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nlp.jl
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nlp.jl
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const VI = MOI.VariableIndex
struct HS071 <: MOI.AbstractNLPEvaluator
enable_hessian::Bool
end
# hs071
# min x1 * x4 * (x1 + x2 + x3) + x3
# st x1 * x2 * x3 * x4 >= 25
# x1^2 + x2^2 + x3^2 + x4^2 = 40
# 1 <= x1, x2, x3, x4 <= 5
# Start at (1,5,5,1)
# End at (1.000..., 4.743..., 3.821..., 1.379...)
function MOI.initialize(d::HS071, requested_features::Vector{Symbol})
for feat in requested_features
if !(feat in MOI.features_available(d))
error("Unsupported feature $feat")
# TODO: implement Jac-vec and Hess-vec products
# for solvers that need them
end
end
end
function MOI.features_available(d::HS071)
if d.enable_hessian
return [:Grad, :Jac, :Hess, :ExprGraph]
else
return [:Grad, :Jac, :ExprGraph]
end
end
MOI.objective_expr(d::HS071) = :(x[$(VI(1))] * x[$(VI(4))] * (x[$(VI(1))] +
x[$(VI(2))] + x[$(VI(3))]) + x[$(VI(3))])
function MOI.constraint_expr(d::HS071, i::Int)
if i == 1
return :(x[$(VI(1))] * x[$(VI(2))] * x[$(VI(3))] * x[$(VI(4))] >= 25.0)
elseif i == 2
return :(x[$(VI(1))]^2 + x[$(VI(2))]^2 +
x[$(VI(3))]^2 + x[$(VI(4))]^2 == 40.0)
else
error("Out of bounds constraint.")
end
end
MOI.eval_objective(d::HS071, x) = x[1] * x[4] * (x[1] + x[2] + x[3]) + x[3]
function MOI.eval_constraint(d::HS071, g, x)
g[1] = x[1] * x[2] * x[3] * x[4]
g[2] = x[1]^2 + x[2]^2 + x[3]^2 + x[4]^2
end
function MOI.eval_objective_gradient(d::HS071, grad_f, x)
grad_f[1] = x[1] * x[4] + x[4] * (x[1] + x[2] + x[3])
grad_f[2] = x[1] * x[4]
grad_f[3] = x[1] * x[4] + 1
grad_f[4] = x[1] * (x[1] + x[2] + x[3])
end
function MOI.jacobian_structure(d::HS071)
return Tuple{Int64,Int64}[(1,1), (1,2), (1,3), (1,4), (2,1), (2,2),
(2,3), (2,4)]
end
# lower triangle only
function MOI.hessian_lagrangian_structure(d::HS071)
@assert d.enable_hessian
return Tuple{Int64,Int64}[(1,1), (2,1), (2,2), (3,1), (3,2), (3,3),
(4,1), (4,2), (4,3), (4,4)]
end
function MOI.eval_constraint_jacobian(d::HS071, J, x)
# Constraint (row) 1
J[1] = x[2]*x[3]*x[4] # 1,1
J[2] = x[1]*x[3]*x[4] # 1,2
J[3] = x[1]*x[2]*x[4] # 1,3
J[4] = x[1]*x[2]*x[3] # 1,4
# Constraint (row) 2
J[5] = 2*x[1] # 2,1
J[6] = 2*x[2] # 2,2
J[7] = 2*x[3] # 2,3
J[8] = 2*x[4] # 2,4
end
function MOI.eval_hessian_lagrangian(d::HS071, H, x, σ, μ)
@assert d.enable_hessian
# Again, only lower left triangle
# Objective
H[1] = σ * (2*x[4]) # 1,1
H[2] = σ * ( x[4]) # 2,1
H[3] = 0 # 2,2
H[4] = σ * ( x[4]) # 3,1
H[5] = 0 # 3,2
H[6] = 0 # 3,3
H[7] = σ* (2*x[1] + x[2] + x[3]) # 4,1
H[8] = σ * ( x[1]) # 4,2
H[9] = σ * ( x[1]) # 4,3
H[10] = 0 # 4,4
# First constraint
H[2] += μ[1] * (x[3] * x[4]) # 2,1
H[4] += μ[1] * (x[2] * x[4]) # 3,1
H[5] += μ[1] * (x[1] * x[4]) # 3,2
H[7] += μ[1] * (x[2] * x[3]) # 4,1
H[8] += μ[1] * (x[1] * x[3]) # 4,2
H[9] += μ[1] * (x[1] * x[2]) # 4,3
# Second constraint
H[1] += μ[2] * 2 # 1,1
H[3] += μ[2] * 2 # 2,2
H[6] += μ[2] * 2 # 3,3
H[10] += μ[2] * 2 # 4,4
end
function hs071test_template(model::MOI.ModelLike, config::TestConfig, evaluator::HS071)
atol = config.atol
rtol = config.rtol
@test MOI.supports(model, MOI.NLPBlock())
@test MOI.supports_constraint(model, MOI.SingleVariable, MOI.LessThan{Float64})
@test MOI.supports_constraint(model, MOI.SingleVariable, MOI.GreaterThan{Float64})
@test MOI.supports(model, MOI.VariablePrimalStart(), MOI.VariableIndex)
MOI.empty!(model)
@test MOI.is_empty(model)
lb = [25.0, 40.0]
ub = [Inf, 40.0]
block_data = MOI.NLPBlockData(MOI.NLPBoundsPair.(lb, ub), evaluator, true)
v = MOI.add_variables(model, 4)
@test MOI.get(model, MOI.NumberOfVariables()) == 4
l = [1.0,1.0,1.0,1.0]
u = [5.0,5.0,5.0,5.0]
start = [1,5,5,1]
for i in 1:4
cub = MOI.add_constraint(model, MOI.SingleVariable(v[i]), MOI.LessThan(u[i]))
