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basic indicator in reified constraint (#251)
* basic indicator/reified in indicator/reified constraint
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‎CHANGELOG.md

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@@ -1,5 +1,8 @@
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# ConstrainSolver.jl - Changelog
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## Unreleased
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- Support for indicator/reified in indicator/reified (without bridges) [PR #251](https://github.com/Wikunia/ConstraintSolver.jl/pull/251)
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## v0.6.9 (17th of July 2021)
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- set activator to false when inner violated [PR #266](https://github.com/Wikunia/ConstraintSolver.jl/pull/266)
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@@ -28,6 +31,9 @@
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i.e `b := { sum(x) >= 10 || x in CS.AllDifferentSet() }`
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- Support for `&&` and `||` outside of `Indicator` and `Reified`
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- i.e `sum(x) >= 10 || x in CS.AllDifferentSet()`
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- Basic support for `Indicator` inside of reified:
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- i.e `@constraint(model, b1 := {b2 => { v == 1 }})`
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- currently lacks support for bridges such that `v > 1` in the inner constraint will fail
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- Some general performance updates [PR #247](https://github.com/Wikunia/ConstraintSolver.jl/pull/247)
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## v0.6.3 (17th of January 2021)

‎docs/src/supported.md

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@@ -89,6 +89,8 @@ The following list shows constraints that are implemented and those which are pl
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- [X] for all types of inner constraints
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- [X] Allow `&&` inside the inner constraint i.e `@constraint(m, b => {x + y >= 12 && 2x + y <= 7})`
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- [X] Allow `||` inside the inner constraint i.e `@constraint(m, b => {x + y >= 12 || 2x + y <= 7})`
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- [-] Have `Indicator` and `Reified` inside one another
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- this is only supported for simple cases i.e bridge support is missing
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- Reified constraints [#171](https://github.com/Wikunia/ConstraintSolver.jl/pull/171)
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- i.e `@constraint(m, b := {x + y >= 12})`
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- [X] for everything that is supported by indicator constraints

‎src/MOI_wrapper/util.jl

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@@ -48,6 +48,34 @@ function get_inner_constraint(com, func::VAF{T}, set::Union{ReifiedSet, Indicato
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return init_constraint_struct(com, set.set, inner_internals)
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end
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"""
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get_inner_constraint(com, func::VAF{T}, set::Union{ReifiedSet, IndicatorSet}, inner_set::Union{ReifiedSet{A}, IndicatorSet{A}, MOI.IndicatorSet{A}}) where {A,T<:Real}
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Create the inner constraint when the inner constraint is a reified or indicator constraint as well.
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"""
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function get_inner_constraint(com, func::VAF{T}, set::Union{ReifiedSet, IndicatorSet}, inner_set::Union{ReifiedSet{A}, IndicatorSet{A}, MOI.IndicatorSet{A}}) where {A,T<:Real}
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f = MOIU.eachscalar(func)
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inner_internals = ConstraintInternals(
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0,
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f[2:end],
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set.set,
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get_indices(f[2:end]),
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)
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inner_constraint = get_inner_constraint(com, f[2:end], inner_set, inner_set.set)
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complement_inner = get_complement_constraint(com, inner_constraint)
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indices = inner_internals.indices
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activator_internals = get_activator_internals(A, indices)
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if inner_set isa ReifiedSet
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constraint =
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ReifiedConstraint(inner_internals, activator_internals, inner_constraint, complement_inner)
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else
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constraint =
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IndicatorConstraint(inner_internals, activator_internals, inner_constraint)
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end
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return constraint
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end
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function get_inner_constraint(com, func::VAF{T}, set::Union{ReifiedSet, IndicatorSet, MOI.IndicatorSet}, inner_set::MOI.AbstractScalarSet) where {T<:Real}
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inner_terms = [v.scalar_term for v in func.terms if v.output_index == 2]
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inner_constant = func.constants[2]

