disjunction described by the expression expr parameterized by i, j, ..., which must be a Vector of LogicalVariableRefs. 
In both of the above calls, a Disjunct tag can be added to create  nested disjunctions.
The recognized keyword arguments in kw_args are the following:
  • base_name: Sets the name prefix used to generate constraint names.    It corresponds to the constraint name for scalar constraints, otherwise,    the constraint names are set to base_name[...] for each index ...        of the axes axes.
  • container: Specify the container type.
  • exactly1: Specify a Bool whether a constraint should be added to   only allow selecting one disjunct in the disjunction.
To create disjunctions without macros, see disjunction.
source
DisjunctiveProgramming.@disjunctions — Macro
@disjunctions(model, args...)
Adds groups of disjunctions at once, in the same fashion as the @disjunction macro.
The model must be the first argument, and multiple disjunctions can be added on multiple  lines wrapped in a begin ... end block.
The macro returns a tuple containing the disjunctions that were defined.
Example
julia model = GDPModel(); @variable(model, w); @variable(model, x); @variable(model, Y[1:4], LogicalVariable); @constraint(model, [i=1:2], w == i, Disjunct(Y[i])); @constraint(model, [i=3:4], x == i, Disjunct(Y[i])); @disjunctions(model, begin     [Y[1], Y[2]]     [Y[3], Y[4]] end);`
source
DisjunctiveProgramming.binary_variable — Method
binary_variable(vref::LogicalVariableRef)::JuMP.AbstractVariableRef
Returns the underlying binary variable for the logical variable vref which  is used in the reformulated model. This is helpful to embed logical variables  in algebraic constraints.
source
DisjunctiveProgramming.disjunction — Function
disjunction(
     model::JuMP.AbstractModel,
     disjunct_indicators::Vector{LogicalVariableRef},
     [nested_tag::Disjunct],
     [name::String = ""];
     [exactly1::Bool = true]
-)
Create a disjunction comprised of disjuncts with indicator variables disjunct_indicators and add it to model. For nested disjunctions, the nested_tag is required to indicate which disjunct it will be part of in the parent disjunction. By default, exactly1 adds a constraint of the form @constraint(model, disjunct_indicators in Exactly(1)) only allowing one of the disjuncts to be selected; this is required for certain reformulations like Hull. For nested disjunctions, exactly1 creates a constraint of the form @constraint(model, disjunct_indicators in Exactly(nested_tag.indicator)). To conveniently generate many disjunctions at once, see @disjunction and @disjunctions.
source
DisjunctiveProgramming.make_disaggregated_variable — Method
make_disaggregated_variable(
+)
Create a disjunction comprised of disjuncts with indicator variables disjunct_indicators and add it to model. For nested disjunctions, the nested_tag is required to indicate which disjunct it will be part of in the parent disjunction. By default, exactly1 adds a constraint of the form @constraint(model, disjunct_indicators in Exactly(1)) only allowing one of the disjuncts to be selected; this is required for certain reformulations like Hull. For nested disjunctions, exactly1 creates a constraint of the form @constraint(model, disjunct_indicators in Exactly(nested_tag.indicator)). To conveniently generate many disjunctions at once, see @disjunction and @disjunctions.
source
DisjunctiveProgramming.make_disaggregated_variable — Method
make_disaggregated_variable(
     model::JuMP.AbstractModel, 
     vref::JuMP.AbstractVariableRef, 
     name::String, 
     lower_bound::Number, 
     upper_bound::Number
-    )::JuMP.AbstractVariableRef
Creates and adds a variable to model with name name and bounds lower_bound  and upper_bound based on the original variable vref. This is used to  create dissagregated variables needed for the Hull reformulation. This is implemented for model::JuMP.GenericModel and  vref::JuMP.GenericVariableRef, but it serves as an extension point for  interfaces with other model/variable reference types. This also requires  the implementation of requires_disaggregation.
source
DisjunctiveProgramming.reformulate_disjunct_constraint — Method
reformulate_disjunct_constraint(
+    )::JuMP.AbstractVariableRef
Creates and adds a variable to model with name name and bounds lower_bound  and upper_bound based on the original variable vref. This is used to  create dissagregated variables needed for the Hull reformulation. This is implemented for model::JuMP.GenericModel and  vref::JuMP.GenericVariableRef, but it serves as an extension point for  interfaces with other model/variable reference types. This also requires  the implementation of requires_disaggregation.
source
DisjunctiveProgramming.reformulate_disjunct_constraint — Method
reformulate_disjunct_constraint(