# We test this after the creation of every `SingleVariable` constraint
# to ensure a good coverage of corner cases.
@test cub.value == v[i].value
clb = MOI.add_constraint(model, MOI.SingleVariable(v[i]), MOI.GreaterThan(l[i]))
@test clb.value == v[i].value
MOI.set(model, MOI.VariablePrimalStart(), v[i], start[i])
end
MOI.set(model, MOI.NLPBlock(), block_data)
MOI.set(model, MOI.ObjectiveSense(), MOI.MIN_SENSE)
# TODO: config.query tests
if config.solve
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test MOI.get(model, MOI.ObjectiveValue()) ≈ 17.014017145179164 atol=atol rtol=rtol
optimal_v = [1.0, 4.7429996418092970, 3.8211499817883077, 1.3794082897556983]
@test MOI.get(model, MOI.VariablePrimal(), v) ≈ optimal_v atol=atol rtol=rtol
# TODO: Duals? Maybe better to test on a convex instance.
end
end
hs071_test(model, config) = hs071test_template(model, config, HS071(true))
hs071_no_hessian_test(model, config) = hs071test_template(model, config, HS071(false))
# Test for FEASIBILITY_SENSE.
# Find x satisfying x^2 == 1.
struct FeasibilitySenseEvaluator <: MOI.AbstractNLPEvaluator
enable_hessian::Bool
end
function MOI.initialize(d::FeasibilitySenseEvaluator,
requested_features::Vector{Symbol})
for feat in requested_features
if !(feat in MOI.features_available(d))
error("Unsupported feature $feat")
# TODO: implement Jac-vec and Hess-vec products
# for solvers that need them
end
end
end
function MOI.features_available(d::FeasibilitySenseEvaluator)
if d.enable_hessian
return [:Grad, :Jac, :Hess, :ExprGraph]
else
return [:Grad, :Jac, :ExprGraph]
end
end
MOI.objective_expr(d::FeasibilitySenseEvaluator) = :()
function MOI.constraint_expr(d::FeasibilitySenseEvaluator, i::Int)
if i == 1
return :(x[$(VI(1))]^2 == 1)
else
error("Out of bounds constraint.")
end
end
MOI.eval_objective(d::FeasibilitySenseEvaluator, x) = 0.0
function MOI.eval_constraint(d::FeasibilitySenseEvaluator, g, x)
g[1] = x[1]^2
end
function MOI.eval_objective_gradient(d::FeasibilitySenseEvaluator, grad_f, x)
grad_f[1] = 0.0
end
function MOI.jacobian_structure(d::FeasibilitySenseEvaluator)
return Tuple{Int64,Int64}[(1,1)]
end
function MOI.hessian_lagrangian_structure(d::FeasibilitySenseEvaluator)
@assert d.enable_hessian
return Tuple{Int64,Int64}[(1,1)]
end
function MOI.eval_constraint_jacobian(d::FeasibilitySenseEvaluator, J, x)
J[1] = 2x[1]
end
function MOI.eval_hessian_lagrangian(d::FeasibilitySenseEvaluator, H, x, σ, μ)
@assert d.enable_hessian
H[1] = 2μ[1] # 1,1
end
function feasibility_sense_test_template(model::MOI.ModelLike,
config::TestConfig,
set_has_objective::Bool,
evaluator::FeasibilitySenseEvaluator)
atol = config.atol
rtol = config.rtol
@test MOI.supports(model, MOI.NLPBlock())
@test MOI.supports(model, MOI.VariablePrimalStart(), MOI.VariableIndex)
MOI.empty!(model)
@test MOI.is_empty(model)
lb = [1.0]
ub = [1.0]
block_data = MOI.NLPBlockData(MOI.NLPBoundsPair.(lb, ub), evaluator,
set_has_objective)
x = MOI.add_variable(model)