‎src/constraints/indicator.jl

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@@ -12,7 +12,8 @@ function init_constraint!(
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com::CS.CoM,
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constraint::IndicatorConstraint,
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fct::Union{MOI.VectorOfVariables,VAF{T}},
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set::IS,
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set::IS;
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active = true
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) where {
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A,
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T<:Real,
@@ -37,7 +38,7 @@ function init_constraint!(
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# map the bounds to the indicator constraint
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constraint.bound_rhs = inner_constraint.bound_rhs
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# the indicator can't be activated if inner constraint is infeasible
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if !feasible
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if !feasible && active
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!rm!(com, indicator_var, Int(constraint.activate_on)) && return false
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end
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end

‎src/constraints/reified.jl

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@@ -2,7 +2,8 @@ function init_constraint!(
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com::CS.CoM,
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constraint::ReifiedConstraint,
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fct::Union{MOI.VectorOfVariables,VAF{T}},
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set::RS,
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set::RS;
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active = true
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) where {A,T<:Real,RS<:ReifiedSet{A}}
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inner_constraint = constraint.inner_constraint
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complement_constraint = constraint.complement_constraint
@@ -25,7 +26,7 @@ function init_constraint!(
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# map the bounds to the indicator constraint
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constraint.bound_rhs = inner_constraint.bound_rhs
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# the reified variable can't be activated if inner constraint is infeasible
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if !feasible
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if !feasible && active
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!rm!(com, rei_var, Int(constraint.activate_on)) && return false
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end
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end

‎test/constraints/indicator.jl

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com = CS.get_inner_model(m)
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@test is_solved(com)
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end
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@testset "indicator in indicator" begin
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m = Model(optimizer_with_attributes(
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CS.Optimizer,
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"all_solutions"=> true,
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"logging" => [],
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))
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@variable(m, a, Bin)
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@variable(m, b, Bin)
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@variable(m, c, Bin)
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@constraint(m, a => { b => { c == 1}})
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optimize!(m)
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@test JuMP.termination_status(m) == MOI.OPTIMAL
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com = CS.get_inner_model(m)
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nresults = JuMP.result_count(m)
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results = Set{Tuple{Bool,Bool,Bool}}()
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for i in 1:nresults
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aval,bval,cval = convert.(Bool, round.(JuMP.value.([a,b,c]; result=i)))
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if aval && bval
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@test cval
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end
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push!(results, (aval, bval, cval))
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end
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for av in [false, true], bv in [false, true], cv in [false, true]
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if !av || !bv
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@test (av,bv,cv) in results
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elseif av && bv && cv
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@test (av,bv,cv) in results
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end
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end
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end
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@testset "reified in indicator" begin
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m = Model(optimizer_with_attributes(
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CS.Optimizer,
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"all_solutions"=> true,
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"logging" => [],
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))
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@variable(m, a, Bin)
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@variable(m, b, Bin)
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@variable(m, c, Bin)
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@constraint(m, a => { b := { c == 1}})
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optimize!(m)
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@test JuMP.termination_status(m) == MOI.OPTIMAL
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com = CS.get_inner_model(m)
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nresults = JuMP.result_count(m)
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results = Set{Tuple{Bool,Bool,Bool}}()
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for i in 1:nresults
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aval,bval,cval = convert.(Bool, round.(JuMP.value.([a,b,c]; result=i)))
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if aval
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@test (bval && cval) || (!bval && !cval)
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end
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push!(results, (aval, bval, cval))
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end
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for av in [false, true], bv in [false, true], cv in [false, true]
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if !av
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@test (av,bv,cv) in results
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elseif (bv && cv) || (!bv && !cv)
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@test (av,bv,cv) in results
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end
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end
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end
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@testset "reified in reified in indicator" begin
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m = Model(optimizer_with_attributes(
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CS.Optimizer,
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"all_solutions"=> true,
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"logging" => [],
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))
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@variable(m, a, Bin)
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@variable(m, b, Bin)
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@variable(m, c, Bin)
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@variable(m, d, Bin)
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@constraint(m, a => { b := { c := {d == 1}}})
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optimize!(m)
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@test JuMP.termination_status(m) == MOI.OPTIMAL
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com = CS.get_inner_model(m)
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nresults = JuMP.result_count(m)
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results = Set{Tuple{Bool,Bool,Bool,Bool}}()
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for i in 1:nresults
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aval,bval,cval,dval = convert.(Bool, round.(JuMP.value.([a,b,c,d]; result=i)))
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if aval
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if bval
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@test (cval && dval) || (!cval && !dval)
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else
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@test (dval && !cval) || (!dval && cval)
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end
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end
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push!(results, (aval, bval, cval, dval))
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end
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for av in [false, true], bv in [false, true], cv in [false, true], dv in [false, true]
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if !av
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@test (av,bv,cv,dv) in results
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else
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if bv
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if (cv && dv) || (!cv && !dv)
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@test (av,bv,cv,dv) in results
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end
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elseif (dv && !cv) || (!dv && cv)
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@test (av,bv,cv,dv) in results
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end
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end
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end
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end
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end

