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Remove what was moved to LowRankOpt
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@blegat
blegat committed Dec 11, 2024
1 parent dfa44d9 commit 9564dc1
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1 change: 0 additions & 1 deletion src/Bridges/Constraint/Constraint.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -108,7 +108,6 @@ function add_all_bridges(bridged_model, ::Type{T}) where {T}
MOI.Bridges.add_bridge(bridged_model, SOS1ToMILPBridge{T})
MOI.Bridges.add_bridge(bridged_model, SOS2ToMILPBridge{T})
MOI.Bridges.add_bridge(bridged_model, IndicatorToMILPBridge{T})
MOI.Bridges.add_bridge(bridged_model, LinearCombinationBridge{T})
return
end

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1 change: 0 additions & 1 deletion src/Bridges/Variable/Variable.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,6 @@ function add_all_bridges(model, ::Type{T}) where {T}
MOI.Bridges.add_bridge(model, RSOCtoPSDBridge{T})
MOI.Bridges.add_bridge(model, HermitianToSymmetricPSDBridge{T})
MOI.Bridges.add_bridge(model, ParameterToEqualToBridge{T})
MOI.Bridges.add_bridge(model, DotProductsBridge{T})
return
end

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179 changes: 0 additions & 179 deletions src/Test/test_conic.jl
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Expand Up @@ -5770,185 +5770,6 @@ function setup_test(
return
end

"""
The goal is to find the maximum lower bound `γ` for the polynomial `x^2 - 2x`.
Using samples `-1` and `1`, the polynomial `x^2 - 2x - γ` evaluates at `-γ`
and `2 - γ` respectively.
The dot product with the gram matrix is the evaluation of `[1; x] * [1 x]` hence
`[1; -1] * [1 -1]` and `[1; 1] * [1 1]` respectively.
The polynomial version is:
max γ
s.t. [-γ, 2 - γ] in SetDotProducts(
PSD(2),
[[1; -1] * [1 -1], [1; 1] * [1 1]],
)
Its dual (moment version) is:
min -y[1] - y[2]
s.t. [-γ, 2 - γ] in LinearCombinationInSet(
PSD(2),
[[1; -1] * [1 -1], [1; 1] * [1 1]],
)
"""
function test_conic_PositiveSemidefinite_RankOne_polynomial(
model::MOI.ModelLike,
config::Config{T},
) where {T}
set = MOI.SetDotProducts(
MOI.PositiveSemidefiniteConeTriangle(2),
MOI.TriangleVectorization.([
MOI.PositiveSemidefiniteFactorization(T[1, -1]),
MOI.PositiveSemidefiniteFactorization(T[1, 1]),
]),
)
@requires MOI.supports_constraint(
model,
MOI.VectorAffineFunction{T},
typeof(set),
)
@requires MOI.supports_incremental_interface(model)
@requires MOI.supports(model, MOI.ObjectiveSense())
@requires MOI.supports(model, MOI.ObjectiveFunction{MOI.VariableIndex}())
γ = MOI.add_variable(model)
c = MOI.add_constraint(
model,
MOI.Utilities.operate(vcat, T, T(3) - T(1) * γ, T(-1) - T(1) * γ),
set,
)
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
MOI.set(model, MOI.ObjectiveFunction{MOI.VariableIndex}(), γ)
if _supports(config, MOI.optimize!)
@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.PrimalStatus()) == MOI.FEASIBLE_POINT
if _supports(config, MOI.ConstraintDual)
@test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
end
@test ≈(MOI.get(model, MOI.ObjectiveValue()), T(-1), config)
if _supports(config, MOI.DualObjectiveValue)
@test ≈(MOI.get(model, MOI.DualObjectiveValue()), T(-1), config)
end
@test ≈(MOI.get(model, MOI.VariablePrimal(), γ), T(-1), config)
@test ≈(MOI.get(model, MOI.ConstraintPrimal(), c), T[4, 0], config)
if _supports(config, MOI.ConstraintDual)
@test ≈(MOI.get(model, MOI.ConstraintDual(), c), T[0, 1], config)
end
end
return
end