     model::JuMP.AbstractModel,  
     con::JuMP.AbstractConstraint, 
     bvref::JuMP.AbstractVariableRef,
     method::AbstractReformulationMethod
-)
Extension point for reformulation method method to reformulate disjunction constraint con over each  constraint. If method needs to specify how to reformulate the entire disjunction, see  reformulate_disjunction.
source
DisjunctiveProgramming.reformulate_disjunction — Method
reformulate_disjunction(
     model::JuMP.AbstractModel, 
     disj::Disjunction,
     method::AbstractReformulationMethod
-) where {T<:Disjunction}
Reformulate a disjunction using the specified method. Current reformulation methods include BigM, Hull, and Indicator. This method can be extended for other reformulation techniques.
The disj field is the ConstraintData object for the disjunction, stored in the  disjunctions field of the GDPData object.
source
DisjunctiveProgramming.reformulate_model — Function
reformulate_model(model::JuMP.AbstractModel, method::AbstractSolutionMethod = BigM())
Reformulate a GDPModel using the specified method. Prior to reformulation, all previous reformulation variables and constraints are deleted.
source
DisjunctiveProgramming.requires_disaggregation — Method
requires_disaggregation(vref::JuMP.AbstractVariableRef)::Bool
Return a Bool whether vref requires disaggregation for the Hull  reformulation. This is intended as an extension point for interfaces with  DisjunctiveProgramming that use variable reference types that are not  JuMP.GenericVariableRefs. Errors if vref is not a JuMP.GenericVariableRef. See also make_disaggregated_variable.
source
DisjunctiveProgramming.requires_exactly1 — Method
requires_exactly1(method::AbstractReformulationMethod)
Return a Bool whether method requires that Exactly 1 disjunct be selected  as true for each disjunction. For new reformulation method types, this should be  extended to return true if such a constraint is required (defaults to false otherwise).
source
DisjunctiveProgramming.set_variable_bound_info — Function
set_variable_bound_info(vref, method::AbstractReformulationMethod)::Tuple{<:Number, <:Number}
Returns a tuple of the form (lower_bound, upper_bound) which are the bound information needed by  method to reformulate disjunctions. This only needs to be implemented for methods where  requires_variable_bound_info(method) = true. These bounds can later be accessed via  variable_bound_info.
source
JuMP.add_constraint — Function
JuMP.add_constraint(
+) where {T<:Disjunction}
Reformulate a disjunction using the specified method. Current reformulation methods include BigM, Hull, and Indicator. This method can be extended for other reformulation techniques.
The disj field is the ConstraintData object for the disjunction, stored in the  disjunctions field of the GDPData object.
source
DisjunctiveProgramming.reformulate_model — Function
reformulate_model(model::JuMP.AbstractModel, method::AbstractSolutionMethod = BigM())
Reformulate a GDPModel using the specified method. Prior to reformulation, all previous reformulation variables and constraints are deleted.
source
DisjunctiveProgramming.requires_disaggregation — Method
requires_disaggregation(vref::JuMP.AbstractVariableRef)::Bool
Return a Bool whether vref requires disaggregation for the Hull  reformulation. This is intended as an extension point for interfaces with  DisjunctiveProgramming that use variable reference types that are not  JuMP.GenericVariableRefs. Errors if vref is not a JuMP.GenericVariableRef. See also make_disaggregated_variable.
source
DisjunctiveProgramming.requires_exactly1 — Method
requires_exactly1(method::AbstractReformulationMethod)
Return a Bool whether method requires that Exactly 1 disjunct be selected  as true for each disjunction. For new reformulation method types, this should be  extended to return true if such a constraint is required (defaults to false otherwise).
source
DisjunctiveProgramming.set_variable_bound_info — Function
set_variable_bound_info(vref, method::AbstractReformulationMethod)::Tuple{<:Number, <:Number}
Returns a tuple of the form (lower_bound, upper_bound) which are the bound information needed by  method to reformulate disjunctions. This only needs to be implemented for methods where  requires_variable_bound_info(method) = true. These bounds can later be accessed via  variable_bound_info.
source
JuMP.add_constraint — Function
JuMP.add_constraint(
     model::JuMP.AbstractModel,
     con::_DisjunctConstraint,
     name::String = ""
-)::DisjunctConstraintRef
Extend JuMP.add_constraint to add a Disjunct to a GDPModel. The constraint is added to the GDPData in the .ext dictionary of the GDPModel.
source
JuMP.add_constraint — Method
function JuMP.add_constraint(
+)::DisjunctConstraintRef
