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Implement value for GenericQuadExpr. #1588

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Nov 8, 2018
Merged

Implement value for GenericQuadExpr. #1588

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899886a
Implement value for GenericQuadExpr.
odow Nov 7, 2018
3f88a6d
Respond to @blegat's comments
odow Nov 7, 2018
9a069fe
[ci skip] clarify comment
odow Nov 7, 2018
c4dd6ce
[ci skip] clarify comment redux.
odow Nov 7, 2018
4a56ac4
foo -> value_func
odow Nov 8, 2018
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18 changes: 17 additions & 1 deletion src/quad_expr.jl
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Expand Up @@ -232,4 +232,20 @@ function Base.copy(q::GenericQuadExpr, new_model::Model)
copy(q.qcoeffs), copy(q.aff, new_model))
end

# TODO: result_value for QuadExpr
# Requires that foo(::GenericAffExpr) is defined.
function value(ex::GenericQuadExpr{CoefType, VarType},
foo::Function) where {CoefType, VarType}
# The return type of appling map to ::VarType.
MapVarType = Base.promote_op(foo, VarType)
# Later, we're going to multiply two MapVarType together
MapVarType2 = Base.promote_op(*, MapVarType, MapVarType)
# We're also going to multiply a constant with ::MapVarType2
RetType = Base.promote_op(*, CoefType, MapVarType2)
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I was once told that it is not safe to call promote_op on top of promote_op, see JuliaLang/julia#18218 (comment).
So maybe something like RetType = Base.promote_op((c, v) -> c * foo(v) * foo(v), CoefType, VarType) is better.

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I just followed
https://github.com/JuliaOpt/JuMP.jl/blob/6daac7ae3105d1c2ddcd44a1324acd3769fcef64/src/aff_expr.jl#L105-L113

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I never understood the argument so I am not surprised to see that I am not following it :-P
In fact, it appears that using promote_op is just bad: JuliaLang/julia#26344 (comment)
So the best way is just to play with typeof, zero, one and oneunit. To choose whether to use one or oneunit you need to think what would happen if the type was frim Unitful. For choosing between zero and one, you have some freedom but you need to avoid dividing by zero.
Maybe the best way here is doing typeof(oneunit(CoefType) * oneunit(MapVarType) * oneunit(MapVarType)). We are kind of forced to use promote_op to get MapVarType since one ... would on JuMP.VariableRef would not really do what we want.

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I think we have to use promote_op here because the variables get passed through foo...

Alternatively, we just avoid the value result_value misdirection and write a result_value function knowing that it will get a Float64 back...

ret = convert(RetType, foo(ex.aff))
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for (vars, coef) in ex.terms
ret += coef * foo(vars.a) * foo(vars.b)
end
return ret
end

JuMP.result_value(ex::JuMP.GenericQuadExpr) = value(ex, JuMP.result_value)
13 changes: 13 additions & 0 deletions test/expr.jl
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Expand Up @@ -42,6 +42,19 @@ function expressions_test(ModelType::Type{<:JuMP.AbstractModel}, VariableRefType
@test @inferred(JuMP.value(expr2, i -> 1.0)) == 2.0
end

@testset "value for GenericQuadExpr" begin
# 1 + 2x(1) + 3x(2)
affine_term = JuMP.GenericAffExpr(1.0, 1 => 2.0, 2 => 3.0)
# 1 + 2x(1) + 3x(2) + 4x(1)^2 + 5x(1)*x(2) + 6x(2)^2
expr = JuMP.GenericQuadExpr(affine_term,
JuMP.UnorderedPair(1, 1) => 4.0,
JuMP.UnorderedPair(1, 2) => 5.0,
JuMP.UnorderedPair(2, 2) => 6.0)
@test typeof(@inferred(JuMP.value(expr, i -> 1.0))) == Float64
@test @inferred(JuMP.value(expr, i -> 1.0)) == 16
@test @inferred(JuMP.value(expr, i -> 2.0)) == 62
end

@testset "add_to_expression!(::GenericAffExpr{C,V}, ::V)" begin
aff = JuMP.GenericAffExpr(1.0, :a => 2.0)
@test JuMP.isequal_canonical(JuMP.add_to_expression!(aff, :b),
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2 changes: 2 additions & 0 deletions test/generate_and_solve.jl
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Expand Up @@ -220,6 +220,8 @@
@test JuMP.result_dual(c1) == -1.0
@test JuMP.result_dual(c2) == 2.0
@test JuMP.result_dual(c3) == 3.0

@test JuMP.result_value(2 * x + 3 * y * x) == 2.0
end

@testset "SOC" begin
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