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[WIP] Move tests to new JuMP nonlinear interface #236

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2 changes: 1 addition & 1 deletion test/nlp_problems/brownden.jl
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Expand Up @@ -4,7 +4,7 @@ function brownden()
@variable(nlp, x[1:4])
set_start_value.(x, [25.0; 5.0; -5.0; -1.0])

@NLobjective(
@objective(
nlp,
Min,
sum(
Expand Down
10 changes: 5 additions & 5 deletions test/nlp_problems/hs100.jl
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Expand Up @@ -3,12 +3,12 @@ function hs100(args...; kwargs...)
x0 = [1, 2, 0, 4, 0, 1, 1]
@variable(nlp, x[i = 1:7], start = x0[i])

@NLconstraint(nlp, 127 - 2 * x[1]^2 - 3 * x[2]^4 - x[3] - 4 * x[4]^2 - 5 * x[5] ≥ 0)
@constraint(nlp, 127 - 2 * x[1]^2 - 3 * x[2]^4 - x[3] - 4 * x[4]^2 - 5 * x[5] ≥ 0)
@constraint(nlp, 282 - 7 * x[1] - 3 * x[2] - 10 * x[3]^2 - x[4] + x[5] ≥ 0)
@constraint(nlp, -196 + 23 * x[1] + x[2]^2 + 6 * x[6]^2 - 8 * x[7] ≤ 0)
@constraint(nlp, -4 * x[1]^2 - x[2]^2 + 3 * x[1] * x[2] - 2 * x[3]^2 - 5 * x[6] + 11 * x[7] ≥ 0)

@NLobjective(
@objective(
nlp,
Min,
(x[1] - 10)^2 +
Expand All @@ -29,8 +29,8 @@ function hs100_oracle(args...; kwargs...)
x0 = [1, 2, 0, 4, 0, 1, 1]
@variable(model, x[i = 1:7], start = x0[i])

# 1st constraint: keep as NLconstraint
@NLconstraint(model, 127 - 2 * x[1]^2 - 3 * x[2]^4 - x[3] - 4 * x[4]^2 - 5 * x[5] ≥ 0)
# 1st constraint: keep as constraint
@constraint(model, 127 - 2 * x[1]^2 - 3 * x[2]^4 - x[3] - 4 * x[4]^2 - 5 * x[5] ≥ 0)

# 2nd constraint as oracle:
# Original: 282 - 7x1 - 3x2 - 10x3^2 - x4 + x5 ≥ 0
Expand Down Expand Up @@ -94,7 +94,7 @@ function hs100_oracle(args...; kwargs...)
@constraint(model, -4 * x[1]^2 - x[2]^2 + 3 * x[1] * x[2] - 2 * x[3]^2 - 5 * x[6] + 11 * x[7] ≥ 0)

# Objective: same as original
@NLobjective(
@objective(
model,
Min,
(x[1] - 10)^2 +
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4 changes: 2 additions & 2 deletions test/nlp_problems/hs13.jl
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Expand Up @@ -3,9 +3,9 @@ function hs13(args...; kwargs...)
nlp = Model()
@variable(nlp, x[i = 1:2] ≥ 0, start = -2)

@NLobjective(nlp, Min, (x[1] - 2)^2 + x[2]^2)
@objective(nlp, Min, (x[1] - 2)^2 + x[2]^2)

@NLconstraint(nlp, (1 - x[1])^3 ≥ x[2])
@constraint(nlp, (1 - x[1])^3 ≥ x[2])

return nlp
end
4 changes: 2 additions & 2 deletions test/nlp_problems/hs219.jl
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Expand Up @@ -4,9 +4,9 @@ function hs219(args...; kwargs...)
@variable(nlp, x[i = 1:4], start = x0[i])

@constraint(nlp, x[1]^2 - x[2] - x[4]^2 == 0)
@NLconstraint(nlp, x[2] - x[1]^3 - x[3]^2 == 0)
@constraint(nlp, x[2] - x[1]^3 - x[3]^2 == 0)

