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Add support for MOI.ScalarNonlinearFunction
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@DimitriAlston
DimitriAlston committed Oct 8, 2024
1 parent 69fae3c commit 236846a
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30 changes: 13 additions & 17 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -71,33 +71,29 @@ This model can be formulated using JuMP code as:
```julia
using JuMP, EAGO

m = Model(EAGO.Optimizer)
model = Model(EAGO.Optimizer)

# Define bounded variables
xL = [10.0; 0.0; 0.0; 0.0; 0.0; 85.0; 90.0; 3.0; 1.2; 145.0]
xU = [2000.0; 16000.0; 120.0; 5000.0; 2000.0; 93.0; 95.0; 12.0; 4.0; 162.0]
@variable(m, xL[i] <= x[i=1:10] <= xU[i])
@variable(model, xL[i] <= x[i=1:10] <= xU[i])

# Define nonlinear constraints
@NLconstraint(m, e1, -x[1]*(1.12 + 0.13167*x[8] - 0.00667*(x[8])^2) + x[4] == 0.0)
@NLconstraint(m, e3, -0.001*x[4]*x[9]*x[6]/(98.0 - x[6]) + x[3] == 0.0)
@NLconstraint(m, e4, -(1.098*x[8] - 0.038*(x[8])^2) - 0.325*x[6] + x[7] == 57.425)
@NLconstraint(m, e5, -(x[2] + x[5])/x[1] + x[8] == 0.0)
# Define constraints
@constraint(model, e1, -x[1]*(1.12 + 0.13167*x[8] - 0.00667*(x[8])^2) + x[4] == 0.0)
@constraint(model, e2, -x[1] + 1.22*x[4] - x[5] == 0.0)
@constraint(model, e3, -0.001*x[4]*x[9]*x[6]/(98.0 - x[6]) + x[3] == 0.0)
@constraint(model, e4, -(1.098*x[8] - 0.038*(x[8])^2) - 0.325*x[6] + x[7] == 57.425)
@constraint(model, e5, -(x[2] + x[5])/x[1] + x[8] == 0.0)
@constraint(model, e6, x[9] + 0.222*x[10] == 35.82)
@constraint(model, e7, -3.0*x[7] + x[10] == -133.0)

# Define linear constraints
@constraint(m, e2, -x[1] + 1.22*x[4] - x[5] == 0.0)
@constraint(m, e6, x[9] + 0.222*x[10] == 35.82)
@constraint(m, e7, -3.0*x[7] + x[10] == -133.0)

# Define nonlinear objective
@NLobjective(m, Max, 0.063*x[4]*x[7] - 5.04*x[1] - 0.035*x[2] - 10*x[3] - 3.36*x[5])
# Define objective
@objective(model, Max, 0.063*x[4]*x[7] - 5.04*x[1] - 0.035*x[2] - 10*x[3] - 3.36*x[5])

# Solve the optimization problem
JuMP.optimize!(m)
JuMP.optimize!(model)
```

Special handling has been included for linear/quadratic functions defined using the `@constraint` macro in JuMP and these can generally be expected to perform better than specifying quadratic or linear terms with the `@NLconstraint` macro.

## A Cautionary Note on Global Optimization

As a global optimization platform, EAGO's solvers can be used to find solutions of general nonconvex problems with a guaranteed certificate of optimality. However, global solvers suffer from the curse of dimensionality and therefore their performance is outstripped by convex/local solvers. For users interested in large-scale applications, be warned that problems generally larger than a few variables may prove challenging for certain types of global optimization problems.
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55 changes: 46 additions & 9 deletions src/eago_optimizer/moi_wrapper.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ function check_inbounds!(m::Optimizer, vi::VI)
end
return nothing
end
check_inbounds!(m::Optimizer, f::Number) = nothing
check_inbounds!(m::Optimizer, f::SAF) = foreach(x -> check_inbounds!(m, x.variable), f.terms)
check_inbounds!(m::Optimizer, f::VECOFVAR) = foreach(x -> check_inbounds!(m, x), f.variables)
function check_inbounds!(m::Optimizer, f::SQF)
Expand All @@ -31,19 +32,23 @@ function check_inbounds!(m::Optimizer, f::SQF)
end
return nothing
end
function check_inbounds!(m::Optimizer, f::MOI.ScalarNonlinearFunction)
foreach(x -> check_inbounds!(m, x), f.args)
return nothing
end

MOI.supports_constraint(::Optimizer, ::Type{VI}, ::Type{S}) where S <: VAR_SETS = true
MOI.supports_constraint(::Optimizer,::Type{T},::Type{S}) where {T<:Union{SAF,SQF},S<:INEQ_SETS} = true
MOI.supports_constraint(::Optimizer,::Type{T},::Type{S}) where {T<:Union{SAF,SQF,MOI.ScalarNonlinearFunction},S<:INEQ_SETS} = true

MOI.is_valid(m::Optimizer, v::VI) = (1 <= v.value <= m._input_problem._variable_count)
MOI.is_valid(m::Optimizer, c::CI{VI,S}) where S <: VAR_SETS = (1 <= c.value <= m._input_problem._variable_count)
MOI.is_valid(m::Optimizer, c::CI{F,S}) where {F<:Union{SAF,SQF}, S<:INEQ_SETS} = (1 <= c.value <= length(_constraints(m,F,S)))
MOI.is_valid(m::Optimizer, c::CI{F,S}) where {F<:Union{SAF,SQF,MOI.ScalarNonlinearFunction}, S<:INEQ_SETS} = (1 <= c.value <= length(_constraints(m,F,S)))

