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[release-0.18] Fix objective constant bug in conic problems #1727

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Jan 5, 2019
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[release-0.18] Fix objective constant bug in conic problems #1727

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8dca2c2
Fix objective constant bug in conic problems
odow Jan 4, 2019
f120c1d
[ci skip] handle constant verbosely for clarity
odow Jan 4, 2019
1e3d9a4
Update sdp.jl
odow Jan 4, 2019
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11 changes: 6 additions & 5 deletions src/solvers.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -269,12 +269,13 @@ function solve(m::Model; suppress_warnings=false,
end
end

# The MathProgBase interface defines a conic problem to always be
# a minimization problem, so we need to flip the objective before
# reporting it to the user
# The MathProgBase interface defines a conic problem to always be a
# minimization problem, so we need to flip the objective before reporting it
# to the user. We also need to account for the objective constant which was
# added above by subtracting it prior to the flip and then re-adding it.
if traits.conic && m.objSense == :Max
m.objBound *= -1
m.objVal *= -1
m.objBound = -1 * (m.objBound - m.obj.aff.constant) + m.obj.aff.constant
m.objVal = -1 * (m.objVal - m.obj.aff.constant) + m.obj.aff.constant
end

# If the solver was initially not set, we will restore this status
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28 changes: 28 additions & 0 deletions test/sdp.jl
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Expand Up @@ -770,4 +770,32 @@ ispsd(x::JuMP.JuMPArray) = ispsd(x.innerArray)
@test norm(getvalue(X)) < 1e-5
@test isapprox(getdual(X), Matrix(1.0I, 3, 3), atol=1e-5)
end

@testset "Objective constants with $solver" for solver in sdp_solvers
# Test handling of constants in the objective. See JuMP issue #1390.
@testset "Minimize" begin
model = Model(solver = solver)
@variable(model, Xs1[1:3, 1:3], SDP)
@variable(model, Xs2[1:1, 1:1], SDP)
@variable(model, Xs3[1:1, 1:1], SDP)
@constraint(model, Xs1[1, 1] == 1)
@constraint(model, Xs2[1, 1] + Xs1[2, 2] + Xs1[3, 3] == 4)
@constraint(model, Xs3[1, 1] - Xs1[2, 1] - Xs1[3, 1] == 0)
@objective(model, Min, -Xs1[1, 3] + 0.5)
@test solve(model) == :Optimal
@test isapprox(getobjectivevalue(model), -1.5000, atol=1e-3)
end
@testset "Maximize" begin
model = Model(solver = solver)
@variable(model, Xs1[1:3, 1:3], SDP)
@variable(model, Xs2[1:1, 1:1], SDP)
@variable(model, Xs3[1:1, 1:1], SDP)
@constraint(model, Xs1[1, 1] == 1)
@constraint(model, Xs2[1, 1] + Xs1[2, 2] + Xs1[3, 3] == 4)
@constraint(model, Xs3[1, 1] - Xs1[2, 1] - Xs1[3, 1] == 0)
@objective(model, Max, -Xs1[1, 3] + 0.5)
@test solve(model) == :Optimal
@test isapprox(getobjectivevalue(model), 1.9142, atol=1e-3)
end
end
end