@NLobjective(nlp, Min, -x[1])
@objective(nlp, Min, -x[1])

return nlp
end
2 changes: 1 addition & 1 deletion test/nlp_problems/hs5.jl
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Expand Up @@ -6,7 +6,7 @@ function hs5()
u = [4, 3]
@variable(nlp, l[i] ≤ x[i = 1:2] ≤ u[i], start = 0.0)

@NLobjective(nlp, Min, sin(x[1] + x[2]) + (x[1] - x[2])^2 - 1.5 * x[1] + 2.5 * x[2] + 1)
@objective(nlp, Min, sin(x[1] + x[2]) + (x[1] - x[2])^2 - 1.5 * x[1] + 2.5 * x[2] + 1)

return nlp
end
2 changes: 1 addition & 1 deletion test/nlp_problems/hs6.jl
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Expand Up @@ -6,7 +6,7 @@ function hs6()
set_start_value(x[1], -1.2)
set_start_value(x[2], 1.0)

@NLobjective(nlp, Min, (1 - x[1])^2)
@objective(nlp, Min, (1 - x[1])^2)

@constraint(nlp, 10 * (x[2] - x[1]^2) == 0)

Expand Down
4 changes: 2 additions & 2 deletions test/nlp_problems/hs61.jl
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Expand Up @@ -6,7 +6,7 @@ function hs61(args...; kwargs...)
@constraint(nlp, 3 * x[1] - 2 * x[2]^2 - 7 == 0)
@constraint(nlp, 4 * x[1] - x[3]^2 - 11 == 0)

@NLobjective(nlp, Min, 4 * x[1]^2 + 2 * x[2]^2 + 2 * x[3]^2 - 33 * x[1] + 16 * x[2] - 24 * x[3])
@objective(nlp, Min, 4 * x[1]^2 + 2 * x[2]^2 + 2 * x[3]^2 - 33 * x[1] + 16 * x[2] - 24 * x[3])

return nlp
end
Expand All @@ -16,7 +16,7 @@ function hs61_oracle(args...; kwargs...)
model = Model()
@variable(model, x[i = 1:3], start = 0)

@NLobjective(model, Min, 4 * x[1]^2 + 2 * x[2]^2 + 2 * x[3]^2 - 33 * x[1] + 16 * x[2] - 24 * x[3])
@objective(model, Min, 4 * x[1]^2 + 2 * x[2]^2 + 2 * x[3]^2 - 33 * x[1] + 16 * x[2] - 24 * x[3])

# First equality: 3*x1 - 2*x2^2 - 7 == 0
# Canonical form for MathOptNLPModel:
Expand Down
2 changes: 1 addition & 1 deletion test/nlp_problems/lincon.jl
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Expand Up @@ -23,7 +23,7 @@ function lincon()

@constraint(nlp, 15.0 * x[15] - 21.0 == 1.0)

@NLobjective(nlp, Min, sum(i + x[i]^4 for i = 1:15))
@objective(nlp, Min, sum(i + x[i]^4 for i = 1:15))

return nlp
end
4 changes: 2 additions & 2 deletions test/nlp_problems/nohesspb.jl
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Expand Up @@ -11,8 +11,8 @@ function nohesspb()
return v
end

register(nlp, :g, 2, g, ∇g)
@NLobjective(nlp, Min, g(x[1], x[2]))
@operator(nlp, custom_g, 2, g, ∇g)
@objective(nlp, Min, custom_g(x[1], x[2]))

return nlp
end
2 changes: 1 addition & 1 deletion test/nls_problems/hs30.jl
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Expand Up @@ -4,7 +4,7 @@ function hs30()
lvar = [1.0; -10.0; -10.0]
@variable(model, lvar[i] ≤ x[i = 1:3] ≤ 10, start = 1.0)
@expression(model, F[i = 1:3], x[i])
@NLconstraint(model, x[1]^2 + x[2]^2 ≥ 1.0)
@constraint(model, x[1]^2 + x[2]^2 ≥ 1.0)

