Remove what was moved to LowRankOpt

Author: blegat

src/Bridges/Constraint/Constraint.jl

@@ -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
 

src/Bridges/Variable/Variable.jl

@@ -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
 

src/Test/test_conic.jl

@@ -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,