DisjunctiveProgramming.jl

A JuMP extension for Generalized Disjunctive Programming
Author hdavid16
Popularity
12 Stars
Updated Last
10 Months Ago
Started In
July 2020

DisjunctiveProgramming.jl

Generalized Disjunctive Programming extension to JuMP

Installation

using Pkg
Pkg.add("DisjunctiveProgramming")

Disjunctions

After defining a JuMP model, disjunctions can be added to the model by using the @disjunction macro. This macro is called by @disjunction(m, disjuncts...; kwargs...), where disjuncts...` is a list of at least two expressions of the form:

  1. A valid expression accepted by JuMP.@constraint. Names for the constraints or containers of constraints cannot be passed (use option 2).
  2. A valid expression accepted by JuMP.@constraints (using `begin...end)
  3. A valid expression accepted by JuMP.@NLconstraint. Containers of constraints cannot be passed (use option 4). Naming of non-linear constraints is not currently supported.
  4. A valid expression accepted by JuMP.@NLconstraints (using `begin...end)
  5. Tuple of expressions accepted by options 1 and/or 3.

NOTES:

  • Vectorized constraints (using . notation) are not currently supported. The current workarround is to first create the constraint with the @constraint macro and then use the add_disjunction!, instead of the @disjunction macro. The add_disjunction! function receives the same arguments as the @disjunction macro, with the exception that instead of creating the constraints in the disjunctions, references to previously created constraints are used for the disjuncts.
  • Any constraints that are of EqualTo type are split into two constraints (e.g., f(x) == 0 -> 0 <= f(x) <= 0). This is necessary only for the Big-M reformulation of equality constraints, but is currently applied regardless of the reformulation technique.
  • Any constraints that are of Interval type are split into two constraints (one for each bound).
  • It is assumed that the disjuncts belonging to a disjunction are proper disjunctions (mutually exclussive) and only one of them will be selected (XOR).

The valid key-word arguments for the @disjunction macro are:

  • reformulation::Symbol: :big_m for Big-M Reformulation, :hull for Hull Reformulation
  • M: Big-M value used when reformulation = :big_m.
  • ϵ: epsilon tolerance for the perspective function proposed by Furman, et al. [2020]. Only used when reformulation = :hull.
  • name::Symbol: Name for the disjunction (also name for indicator variable used on that disjunction). If not passed (name = missing), a symbolic name will be generated with the prefix disj. The mutual exclussion constraint on the binary indicator variables can be accessed with model[Symbol("XOR(disj_$name)")].

When a disjunction is defined using the @disjunction macro, the disjunctions are reformulated to algebraic constraints via either Big-M or Hull reformulations. For the Hull reformulation, disaggregated variables are generated by adding the suffix _$name$i to the original variables, where i is the index of the disjunct in that disjunction. Bounding constraints are applied to the disaggregated variables and can be accessed with model[Symbol("$<original var>_$name$i_lb")] and model[Symbol("$<original var>_$name$i_ub")] for the lower bound and upper bound constraints, respectively. The aggregation constraint can be accessed with model[Symbol("$<original var>_aggregation")]. For Big-M reformulations, the user may provide an M object that represents the BigM value(s). The M object can be a Number that is applied to all constraints in the disjuncts, or a Vector/Tuple of values that are used for each of the disjuncts. For Hull reformulations, the user may provide an ϵ value for the perspective function (default is ϵ = 1e-6). The ϵ object can be a Number that is applied to all perspective functions, or a Vector/Tuple of values that are used for each of the disjuncts.

For empty disjuncts, use nothing for their positional argument (e.g., @disjunction(m, x <= 1, nothing, reformulation = :big_m)).

NOTE: :object_dict is used in the extension dictionary to store the object dictionary of models using DisjunctiveProgramming.jl.

Logical Propositions

Boolean logic can be included in the model by using the @proposition macro. This macro will take an expression that uses only binary variables from the model (typically a subset of the indicator variables used in the disjunctions) and one or more of the following Boolean operators:

  • (or, typed with \vee + tab)
  • (and, typed with \wedge + tab)
  • ¬ (negation, typed with \neg + tab)
  • (implication, typed with \Rightarrow + tab)
  • (double implication or equivalence, typed with \Leftrightarrow + tab) The logical proposition is then internally reformulated to an algebraic constraint that is added to the model. This constrait can be accessed with model[Symbol("<logical proposition expression>")].

Example

The example below is from the Northwestern University Process Optimization Open Textbook.

To perform the Big-M reformulation, :big_m is passed to the reformulation keyword argument. If nothing is passed to the keyword argument M, tight Big-M values will be inferred from the variable bounds using IntervalArithmetic.jl. If x is not bounded, Big-M values must be provided for either the whole system (e.g., M = 10) or for each of the constraint arrays in the example (e.g., M = (10,10)).

To perform the Hull reformulation, reformulation = :hull. Variables must have bounds for the reformulation to work.

using JuMP
using DisjunctiveProgramming

m = Model()
@variable(m, -5  x  10)
@disjunction(
    m,
    0  x  3,
    5  x  9,
    reformulation=:big_m,
    name=:y
)
@proposition(m, y[1] ∨ y[2]) #this is a redundant proposition

print(m)

┌ Warning: disj_y[1] : x in [0.0, 3.0] uses the `MOI.Interval` set. Each instance of the interval set has been split into two constraints, one for each bound.
┌ Warning: disj_y[2] : x in [5.0, 9.0] uses the `MOI.Interval` set. Each instance of the interval set has been split into two constraints, one for each bound.
Feasibility
Subject to
 XOR(disj_y) : y[1] + y[2] == 1.0         <- XOR constraint
 y[1] ∨ y[2] : y[1] + y[2] >= 1.0         <- reformulated logical proposition (name is the proposition)
 disj_y[1][lb] : -x + 5 y[1] <= 5.0       <- left-side of constraint in 1st disjunct (name is assigned to disj_y[1][lb])
 disj_y[1][ub] : x + 7 y[1] <= 10.0       <- right-side of constraint in 1st disjunct (name is assigned to disj_y[1][ub])
 disj_y[2][lb] : -x + 10 y[2] <= 5.0      <- left-side of constraint in 2nd disjunct (name is assigned to disj_y[2][lb])
 disj_y[2][ub] : x + y[2] <= 10.0         <- right-side of constraint in 2nd disjunct (name is assigned to disj_y[2][ub])
 x >= -5.0                                <- variable lower bound
 x <= 10.0                                <- variable upper bound
 y[1] binary                              <- indicator variable (1st disjunct) is binary
 y[2] binary                              <- indicator variable (2nd disjunct) is binary

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