Extend JuMP.add_constraint to add a Disjunct to a GDPModel. The constraint is added to the GDPData in the .ext dictionary of the GDPModel.
source
JuMP.add_constraint — Method
function JuMP.add_constraint(
     model::JuMP.GenericModel,
     c::JuMP.ScalarConstraint{_LogicalExpr, MOI.EqualTo{Bool}},
     name::String = ""
-)
Extend JuMP.add_constraint to allow creating logical proposition constraints for a GDPModel with the @constraint macro. Users should define logical constraints via the syntax @constraint(model, logical_expr := true).
source
JuMP.add_constraint — Method
function JuMP.add_constraint(
+)
Extend JuMP.add_constraint to allow creating logical proposition constraints for a GDPModel with the @constraint macro. Users should define logical constraints via the syntax @constraint(model, logical_expr := true).
source
JuMP.add_constraint — Method
function JuMP.add_constraint(
     model::JuMP.GenericModel,
     c::VectorConstraint{<:F, S},
     name::String = ""
-) where {F <: Vector{<:LogicalVariableRef}, S <: AbstractCardinalitySet}
Extend JuMP.add_constraint to allow creating logical cardinality constraints for a GDPModel with the @constraint macro.
source
JuMP.add_variable — Function
JuMP.add_variable(model::JuMP.Model, v::LogicalVariable, 
-                  name::String = "")::LogicalVariableRef
Extend JuMP.add_variable for LogicalVariables. This  helps enable @variable(model, [var_expr], Logical).
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+) where {F <: Vector{<:LogicalVariableRef}, S <: AbstractCardinalitySet}
Extend JuMP.add_constraint to allow creating logical cardinality constraints for a GDPModel with the @constraint macro.
source
JuMP.add_variable — Function
JuMP.add_variable(model::JuMP.Model, v::LogicalVariable, 
+                  name::String = "")::LogicalVariableRef
Extend JuMP.add_variable for LogicalVariables. This  helps enable @variable(model, [var_expr], Logical).
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::_MOI.AbstractScalarSet,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add constraints to disjuncts. This in combination with JuMP.add_constraint enables the use of @constraint(model, [name], constr_expr, tag), where tag is a Disjunct(::Type{LogicalVariableRef}). The user must specify the LogicalVariable to use as the indicator for the _DisjunctConstraint being created.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add constraints to disjuncts. This in combination with JuMP.add_constraint enables the use of @constraint(model, [name], constr_expr, tag), where tag is a Disjunct(::Type{LogicalVariableRef}). The user must specify the LogicalVariable to use as the indicator for the _DisjunctConstraint being created.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::MathOptInterface.Nonnegatives,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::MathOptInterface.Nonpositives,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::MathOptInterface.Zeros,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::Nonnegatives,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::Nonpositives,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
JuMP.build_constraint(
     _error::Function,
     func,
     set::Zeros,
     tag::Disjunct
-)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
function JuMP.build_constraint(
+)::_DisjunctConstraint
Extend JuMP.build_constraint to add VectorConstraints to disjuncts.
source
JuMP.build_constraint — Method
function JuMP.build_constraint(
     _error::Function,
     func::AbstractVector{T},
     set::S
 ) where {T <: LogicalVariableRef, S <: Union{Exactly, AtLeast, AtMost}}
Extend JuMP.build_constraint to add logical cardinality constraints to a GDPModel. This in combination with JuMP.add_constraint enables the use of @constraint(model, [name], logical_expr in set), where set can be either of the following cardinality sets: AtLeast(n), AtMost(n), or Exactly(n).
Example
To select exactly 1 logical variable Y to be true, do (the same can be done with AtLeast(n) and AtMost(n)):
using DisjunctiveProgramming
 model = GDPModel();
 @variable(model, Y[i = 1:2], LogicalVariable);
-@constraint(model, [Y[1], Y[2]] in Exactly(1));
source
JuMP.build_variable — Method
JuMP.build_variable(_error::Function, info::JuMP.VariableInfo, 
-                    ::Union{Type{Logical}, Logical})
Extend JuMP.build_variable to work with logical variables. This in  combination with JuMP.add_variable enables the use of  @variable(model, [var_expr], Logical).
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::DisjunctConstraintRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::DisjunctionRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::LogicalConstraintRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::DisjunctConstraintRef)
Delete a disjunct constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::DisjunctionRef)