@test MOI.get(model, MOI.NumberOfVariables()) == 1
# Avoid starting at zero because it's a critial point.
MOI.set(model, MOI.VariablePrimalStart(), x, 1.5)
MOI.set(model, MOI.NLPBlock(), block_data)
MOI.set(model, MOI.ObjectiveSense(), MOI.FEASIBILITY_SENSE)
# TODO: config.query tests
if config.solve
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test MOI.get(model, MOI.ObjectiveValue()) ≈ 0.0 atol=atol rtol=rtol
@test abs(MOI.get(model, MOI.VariablePrimal(), x)) ≈ 1.0 atol=atol rtol=rtol
end
end
function feasibility_sense_with_objective_and_hessian_test(model, config)
feasibility_sense_test_template(model, config, true,
FeasibilitySenseEvaluator(true))
end
function feasibility_sense_with_objective_and_no_hessian_test(model, config)
feasibility_sense_test_template(model, config, true,
FeasibilitySenseEvaluator(false))
end
function feasibility_sense_with_no_objective_and_with_hessian_test(model, config)
feasibility_sense_test_template(model, config, false,
FeasibilitySenseEvaluator(true))
end
function feasibility_sense_with_no_objective_and_no_hessian_test(model, config)
feasibility_sense_test_template(model, config, false,
FeasibilitySenseEvaluator(false))
end
function nlp_objective_and_moi_objective_test(model::MOI.ModelLike,
config::TestConfig)
atol = config.atol
rtol = config.rtol
@test MOI.supports(model, MOI.NLPBlock())
@test MOI.supports(model, MOI.VariablePrimalStart(), MOI.VariableIndex)
MOI.empty!(model)
@test MOI.is_empty(model)
lb = [1.0]
ub = [2.0]
block_data = MOI.NLPBlockData(MOI.NLPBoundsPair.(lb, ub),
FeasibilitySenseEvaluator(true), true)
x = MOI.add_variable(model)
@test MOI.get(model, MOI.NumberOfVariables()) == 1
# Avoid starting at zero because it's a critial point.
MOI.set(model, MOI.VariablePrimalStart(), x, 1.5)
MOI.set(model, MOI.NLPBlock(), block_data)
MOI.set(model, MOI.ObjectiveSense(), MOI.MAX_SENSE)
f_x = MOI.ScalarAffineFunction([MOI.ScalarAffineTerm(1.0, x)], 0.0)
# This objective function should be ignored.
MOI.set(model, MOI.ObjectiveFunction{typeof(f_x)}(), f_x)
# TODO: config.query tests
if config.solve
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
@test MOI.get(model, MOI.ResultCount()) >= 1
@test MOI.get(model, MOI.PrimalStatus()) == MOI.FEASIBLE_POINT
@test MOI.get(model, MOI.ObjectiveValue()) ≈ 0.0 atol=atol rtol=rtol
@test 1.0 ≤ MOI.get(model, MOI.VariablePrimal(), x) ≤ 2.0
end
end
const nlptests = Dict("hs071" => hs071_test,
"hs071_no_hessian" => hs071_no_hessian_test,
"feasibility_sense_with_objective_and_hessian" =>
feasibility_sense_with_objective_and_hessian_test,
"feasibility_sense_with_objective_and_no_hessian" =>
feasibility_sense_with_objective_and_no_hessian_test,
"feasibility_sense_with_no_objective_and_with_hessian" =>
feasibility_sense_with_no_objective_and_with_hessian_test,
"feasibility_sense_with_no_objective_and_no_hessian" =>
feasibility_sense_with_no_objective_and_no_hessian_test,
"nlp_objective_and_moi_objective" =>
nlp_objective_and_moi_objective_test
)
@moitestset nlp
"""
test_linear_mixed_complementarity(model::MOI.ModelLike, config::TestConfig)
Test the solution of the linear mixed-complementarity problem:
`F(x) complements x`, where `F(x) = M * x .+ q` and `0 <= x <= 10`.
