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[feature] Support MOI.VectorAffineFunction #170

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oyamad opened this issue May 14, 2019 · 6 comments · May be fixed by #171
Open

[feature] Support MOI.VectorAffineFunction #170

oyamad opened this issue May 14, 2019 · 6 comments · May be fixed by #171

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@oyamad
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oyamad commented May 14, 2019

Would it be costly to support MOI.VectorAffineFunction also?

Polyhedra.jl/src/lphrep.jl

Lines 3 to 5 in 3b289dd

MOI.Utilities.@model(_MOIModel,
(), (MOI.EqualTo, MOI.LessThan,), (), (),
(), (MOI.ScalarAffineFunction,), (), ())

@blegat
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blegat commented May 14, 2019

It is already supported through bridges:

Polyhedra.jl/src/lphrep.jl

Line 21 in 3b289dd

MOI.copy_to(MOI.Bridges.full_bridge_optimizer(_model, Float64), model)

full_bridge_optimizer adds many bridges including http://www.juliaopt.org/MathOptInterface.jl/dev/apireference/#MathOptInterface.Bridges.ScalarizeBridge which transforms VectorAffineFunction to ScalarAffineFunction.

@oyamad
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oyamad commented May 14, 2019

@blegat Thanks. My problem seems to be that I haven't been able to understand the manual of MOI...

To be concrete, consider the linear program max c x s.t. A x <= b, where

A = [1 1; -1 0; 0 -1]
b = [1, 0, 0]
c = [1, 0]

Let's use CDDLib.Optimizer:

T = Float64  # or Rational{BigInt}
optimizer = CDDLib.Optimizer{T}()
x = MOI.add_variables(optimizer, length(c))
MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}(),
        MOI.ScalarAffineFunction{T}(MOI.ScalarAffineTerm{T}.(c, x), 0))
MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE)

I know the following works:

for i in 1:size(A, 1)
    MOI.add_constraint(
        optimizer,
        MOI.ScalarAffineFunction{T}(
            MOI.ScalarAffineTerm{T}.(A[i, :], x), 0
        ),
        MOI.LessThan{T}(b[i])
    )
end
MOI.optimize!(optimizer)
2-element Array{Float64,1}:
 1.0
 0.0

But it feels a bit stupid to pass the content of the matrix A row by row, so I wanted to pass A itself (along with b) without decomposing it. Is it possible? The following doesn't work:

MOI.add_constraint(
    optimizer,
    MOI.VectorAffineFunction{T}(A, -b),
    MOI.Nonpositives(length(b))
)
MethodError: no method matching Array{MathOptInterface.VectorAffineTerm{Float64},1}(::Array{Int64,2})

@blegat
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blegat commented May 14, 2019

See this discourse post for how to formulate the constraint with VectorAffineFunction.

@oyamad
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oyamad commented May 14, 2019

Ah sorry, I should have read that thread carefully.

Now the issue seems to be that MOI.Nonpositives is not supported:

T = Float64  # or Rational{BigInt}
optimizer = CDDLib.Optimizer{T}()
x = MOI.add_variables(optimizer, length(c))
MOI.set(optimizer, MOI.ObjectiveFunction{MOI.ScalarAffineFunction{T}}(),
        MOI.ScalarAffineFunction{T}(MOI.ScalarAffineTerm{T}.(c, x), 0))
MOI.set(optimizer, MOI.ObjectiveSense(), MOI.MAX_SENSE)

terms = MOI.VectorAffineTerm{T}.(
    1:size(A, 1), MOI.ScalarAffineTerm{T}.(A, reshape(x, 1, length(x)))
)
f = MOI.VectorAffineFunction{T}(vec(terms), -b)
MOI.add_constraint(optimizer, f, MOI.Nonpositives(size(A, 1)))
MathOptInterface.UnsupportedConstraint{MathOptInterface.VectorAffineFunction{Float64},MathOptInterface.Nonpositives}: `MathOptInterface.VectorAffineFunction{Float64}`-in-`MathOptInterface.Nonpositives` constraints is not supported by the model.

@blegat
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blegat commented May 14, 2019

You should do optimizer = MOI.Bridges.full_bridge_optimiezr(CDDLib.Optimizer{T}(), T)

oyamad added a commit to oyamad/Polyhedra.jl that referenced this issue May 15, 2019
@oyamad
Document how to use MOI.VectorAffineFunction
aadada4 
Close JuliaPolyhedra#170
@oyamad oyamad linked a pull request May 15, 2019 that will close this issue
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@oyamad
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oyamad commented May 15, 2019

Great, that works now.

In PR #171 I added a description of the usage to the documentation.

oyamad added a commit to oyamad/Polyhedra.jl that referenced this issue May 16, 2019
@oyamad
Document how to use MOI.VectorAffineFunction
fef7019 
Close JuliaPolyhedra#170
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Document how to use MOI.VectorAffineFunction oyamad/Polyhedra.jl
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@blegat @oyamad