‎test/monks_and_doors.jl

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# This test case is copied from http://hakank.org/julia/constraints/monks_and_doors.jl
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# which is a website by Håkan Kjellerstrand
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# please check it out :)
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@testset "Monks & Doors" begin
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all_solutions = true
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model = Model(optimizer_with_attributes(CS.Optimizer,
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"all_solutions"=> all_solutions,
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# "all_optimal_solutions"=>all_solutions,
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"logging"=>[],
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"traverse_strategy"=>:BFS,
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"branch_split"=>:InHalf,
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"branch_strategy" => :IMPS, # default
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))
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num_doors = 4
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num_monks = 8
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@variable(model, doors[1:num_doors], Bin)
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da,db,dc,dd = doors
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door_names = ["A","B","C","D"]
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@variable(model, monks[1:num_monks], Bin)
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m1,m2,m3,m4,m5,m6,m7,m8 = monks
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# Monk 1: Door A is the exit.
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# M1 #= A (Picat constraint)
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@constraint(model, m1 == da)
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# Monk 2: At least one of the doors B and C is the exit.
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# M2 #= 1 #<=> (B #\/ C)
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@constraint(model, m2 := { db == 1 || dc == 1})
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# Monk 3: Monk 1 and Monk 2 are telling the truth.
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# M3 #= 1 #<=> (M1 #/\ M2)
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@constraint(model, m3 := { m1 == 1 && m2 == 1})
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# Monk 4: Doors A and B are both exits.
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# M4 #= 1 #<=> (A #/\ B)
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@constraint(model, m4 := { da == 1 && db == 1})
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# Monk 5: Doors A and C are both exits.
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# M5 #= 1 #<=> (A #/\ C)
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@constraint(model, m5 := { da == 1 && dc == 1})
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# Monk 6: Either Monk 4 or Monk 5 is telling the truth.
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# M6 #= 1 #<=> (M4 #\/ M5)
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@constraint(model, m6 := { m4 == 1|| m5 == 1})
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# Monk 7: If Monk 3 is telling the truth, so is Monk 6.
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# M7 #= 1 #<=> (M3 #=> M6)
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@constraint(model, m7 := { m3 => {m6 == 1}})
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# Monk 8: If Monk 7 and Monk 8 are telling the truth, so is Monk 1.
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# M8 #= 1 #<=> ((M7 #/\ M8) #=> (M1))
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b1 = @variable(model, binary=true)
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@constraint(model, b1 := {m7 == 1 && m8 == 1})
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@constraint(model, m8 := {b1 => {m1 == 1}})
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# Exactly one door is an exit.
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# (A + B + C + D) #= 1
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@constraint(model, da + db + dc + dd == 1)
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# Solve the problem
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optimize!(model)
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status = JuMP.termination_status(model)
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# println("status:$status")
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num_sols = 0
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@test status == MOI.OPTIMAL
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num_sols = MOI.get(model, MOI.ResultCount())
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@test num_sols == 1
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doors_val = convert.(Integer,JuMP.value.(doors))
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monks_val = convert.(Integer,JuMP.value.(monks))
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# exit door is A
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@test doors_val[1] == 1
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@test sum(doors_val) == 1
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@test sum(monks_val) == 3
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# monks 1,7,8 are the only ones who tell the truth
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@test monks_val[1] == monks_val[7] == monks_val[8] == 1
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end

‎test/runtests.jl

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@@ -77,6 +77,7 @@ include("constraints/equal_to.jl")
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include("lp_solver.jl")
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include("steiner.jl")
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include("monks_and_doors.jl")
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include("stable_set.jl")
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include("small_special.jl")
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include("maximum_weight_matching.jl")

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