function setup_test(
::typeof(test_conic_PositiveSemidefinite_RankOne_polynomial),
model::MOIU.MockOptimizer,
::Config{T},
) where {T<:Real}
A = MOI.TriangleVectorization{
T,
MOI.PositiveSemidefiniteFactorization{T,Vector{T}},
}
MOIU.set_mock_optimize!(
model,
(mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(
mock,
T[-1],
(
MOI.VectorAffineFunction{T},
MOI.SetDotProducts{
MOI.PositiveSemidefiniteConeTriangle,
A,
Vector{A},
},
) => [T[0, 1]],
),
)
return
end

"""
The moment version of `test_conic_PositiveSemidefinite_RankOne_polynomial`
We look for a measure `μ = y1 * δ_{-1} + y2 * δ_{1}` where `δ_{c}` is the Dirac
measure centered at `c`. The objective is
`⟨μ, x^2 - 2x⟩ = y1 * ⟨δ_{-1}, x^2 - 2x⟩ + y2 * ⟨δ_{1}, x^2 - 2x⟩ = 3y1 - y2`.
We want `μ` to be a probability measure so `1 = ⟨μ, 1⟩ = y1 + y2`.
"""
function test_conic_PositiveSemidefinite_RankOne_moment(
model::MOI.ModelLike,
config::Config{T},
) where {T}
set = MOI.LinearCombinationInSet(
MOI.PositiveSemidefiniteConeTriangle(2),
MOI.TriangleVectorization.([
MOI.PositiveSemidefiniteFactorization(T[1, -1]),
MOI.PositiveSemidefiniteFactorization(T[1, 1]),
]),
)
@requires MOI.supports_add_constrained_variables(model, typeof(set))
@requires MOI.supports_incremental_interface(model)
@requires MOI.supports(model, MOI.ObjectiveSense())
@requires MOI.supports(
model,
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}(),
)
y, cy = MOI.add_constrained_variables(model, set)
c = MOI.add_constraint(model, T(1) * y[1] + T(1) * y[2], MOI.EqualTo(T(1)))
MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
MOI.set(
model,
MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}(),
T(3) * y[1] - T(1) * y[2],
)
if _supports(config, MOI.optimize!)
@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.PrimalStatus()) == MOI.FEASIBLE_POINT
if _supports(config, MOI.ConstraintDual)
@test MOI.get(model, MOI.DualStatus()) == MOI.FEASIBLE_POINT
end
@test ≈(MOI.get(model, MOI.ObjectiveValue()), T(-1), config)
if _supports(config, MOI.DualObjectiveValue)
@test ≈(MOI.get(model, MOI.DualObjectiveValue()), T(-1), config)
end
@test ≈(MOI.get(model, MOI.VariablePrimal(), y), T[0, 1], config)
@test ≈(MOI.get(model, MOI.ConstraintPrimal(), c), T(1), config)
if _supports(config, MOI.ConstraintDual)
@test ≈(MOI.get(model, MOI.ConstraintDual(), cy), T[4, 0], config)
@test ≈(MOI.get(model, MOI.ConstraintDual(), c), T(-1), config)
end
end
return
end

function setup_test(
::typeof(test_conic_PositiveSemidefinite_RankOne_moment),
model::MOIU.MockOptimizer,
::Config{T},
) where {T<:Real}
A = MOI.TriangleVectorization{
T,
MOI.PositiveSemidefiniteFactorization{T,Vector{T}},
}
MOIU.set_mock_optimize!(
model,
(mock::MOIU.MockOptimizer) -> MOIU.mock_optimize!(
mock,
T[0, 1],
(MOI.ScalarAffineFunction{T}, MOI.EqualTo{T}) => [T(-1)],
(
MOI.VectorOfVariables,
MOI.LinearCombinationInSet{
MOI.PositiveSemidefiniteConeTriangle,
A,
Vector{A},
},
) => [T[4, 0]],
),
)
return
end

"""
_test_det_cone_helper_ellipsoid(
model::MOI.ModelLike,
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