Delete a disjunction constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::LogicalConstraintRef)
Delete a logical constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, vref::LogicalVariableRef)::Nothing
Delete the logical variable associated with vref from the GDP model.
source
JuMP.fix — Method
JuMP.fix(vref::LogicalVariableRef, value::Bool)::Nothing
Fix a logical variable to a value. Update the fixing constraint if one exists, otherwise create a new one.
source
JuMP.fix_value — Method
JuMP.fix_value(vref::LogicalVariableRef)::Bool
Return the value to which a logical variable is fixed.
source
JuMP.index — Method
JuMP.index(cref::DisjunctConstraintRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(cref::DisjunctionRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(cref::LogicalConstraintRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(vref::LogicalVariableRef)::LogicalVariableIndex
Return the index of logical variable that associated with vref.
source
JuMP.is_fixed — Method
JuMP.is_fixed(vref::LogicalVariableRef)::Bool
Return true if vref is a fixed variable. If     true, the fixed value can be queried with     fix_value.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::DisjunctConstraintRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::DisjunctionRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::LogicalConstraintRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, vref::LogicalVariableRef)::Bool
Return true if vref refers to a valid logical variable in GDP model.
source
JuMP.isequal_canonical — Method
JuMP.isequal_canonical(v::LogicalVariableRef, w::LogicalVariableRef)::Bool
Return true if v and w refer to the same logical variable in the same GDP model.
source
JuMP.name — Method
JuMP.name(cref::DisjunctConstraintRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(cref::DisjunctionRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(cref::LogicalConstraintRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(vref::LogicalVariableRef)::String
Get a logical variable's name attribute.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::DisjunctConstraintRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::DisjunctionRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::LogicalConstraintRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(vref::LogicalVariableRef)::JuMP.AbstractModel
Return the GDP model to which vref belongs.
source
JuMP.set_name — Method
JuMP.set_name(cref::DisjunctConstraintRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(cref::DisjunctionRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(cref::LogicalConstraintRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(vref::LogicalVariableRef, name::String)::Nothing
Set a logical variable's name attribute.
source
JuMP.set_start_value — Method
JuMP.set_start_value(vref::LogicalVariableRef, value::Union{Nothing, Bool})::Nothing
Set the start value of the logical variable vref.
Pass nothing to unset the start value.
source
JuMP.start_value — Method
JuMP.start_value(vref::LogicalVariableRef)::Bool
Return the start value of the logical variable vref.
source
JuMP.unfix — Method
JuMP.unfix(vref::LogicalVariableRef)::Nothing
Delete the fixed value of a logical variable.
source
JuMP.value — Method
JuMP.value(vref::LogicalVariableRef)::Bool
Returns the optimized value of vref. This dispatches on  value(binary_variable(vref)) and then rounds to the closest  Bool value.
source
DisjunctiveProgramming.BigM — Type
BigM{T} <: AbstractReformulationMethod
A type for using the big-M reformulation approach for disjunctive constraints.
Fields
  • value::T: Big-M value (default = 1e9).
  • tight::Bool: Attempt to tighten the Big-M value (default = true)?
source
DisjunctiveProgramming.ConstraintData — Type
ConstraintData{C <: JuMP.AbstractConstraint}
A type for storing constraint objects in GDPData and any meta-data  they possess.
Fields
  • constraint::C: The constraint.
  • name::String: The name of the proposition.
source
DisjunctiveProgramming.Disjunct — Type
Disjunct
Used as a tag for constraints that will be used in disjunctions. This is done via  the following syntax:
julia> @constraint(model, [constr_expr], Disjunct)
+@constraint(model, [Y[1], Y[2]] in Exactly(1));
source
JuMP.build_variable — Method
JuMP.build_variable(_error::Function, info::JuMP.VariableInfo, 
+                    ::Union{Type{Logical}, Logical})
Extend JuMP.build_variable to work with logical variables. This in  combination with JuMP.add_variable enables the use of  @variable(model, [var_expr], Logical).
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::DisjunctConstraintRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::DisjunctionRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.constraint_object — Method
