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Merged
odow merged 1 commit into from
Sep 1, 2026
Merged

Add support for v2.10.13 #243

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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@ OpenBLAS32_jll = "656ef2d0-ae68-5445-9ca0-591084a874a2"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[compat]
Cbc_jll = "=200.1000.1200"
Cbc_jll = "=200.1000.1300"
LinearAlgebra = "1"
MathOptInterface = "1.7"
OpenBLAS32_jll = "0.3.10"
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8 changes: 0 additions & 8 deletions src/MOI_wrapper/MOI_wrapper.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -393,12 +393,10 @@ function MOI.copy_to(dest::Optimizer, src::OptimizerCache)
end
end
end
any_sos = false
for (S, type) in ((MOI.SOS1{Float64}, 1), (MOI.SOS2{Float64}, 2))
starts, indices, weights = Cint[], Cint[], Float64[]
attr = MOI.ListOfConstraintIndices{MOI.VectorOfVariables,S}()
for ci in MOI.get(src, attr)
any_sos = true
push!(starts, Cint(length(weights)))
f = MOI.get(src, MOI.ConstraintFunction(), ci)
for x in f.variables
Expand All @@ -413,12 +411,6 @@ function MOI.copy_to(dest::Optimizer, src::OptimizerCache)
Cbc_addSOS(dest, N, starts, indices, weights, Cint(type))
end
end
if any_sos && Cbc_getNumIntegers(dest) == 0
@warn(
"There are known correctness issues using Cbc with SOS " *
"constraints and no binary variables.",
)
end
return _index_map(src)
end

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206 changes: 4 additions & 202 deletions test/MOI_wrapper.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -11,19 +11,17 @@ import Cbc
import MathOptInterface as MOI

function runtests()
for name in names(@__MODULE__; all = true)
if startswith("$(name)", "test_")
@testset "$(name)" begin
getfield(@__MODULE__, name)()
end
end
is_test(name) = startswith("$(name)", "test_")
@testset "$name" for name in filter(is_test, names(@__MODULE__; all = true))
getfield(@__MODULE__, name)()
end
return
end

function test_SolverName()
@test MOI.get(Cbc.Optimizer(), MOI.SolverName()) ==
"COIN Branch-and-Cut (Cbc)"
return
end

function test_supports_incremental_interface()
Expand All @@ -36,10 +34,6 @@ function test_runtests()
MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),
MOI.instantiate(Cbc.Optimizer; with_bridge_type = Float64),
)
MOI.Bridges.remove_bridge(
model.optimizer,
MOI.Bridges.Variable.ZerosBridge{Float64},
)
MOI.set(model, MOI.Silent(), true)
MOI.Test.runtests(
model,
Expand All @@ -52,11 +46,6 @@ function test_runtests()
],
),
exclude = [
# TODO(odow): upstream bug in Cbc
"test_linear_Indicator_",
"test_linear_SOS1_integration",
"test_linear_SOS2_integration",
"test_solve_SOS2_add_and_delete",
# Can't prove infeasible.
"test_conic_NormInfinityCone_INFEASIBLE",
"test_conic_NormOneCone_INFEASIBLE",
Expand Down Expand Up @@ -199,193 +188,6 @@ function test_issue_187()
return
end

# The test_linear_SOS1_integration test with the additional requirement that all
# variables are integer.
function test_SOS1()
model = MOI.Bridges.full_bridge_optimizer(
MOI.Utilities.CachingOptimizer(
MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),
Cbc.Optimizer(),
),
Float64,
)
config = MOI.Test.Config()
MOI.set(model, MOI.Silent(), true)
@test MOI.supports_constraint(
model,
MOI.VectorOfVariables,
MOI.SOS1{Float64},
)
@test MOI.supports_constraint(
model,
MOI.VariableIndex,
MOI.LessThan{Float64},
)
v = MOI.add_variables(model, 3)
MOI.add_constraint.(model, v, MOI.Integer())
@test MOI.get(model, MOI.NumberOfVariables()) == 3
vc1 = MOI.add_constraint(model, v[1], MOI.LessThan(1.0))
@test vc1.value == v[1].value
vc2 = MOI.add_constraint(model, v[2], MOI.LessThan(1.0))
@test vc2.value == v[2].value
vc3 = MOI.add_constraint(model, v[3], MOI.LessThan(2.0))
@test vc3.value == v[3].value
c1 = MOI.add_constraint(
model,
MOI.VectorOfVariables([v[1], v[2]]),
MOI.SOS1([1.0, 2.0]),
)
c2 = MOI.add_constraint(
model,
MOI.VectorOfVariables([v[1], v[3]]),
MOI.SOS1([1.0, 2.0]),
)
@test MOI.get(
model,
MOI.NumberOfConstraints{MOI.VectorOfVariables,MOI.SOS1{Float64}}(),
) == 2
#=
To allow for permutations in the sets and variable vectors
we're going to sort according to the weights
=#
cs_sos = MOI.get(model, MOI.ConstraintSet(), c2)
cf_sos = MOI.get(model, MOI.ConstraintFunction(), c2)
p = sortperm(cs_sos.weights)
@test isapprox(cs_sos.weights[p], [1.0, 2.0], config)
@test cf_sos.variables[p] == v[[1, 3]]
objf =
MOI.ScalarAffineFunction(MOI.ScalarAffineTerm.([2.0, 1.0, 1.0], v), 0.0)
MOI.set(
model,
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
objf,
)
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
@test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
@test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test isapprox(MOI.get(model, MOI.ObjectiveValue()), 3, config)
@test isapprox(MOI.get(model, MOI.VariablePrimal(), v), [0, 1, 2], config)
MOI.delete(model, c1)
MOI.delete(model, c2)
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test isapprox(MOI.get(model, MOI.ObjectiveValue()), 5, config)
@test isapprox(MOI.get(model, MOI.VariablePrimal(), v), [1, 1, 2], config)
return
end