"""
function test_linear_mixed_complementarity(model::MOI.ModelLike, config::TestConfig)
MOI.empty!(model)
x = MOI.add_variables(model, 4)
MOI.add_constraint.(model, MOI.SingleVariable.(x), MOI.Interval(0.0, 10.0))
MOI.set.(model, MOI.VariablePrimalStart(), x, 0.0)
M = Float64[0 0 -1 -1; 0 0 1 -2; 1 -1 2 -2; 1 2 -2 4]
q = [2; 2; -2; -6]
terms = MOI.VectorAffineTerm{Float64}[]
for i = 1:4
push!(
terms,
MOI.VectorAffineTerm(4 + i, MOI.ScalarAffineTerm(1.0, x[i]))
)
for j = 1:4
iszero(M[i, j]) && continue
push!(
terms,
MOI.VectorAffineTerm(i, MOI.ScalarAffineTerm(M[i, j], x[j]))
)
end
end
MOI.add_constraint(
model,
MOI.VectorAffineFunction(terms, [q; 0.0; 0.0; 0.0; 0.0]),
MOI.Complements(4)
)
@test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
if config.solve
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
x_val = MOI.get.(model, MOI.VariablePrimal(), x)
@test isapprox(
x_val, [2.8, 0.0, 0.8, 1.2], atol = config.atol, rtol = config.rtol
)
end
end
const mixed_complementaritytests = Dict(
"test_linear_mixed_complementarity" => test_linear_mixed_complementarity,
)
@moitestset mixed_complementarity
"""
test_qp_complementarity_constraint(model::MOI.ModelLike, config::TestConfig)
Test the solution of the quadratic program with complementarity constraints:
```
min (x0 - 5)^2 +(2 x1 + 1)^2
s.t. -1.5 x0 + 2 x1 + x2 - 0.5 x3 + x4 = 2
x2 complements(3 x0 - x1 - 3)
x3 complements(-x0 + 0.5 x1 + 4)
x4 complements(-x0 - x1 + 7)
x0, x1, x2, x3, x4 >= 0
```
which rewrites, with auxiliary variables
```
min (x0 - 5)^2 +(2 x1 + 1)^2
s.t. -1.5 x0 + 2 x1 + x2 - 0.5 x3 + x4 = 2 (cf1)
3 x0 - x1 - 3 - x5 = 0 (cf2)
-x0 + 0.5 x1 + 4 - x6 = 0 (cf3)
-x0 - x1 + 7 - x7 = 0 (cf4)
x2 complements x5
x3 complements x6
x4 complements x7
x0, x1, x2, x3, x4, x5, x6, x7 >= 0
```
"""
function test_qp_complementarity_constraint(
model::MOI.ModelLike, config::TestConfig
)
MOI.empty!(model)
x = MOI.add_variables(model, 8)
MOI.set.(model, MOI.VariablePrimalStart(), x, 0.0)
MOI.add_constraint.(model, MOI.SingleVariable.(x), MOI.GreaterThan(0.0))
MOI.set(
model,
MOI.ObjectiveFunction{MOI.ScalarQuadraticFunction{Float64}}(),
MOI.ScalarQuadraticFunction(
MOI.ScalarAffineTerm.([-10.0, 4.0], x[[1, 2]]),
MOI.ScalarQuadraticTerm.([2.0, 8.0], x[1:2], x[1:2]),
26.0
)
)
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.([-1.5, 2.0, 1.0, 0.5, 1.0], x[1:5]), 0.0
),
MOI.EqualTo(2.0)
)
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.([3.0, -1.0, -1.0], x[[1, 2, 6]]), 0.0
),
MOI.EqualTo(3.0)
)
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.([-1.0, 0.5, -1.0], x[[1, 2, 7]]), 0.0
),
MOI.EqualTo(-4.0)
)
MOI.add_constraint(
model,
MOI.ScalarAffineFunction(
MOI.ScalarAffineTerm.(-1.0, x[[1, 2, 8]]), 0.0
),
MOI.EqualTo(-7.0)
)
MOI.add_constraint(
model,
MOI.VectorOfVariables([x[3], x[4], x[5], x[6], x[7], x[8]]),
MOI.Complements(3)
)
@test MOI.get(model, MOI.TerminationStatus()) == MOI.OPTIMIZE_NOT_CALLED
if config.solve
MOI.optimize!(model)
@test MOI.get(model, MOI.TerminationStatus()) == config.optimal_status
x_val = MOI.get.(model, MOI.VariablePrimal(), x)
@test isapprox(
x_val,
[1.0, 0.0, 3.5, 0.0, 0.0, 0.0, 3.0, 6.0],
atol = config.atol,
rtol = config.rtol
)
@test isapprox(
MOI.get(model, MOI.ObjectiveValue()),
17.0,
atol = config.atol,
rtol = config.rtol
)
end
end
const math_program_complementarity_constraintstests = Dict(
"test_qp_complementarity_constraint" => test_qp_complementarity_constraint,
)
@moitestset math_program_complementarity_constraints