return MathOptNLSModel(model, F, name = "hs30")
end
6 changes: 3 additions & 3 deletions test/nls_problems/hs43.jl
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Expand Up @@ -6,9 +6,9 @@ function hs43()
@expression(model, F2, x[2] - 5 / 2)
@expression(model, F3, sqrt(2) * (x[3] - 21 / 4))
@expression(model, F4, x[4] + 7 / 2)
@NLconstraint(model, 8 - x[1]^2 - x[2]^2 - x[3]^2 - x[4]^2 - x[1] + x[2] - x[3] + x[4] ≥ 0.0)
@NLconstraint(model, 10 - x[1]^2 - 2 * x[2]^2 - x[3]^2 - 2 * x[4]^2 + x[1] + x[4] ≥ 0.0)
@NLconstraint(model, 5 - 2 * x[1]^2 - x[2]^2 - x[3]^2 - 2 * x[1] + x[2] + x[4] ≥ 0.0)
@constraint(model, 8 - x[1]^2 - x[2]^2 - x[3]^2 - x[4]^2 - x[1] + x[2] - x[3] + x[4] ≥ 0.0)
@constraint(model, 10 - x[1]^2 - 2 * x[2]^2 - x[3]^2 - 2 * x[4]^2 + x[1] + x[4] ≥ 0.0)
@constraint(model, 5 - 2 * x[1]^2 - x[2]^2 - x[3]^2 - 2 * x[1] + x[2] + x[4] ≥ 0.0)

return MathOptNLSModel(model, [F1; F2; F3; F4], name = "hs43")
end
2 changes: 1 addition & 1 deletion test/nls_problems/mgh01.jl
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Expand Up @@ -5,7 +5,7 @@ function mgh01()
set_start_value(x[1], -1.2)
set_start_value(x[2], 1.0)

@expression(model, F1, 1 - x[1])
@NLexpression(model, F1, 1 - x[1])
@NLexpression(model, F2, 10 * (x[2] - x[1]^2))

return MathOptNLSModel(model, [F1, F2], name = "mgh01")
Expand Down
7 changes: 4 additions & 3 deletions test/nls_problems/mgh07.jl
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Expand Up @@ -5,11 +5,12 @@ function mgh07()
@variable(nls, x[i = 1:3], start = x0[i])

θ_aux(t) = (t > 0 ? 0.0 : 0.5)
JuMP.register(nls, :θ_aux, 1, θ_aux, autodiff = true)

@operator(nls, θ, 1, θ_aux)

@expression(nls, F1, x[3])
@NLexpression(nls, F2, 10 * (x[3] - 10 * (atan(x[2] / x[1]) / (2 * π) + θ_aux(x[1]))))
@NLexpression(nls, F3, 10 * (sqrt(x[1]^2 + x[2]^2) - 1.0))
@expression(nls, F2, 10 * (x[3] - 10 * (atan(x[2] / x[1]) / (2 * π) + θ(x[1]))))
@expression(nls, F3, 10 * (sqrt(x[1]^2 + x[2]^2) - 1.0))

return MathOptNLSModel(nls, [F1, F2, F3], name = "mgh07")
end
5 changes: 2 additions & 3 deletions test/nls_problems/nlsnohesspb.jl
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Expand Up @@ -7,9 +7,8 @@ function nlsnohesspb()
x₀ = [1.0, 0.1, 0.2, -0.5, 1.0]
@variable(nls, x[i = 1:n], start = x₀[i])

register(nls, :g, n, g, autodiff = true)

@NLexpression(nls, res[i in 1:n], g(x...))
@operator(nls, custom_g, n, g)
@expression(nls, res[i in 1:n], custom_g(x...))