JuMP.constraint_object(cref::LogicalConstraintRef)
Return the underlying constraint data for the constraint     referenced by cref.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::DisjunctConstraintRef)
Delete a disjunct constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::DisjunctionRef)
Delete a disjunction constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, cref::LogicalConstraintRef)
Delete a logical constraint from the GDP model.
source
JuMP.delete — Method
JuMP.delete(model::JuMP.AbstractModel, vref::LogicalVariableRef)::Nothing
Delete the logical variable associated with vref from the GDP model.
source
JuMP.fix — Method
JuMP.fix(vref::LogicalVariableRef, value::Bool)::Nothing
Fix a logical variable to a value. Update the fixing constraint if one exists, otherwise create a new one.
source
JuMP.fix_value — Method
JuMP.fix_value(vref::LogicalVariableRef)::Bool
Return the value to which a logical variable is fixed.
source
JuMP.index — Method
JuMP.index(cref::DisjunctConstraintRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(cref::DisjunctionRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(cref::LogicalConstraintRef)
Return the index constraint associated with cref.
source
JuMP.index — Method
JuMP.index(vref::LogicalVariableRef)::LogicalVariableIndex
Return the index of logical variable that associated with vref.
source
JuMP.is_fixed — Method
JuMP.is_fixed(vref::LogicalVariableRef)::Bool
Return true if vref is a fixed variable. If     true, the fixed value can be queried with     fix_value.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::DisjunctConstraintRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::DisjunctionRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, cref::LogicalConstraintRef)
Return true if cref refers to a valid constraint in the GDP model.
source
JuMP.is_valid — Method
JuMP.is_valid(model::JuMP.AbstractModel, vref::LogicalVariableRef)::Bool
Return true if vref refers to a valid logical variable in GDP model.
source
JuMP.isequal_canonical — Method
JuMP.isequal_canonical(v::LogicalVariableRef, w::LogicalVariableRef)::Bool
Return true if v and w refer to the same logical variable in the same GDP model.
source
JuMP.name — Method
JuMP.name(cref::DisjunctConstraintRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(cref::DisjunctionRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(cref::LogicalConstraintRef)
Get a constraint's name attribute.
source
JuMP.name — Method
JuMP.name(vref::LogicalVariableRef)::String
Get a logical variable's name attribute.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::DisjunctConstraintRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::DisjunctionRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(cref::LogicalConstraintRef)
Return the model to which cref belongs.
source
JuMP.owner_model — Method
JuMP.owner_model(vref::LogicalVariableRef)::JuMP.AbstractModel
Return the GDP model to which vref belongs.
source
JuMP.set_name — Method
JuMP.set_name(cref::DisjunctConstraintRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(cref::DisjunctionRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(cref::LogicalConstraintRef, name::String)
Set a constraint's name attribute.
source
JuMP.set_name — Method
JuMP.set_name(vref::LogicalVariableRef, name::String)::Nothing
Set a logical variable's name attribute.
source
JuMP.set_start_value — Method
JuMP.set_start_value(vref::LogicalVariableRef, value::Union{Nothing, Bool})::Nothing
Set the start value of the logical variable vref.
Pass nothing to unset the start value.
source
JuMP.start_value — Method
JuMP.start_value(vref::LogicalVariableRef)::Bool
Return the start value of the logical variable vref.
source
JuMP.unfix — Method
JuMP.unfix(vref::LogicalVariableRef)::Nothing
Delete the fixed value of a logical variable.
source
JuMP.value — Method
JuMP.value(vref::LogicalVariableRef)::Bool
Returns the optimized value of vref. This dispatches on  value(binary_variable(vref)) and then rounds to the closest  Bool value.
source
DisjunctiveProgramming.BigM — Type
BigM{T} <: AbstractReformulationMethod
A type for using the big-M reformulation approach for disjunctive constraints.
Fields
  • value::T: Big-M value (default = 1e9).
  • tight::Bool: Attempt to tighten the Big-M value (default = true)?
source
DisjunctiveProgramming.ConstraintData — Type
ConstraintData{C <: JuMP.AbstractConstraint}
A type for storing constraint objects in GDPData and any meta-data  they possess.
Fields
  • constraint::C: The constraint.
  • name::String: The name of the proposition.
source
DisjunctiveProgramming.Disjunct — Type
Disjunct
Used as a tag for constraints that will be used in disjunctions. This is done via  the following syntax:
julia> @constraint(model, [constr_expr], Disjunct)
 
