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Enhancing the improve relaxation test with cut generation #756

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Enhancing the improve relaxation test with cut generation
artalvpes Dec 17, 2022
211d919
Merge branch 'master' into improve_relax_test
guimarqu Dec 19, 2022
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50 changes: 37 additions & 13 deletions test/integration/improve_relax_callback.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@
# (three singletons and three pairs) which gives a fractional solution at the root node.
# Then a relaxation improvement function "improve_relaxation" is called to remove two of
# the pairs from the list of pricing solutions and from the master problem.
CL.@with_kw struct ImproveRelaxationAlgo <: ClA.AbstractOptimizationAlgorithm
CL.@with_kw mutable struct ImproveRelaxationAlgo <: ClA.AbstractOptimizationAlgorithm
userfunc::Function
end

Expand All @@ -31,10 +31,12 @@ ClA.key_from_storage_unit_type(::Type{ToyNodeInfoUnit}) = ToyNodeInfoKey()
ClA.record_type_from_key(::ToyNodeInfoKey) = ToyNodeInfo

function ClB.new_record(::Type{ToyNodeInfo}, id::Int, form::ClMP.Formulation, unit::ToyNodeInfoUnit)
@info "In new_record $id = $(unit.value)"
return ToyNodeInfo(unit.value)
end

function ClB.restore_from_record!(form::ClMP.Formulation, unit::ToyNodeInfoUnit, record::ToyNodeInfo)
@info "In restore_from_record! $(record.value)"
unit.value = record.value
return
end
Expand Down Expand Up @@ -81,15 +83,19 @@ function test_improve_relaxation(; do_improve::Bool)
@axis(B, [1])
@variable(toy, y[b in B] >= 0, Int)
@variable(toy, x[b in B, i in I], Bin)
@constraint(toy, sp[i in I], sum(x[b,i] for b in B) == 1)
@constraint(toy, lb[i in I], sum(4 * x[b,i] for b in B) >= 3)
@constraint(toy, ub[i in I], sum(x[b,i] for b in B) <= 1)
@objective(toy, Min, sum(y[b] for b in B))
@dantzig_wolfe_decomposition(toy, dec, B)
customvars!(toy, VarData)

return toy, x, y, dec, B
end

call_improve_relaxation(masterform, cbdata) = improve_relaxation(masterform, cbdata)
dummyfunc() = nothing
improve_algo = ImproveRelaxationAlgo(
userfunc = dummyfunc
)

coluna = JuMP.optimizer_with_attributes(
CL.Optimizer,
Expand All @@ -104,9 +110,7 @@ function test_improve_relaxation(; do_improve::Bool)
],
primal_heuristics = [],
before_cutgen_user_algorithm = ClA.BeforeCutGenAlgo(
ImproveRelaxationAlgo(
userfunc = call_improve_relaxation
),
improve_algo,
"Improve relaxation"
)
),
Expand All @@ -133,6 +137,7 @@ function test_improve_relaxation(; do_improve::Bool)
storage = ClMP.getstorage(ClMP.getmaster(reform))
unit = storage.units[ToyNodeInfoUnit].storage_unit # TODO: to improve
info_val = unit.value
@info "Read unit.value = $info_val"

max_info_val = max(max_info_val, info_val)
if info_val == 9999
Expand Down Expand Up @@ -173,13 +178,30 @@ function test_improve_relaxation(; do_improve::Bool)
end
return
end
function cutsep(cbdata)
I = [1, 2, 3]
for i in I
if sum(callback_value(cbdata, x[b, i]) for b in B) < 0.999
cut = @build_constraint(sum(x[b, i] for b in B) >= 1)
MOI.submit(model, MOI.UserCut(cbdata), cut)
@info "Adding cut for i = $i"
end
end
end

subproblems = BD.getsubproblems(dec)
MOI.set(model, MOI.UserCutCallback(), cutsep)
BD.specify!.(
subproblems, lower_multiplicity = 0, upper_multiplicity = 3,
solver = enumerative_pricing
)

improve_count = 0

function improve_relaxation(masterform, cbdata)
storage = ClMP.getstorage(masterform)
unit = storage.units[ToyNodeInfoUnit].storage_unit # TODO: to improve
improve_count += 1
if do_improve
# Get the reduced costs of the original variables
I = [1, 2, 3]
Expand All @@ -196,34 +218,36 @@ function test_improve_relaxation(; do_improve::Bool)
if var.custom_data.items in [[1, 2], [1, 3]]
ClMP.deactivate!(masterform, vid)
changed = true

storage = ClMP.getstorage(masterform)
unit = storage.units[ToyNodeInfoUnit].storage_unit # TODO: to improve
unit.value = 9999
end
end
end

@info "Update unit.value = $(unit.value)"
@info "improve_relaxation $(changed ? "changed" : "did not change")"
return changed
else
@info "Update unit.value = $(unit.value)"
@info "improve_relaxation did nothing"
return false
end
end

improve_algo.userfunc = improve_relaxation
JuMP.optimize!(model)
@test JuMP.objective_value(model) ≈ 2.0
@test JuMP.termination_status(model) == MOI.OPTIMAL
for b in B
sets = BD.getsolutions(model, b)
count = [0, 0]
for s in sets
@test BD.value(s) == 1.0 # value of the master column variable
@test BD.value(s, x[b, 1]) != BD.value(s, x[b, 2]) # only x[1,1] in its set
@test BD.value(s, x[b, 1]) != BD.value(s, x[b, 3]) # only x[1,1] in its set
@test BD.value(s, x[b, 2]) == BD.value(s, x[b, 3]) # x[1,2] and x[1,3] in the same set
count[round(Int, sum(BD.value(s, x[b, i]) for i in 1:3))] += 1
end
@test count == [1, 1]
end
@test do_improve || max_info_val == 888
@test do_improve || max_info_val == 999
@test improve_count == (do_improve ? 1 : 2)
end

@testset "Improve relaxation callback" begin
Expand Down