return MathOptNLSModel(nls, res, hessian = false, name = "nlsnohesspb")
end
2 changes: 1 addition & 1 deletion test/nls_problems/nlsqc.jl
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Expand Up @@ -7,7 +7,7 @@ function nlsqc()
@constraint(nls, [x[2], x[2]^2 + x[2] * x[3] + 4.0] in MOI.Nonpositives(2))
@constraint(nls, [x[3], x[3]^2 + x[3] * x[1] + 5.0] in MOI.Zeros(2))

@NLexpression(nls, F[i = 1:3], x[i]^2 - i^2)
@expression(nls, F[i = 1:3], x[i]^2 - i^2)

return MathOptNLSModel(nls, F, name = "nlsqc")
end
16 changes: 8 additions & 8 deletions test/test_moi_nls_model.jl
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Expand Up @@ -31,12 +31,12 @@ println()
println("Testing 2d cat array on NLS")
model = Model()
@variable(model, x[1:2])
@expression(model, F[i = 1:2], x[i] - 1)
@NLexpression(model, F[i = 1:2], x[i] - 1)
@NLexpression(model, G[i = 1:2], x[i]^2 - 1)
@NLexpression(model, H[i = 1:2, j = 1:2], x[i] * x[j] - 1)
@test F isa Array{GenericAffExpr{Float64, VariableRef}}
@test G isa Array{NonlinearExpression}
@test H isa Array{NonlinearExpression}
@test G isa Array{QuadExpr}
@test H isa Array{QuadExpr}
nls = MathOptNLSModel(model, [[F G]; H])
@test all(residual(nls, ones(2)) .== 0.0)
@test jac_residual(nls, ones(2))' * residual(nls, ones(2)) == [0.0; 0.0]
Expand All @@ -60,7 +60,7 @@ nls = MathOptNLSModel(model, F)
# Nonlinear expressions
model = Model()
@variable(model, x[1:2])
@NLexpression(model, F[i = -1:1, j = 1:2], x[j] - i)
@expression(model, F[i = -1:1, j = 1:2], x[j] - i)
@test F isa JuMP.Containers.DenseAxisArray
nls = MathOptNLSModel(model, F)
@test residual(nls, zeros(2)) == [1.0; 0.0; -1.0; 1.0; 0.0; -1.0]
Expand All @@ -80,7 +80,7 @@ nls = MathOptNLSModel(model, F)
model = Model()
@variable(model, x[1:2])
D = Dict(1 => 2, 2 => 4)
@NLexpression(model, F[i = 1:2, j = 1:D[i]], x[i] - j)
@expression(model, F[i = 1:2, j = 1:D[i]], x[i] - j)
@test F isa JuMP.Containers.SparseAxisArray
nls = MathOptNLSModel(model, F)
@test sort(residual(nls, [1.5; 2.5])) == [-1.5; -0.5; -0.5; 0.5; 0.5; 1.5]
Expand All @@ -105,11 +105,11 @@ nls = MathOptNLSModel(model, [F, G])
model = Model()
@variable(model, x[1:4])
D = Dict(1 => 2, 2 => 4)
@NLexpression(model, F[i = 1:2, j = 1:D[i]], x[i] - j)
@NLexpression(model, G[i = -1:1, j = 3:4], x[j] - i)
@expression(model, F[i = 1:2, j = 1:D[i]], x[i] - j)
@expression(model, G[i = -1:1, j = 3:4], x[j] - i)
@test F isa JuMP.Containers.SparseAxisArray
@test G isa JuMP.Containers.DenseAxisArray
@test [F, G] isa Array{<:AbstractArray{NonlinearExpression}}
@test [F, G] isa Array{<:AbstractArray{AffExpr}}
nls = MathOptNLSModel(model, [F, G])
@test sort(residual(nls, [1.5; 2.5; 0.0; 0.0])) ==
[-1.5; -1.0; -1.0; -0.5; -0.5; 0.0; 0.0; 0.5; 0.5; 1.0; 1.0; 1.5]
Expand Down
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