-julia> @constraint(model, [constr_expr], Disjunct(lvref))
where lvref is a LogicalVariableRef that will ultimately be associated  with the disjunct the constraint is added to. If no lvref is given, then one is  generated when the disjunction is created.
source
DisjunctiveProgramming.Disjunction — Type
Disjunction{M <: JuMP.AbstractModel} <: JuMP.AbstractConstraint
A type for a disjunctive constraint that is comprised of a collection of  disjuncts of indicated by a unique LogicalVariableIndex.
Fields
  • indicators::Vector{LogicalVariableref}: The references to the logical variables 
(indicators) that uniquely identify each disjunct in the disjunction.
  • nested::Bool: Is this disjunction nested within another disjunction?
source
DisjunctiveProgramming.GDPModel — Method
GDPModel([optimizer]; [kwargs...])::JuMP.Model
+julia> @constraint(model, [constr_expr], Disjunct(lvref))
where lvref is a LogicalVariableRef that will ultimately be associated  with the disjunct the constraint is added to. If no lvref is given, then one is  generated when the disjunction is created.
source
DisjunctiveProgramming.Disjunction — Type
Disjunction{M <: JuMP.AbstractModel} <: JuMP.AbstractConstraint
A type for a disjunctive constraint that is comprised of a collection of  disjuncts of indicated by a unique LogicalVariableIndex.
Fields
  • indicators::Vector{LogicalVariableref}: The references to the logical variables 
(indicators) that uniquely identify each disjunct in the disjunction.
  • nested::Bool: Is this disjunction nested within another disjunction?
source
DisjunctiveProgramming.GDPModel — Method
GDPModel([optimizer]; [kwargs...])::JuMP.Model
 