# The test_linear_SOS2_integration test with the additional requirement that all
# variables are integer.
function test_SOS2()
model = MOI.Bridges.full_bridge_optimizer(
MOI.Utilities.CachingOptimizer(
MOI.Utilities.UniversalFallback(MOI.Utilities.Model{Float64}()),
Cbc.Optimizer(),
),
Float64,
)
config = MOI.Test.Config()
MOI.set(model, MOI.Silent(), true)
@test MOI.supports_constraint(
model,
MOI.VectorOfVariables,
MOI.SOS1{Float64},
)
@test MOI.supports_constraint(
model,
MOI.VectorOfVariables,
MOI.SOS2{Float64},
)
v = MOI.add_variables(model, 10)
@test MOI.get(model, MOI.NumberOfVariables()) == 10
bin_constraints = []
for i in 1:8
vc = MOI.add_constraint(model, v[i], MOI.Interval(0.0, 2.0))
@test vc.value == v[i].value
push!(bin_constraints, MOI.add_constraint(model, v[i], MOI.ZeroOne()))
@test bin_constraints[i].value == v[i].value
end
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.([1.0, 2.0, 3.0, -1.0], v[[1, 2, 3, 9]]),
0.0,
),
MOI.EqualTo(0.0),
)
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.(
[5.0, 4.0, 7.0, 2.0, 1.0, -1.0],
v[[4, 5, 6, 7, 8, 10]],
),
0.0,
),
MOI.EqualTo(0.0),
)
MOI.add_constraint(
model,
MOI.VectorOfVariables(v[[1, 2, 3]]),
MOI.SOS1([1.0, 2.0, 3.0]),
)
vv = MOI.VectorOfVariables(v[[4, 5, 6, 7, 8]])
sos2 = MOI.SOS2([5.0, 4.0, 7.0, 2.0, 1.0])
c = MOI.add_constraint(model, vv, sos2)
#=
To allow for permutations in the sets and variable vectors
we're going to sort according to the weights
=#
cs_sos = MOI.get(model, MOI.ConstraintSet(), c)
cf_sos = MOI.get(model, MOI.ConstraintFunction(), c)
p = sortperm(cs_sos.weights)
@test isapprox(cs_sos.weights[p], [1.0, 2.0, 4.0, 5.0, 7.0], config)
@test cf_sos.variables[p] == v[[8, 7, 5, 4, 6]]
objf = MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.([1.0, 1.0], [v[9], v[10]]),
0.0,
)
MOI.set(
model,
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{Float64}}(),
objf,
)
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
@test MOI.get(model, MOI.ObjectiveSense()) == MOI.MAX_SENSE
@test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test isapprox(MOI.get(model, MOI.ObjectiveValue()), 15.0, config)
@test isapprox(
MOI.get(model, MOI.VariablePrimal(), v),
[0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 3.0, 12.0],
config,
)
for cref in bin_constraints
MOI.delete(model, cref)
end
MOI.add_constraint.(model, v, MOI.Integer())
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test isapprox(MOI.get(model, MOI.ObjectiveValue()), 30.0, config)
@test isapprox(
MOI.get(model, MOI.VariablePrimal(), v),
[0.0, 0.0, 2.0, 2.0, 0.0, 2.0, 0.0, 0.0, 6.0, 24.0],
config,
)
return
end

"""
test_VariablePrimalStart()

Expand Down
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