 GDPModel{T}([optimizer]; [kwargs...])::JuMP.GenericModel{T}
 
-GDPModel{M <: JuMP.AbstractModel, VrefType, CrefType}([optimizer], [args...]; [kwargs...])::M
The core model object for building general disjunction programming models.
source
DisjunctiveProgramming.Hull — Type
Hull{T} <: AbstractReformulationMethod
A type for using the convex hull reformulation approach for disjunctive  constraints.
Fields
  • value::T: epsilon value for nonlinear hull reformulations (default = 1e-6).
source
DisjunctiveProgramming.Logical — Type
Logical{T}
Tag for creating logical variables using @variable. Most often this will  be used to enable the syntax:
@variable(model, var_expr, Logical, [kwargs...])
which creates a LogicalVariable that will ultimately be  reformulated into a binary variable of the form:
@variable(model, var_expr, Bin, [kwargs...])
To include a tag that is used to create the reformulated variables, the syntax  becomes:
@variable(model, var_expr, Logical(MyTag()), [kwargs...])
which creates a LogicalVariable that is associated with MyTag() such  that the reformulation binary variables are of the form:
@variable(model, var_expr, Bin, MyTag(), [kwargs...])
source
DisjunctiveProgramming.LogicalVariable — Type
LogicalVariable <: JuMP.AbstractVariable
A variable type the logical variables associated with disjuncts in a Disjunction.
Fields
  • fix_value::Union{Nothing, Bool}: A fixed boolean value if there is one.
  • start_value::Union{Nothing, Bool}: An initial guess if there is one.
source

+GDPModel{M <: JuMP.AbstractModel, VrefType, CrefType}([optimizer], [args...]; [kwargs...])::M

The core model object for building general disjunction programming models.

source
DisjunctiveProgramming.Hull — Type
Hull{T} <: AbstractReformulationMethod

A type for using the convex hull reformulation approach for disjunctive constraints.

Fields

  • value::T: epsilon value for nonlinear hull reformulations (default = 1e-6).
source
DisjunctiveProgramming.Logical — Type
Logical{T}

Tag for creating logical variables using @variable. Most often this will be used to enable the syntax:

@variable(model, var_expr, Logical, [kwargs...])

which creates a LogicalVariable that will ultimately be reformulated into a binary variable of the form:

@variable(model, var_expr, Bin, [kwargs...])

To include a tag that is used to create the reformulated variables, the syntax becomes:

@variable(model, var_expr, Logical(MyTag()), [kwargs...])

which creates a LogicalVariable that is associated with MyTag() such that the reformulation binary variables are of the form:

@variable(model, var_expr, Bin, MyTag(), [kwargs...])
source
DisjunctiveProgramming.LogicalVariable — Type
LogicalVariable <: JuMP.AbstractVariable

A variable type the logical variables associated with disjuncts in a Disjunction.

Fields

  • fix_value::Union{Nothing, Bool}: A fixed boolean value if there is one.
  • start_value::Union{Nothing, Bool}: An initial guess if there is one.
source
diff --git a/dev/index.html b/dev/index.html index ab5c9a8..05a5028 100644 --- a/dev/index.html +++ b/dev/index.html @@ -90,4 +90,4 @@ # x[2]_Y[2] ≤ 20 # x[1]_Y[2] ≤ 20 # Y[1] binary -# Y[2] binary

Contributing

DisjunctiveProgramming is being actively developed and suggestions or other forms of contribution are encouraged. There are many ways to contribute to this package. Feel free to create an issue to address questions or provide feedback.

+# Y[2] binary

Contributing

DisjunctiveProgramming is being actively developed and suggestions or other forms of contribution are encouraged. There are many ways to contribute to this package. Feel free to create an issue to address questions or provide feedback.