MOI.get(m::Optimizer, ::MOI.NumberOfConstraints{VI,S}) where S<:VAR_SETS = length(_constraints(m,VI,S))
MOI.get(m::Optimizer, ::MOI.NumberOfConstraints{F,S}) where {F<:Union{SAF,SQF},S<:INEQ_SETS} = length(_constraints(m,F,S))
MOI.get(m::Optimizer, ::MOI.NumberOfConstraints{F,S}) where {F<:Union{SAF,SQF,MOI.ScalarNonlinearFunction},S<:INEQ_SETS} = length(_constraints(m,F,S))

MOI.get(m::Optimizer, ::MOI.ListOfConstraintIndices{VI,S}) where S<:VAR_SETS = collect(keys(_constraints(m,VI,S)))
MOI.get(m::Optimizer, ::MOI.ListOfConstraintIndices{F,S}) where {F<:Union{SAF,SQF},S<:INEQ_SETS} = collect(keys(_constraints(m,F,S)))
MOI.get(m::Optimizer, ::MOI.ListOfConstraintIndices{F,S}) where {F<:Union{SAF,SQF,MOI.ScalarNonlinearFunction},S<:INEQ_SETS} = collect(keys(_constraints(m,F,S)))

function MOI.add_variable(m::Optimizer)
vi = VI(m._input_problem._variable_count += 1)
Expand All @@ -57,10 +62,31 @@ function MOI.add_variable(m::Optimizer, name::String)
return vi
end

function MOI.add_constraint(m::Optimizer, f::F, s::S) where {F<:Union{SAF,SQF},S<:INEQ_SETS}
function MOI.add_constraint(m::Optimizer, f::F, s::S) where {F<:Union{SAF,SQF,MOI.ScalarNonlinearFunction},S<:INEQ_SETS}
check_inbounds!(m, f)
ci = CI{F, S}(m._input_problem._constraint_count += 1)
_constraints(m, F, S)[ci] = (f, s)
if f isa MOI.ScalarNonlinearFunction
if isnothing(m._input_problem._nlp_data)
model = MOI.Nonlinear.Model()
backend = MOI.Nonlinear.SparseReverseMode()
vars = MOI.get(m, MOI.ListOfVariableIndices())
evaluator = MOI.Nonlinear.Evaluator(model, backend, vars)
m._input_problem._nlp_data = MOI.NLPBlockData(evaluator)
end
MOI.Nonlinear.add_constraint(m._input_problem._nlp_data.evaluator.model, f, s)
constraint_bounds = m._input_problem._nlp_data.constraint_bounds
has_objective = m._input_problem._nlp_data.has_objective
if s isa MOI.LessThan
push!(constraint_bounds, MOI.NLPBoundsPair(-Inf, s.upper))
elseif s isa MOI.GreaterThan
push!(constraint_bounds, MOI.NLPBoundsPair(s.lower, Inf))
else
push!(constraint_bounds, MOI.NLPBoundsPair(s.value, s.value))
end
m._input_problem._nlp_data = MOI.NLPBlockData(constraint_bounds, m._input_problem._nlp_data.evaluator, has_objective)
else
_constraints(m, F, S)[ci] = (f, s)
end
return ci
end
function MOI.add_constraint(m::Optimizer, f::VI, s::S) where S<:VAR_SETS
Expand Down Expand Up @@ -230,7 +256,7 @@ MOI.get(m::Optimizer, p::MOI.RawStatusString) = string(m._global_optimizer._end_
#####
MOI.supports(::Optimizer, ::MOI.TimeLimitSec) = true
MOI.supports(::Optimizer, ::MOI.ObjectiveSense) = true
MOI.supports(::Optimizer, ::MOI.ObjectiveFunction{F}) where {F <: Union{VI, SAF, SQF}} = true
MOI.supports(::Optimizer, ::MOI.ObjectiveFunction{F}) where {F <: Union{VI, SAF, SQF, MOI.ScalarNonlinearFunction}} = true

function MOI.set(m::Optimizer, ::MOI.NLPBlock, nlp_data::MOI.NLPBlockData)
if nlp_data.has_objective
Expand All @@ -239,9 +265,20 @@ function MOI.set(m::Optimizer, ::MOI.NLPBlock, nlp_data::MOI.NLPBlockData)
m._input_problem._nlp_data = nlp_data
end

function MOI.set(m::Optimizer, ::MOI.ObjectiveFunction{T}, f::T) where T <: Union{VI,SAF,SQF}
function MOI.set(m::Optimizer, ::MOI.ObjectiveFunction{T}, f::T) where T <: Union{VI,SAF,SQF,MOI.ScalarNonlinearFunction}
check_inbounds!(m, f)
m._input_problem._objective = f
if f isa MOI.ScalarNonlinearFunction
if isnothing(m._input_problem._nlp_data)
model = MOI.Nonlinear.Model()
backend = MOI.Nonlinear.SparseReverseMode()
vars = MOI.get(m, MOI.ListOfVariableIndices())
evaluator = MOI.Nonlinear.Evaluator(model, backend, vars)
m._input_problem._nlp_data = MOI.NLPBlockData(evaluator)
end
MOI.Nonlinear.set_objective(m._input_problem._nlp_data.evaluator.model, f)
else
m._input_problem._objective = f
end
end
MOI.get(m::Optimizer, ::MOI.ObjectiveFunction{T}) where T <: Union{VI,SAF,SQF} = m._input_problem._objective
MOI.get(m::Optimizer, ::MOI.ObjectiveFunctionType) = typeof(m._input_problem._objective)
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35 changes: 28 additions & 7 deletions test/moit_tests.jl
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Expand Up @@ -73,25 +73,46 @@ function test_runtests()
"test_conic_NormOneCone_VectorAffineFunction",
"test_conic_NormOneCone_VectorOfVariables",
"test_conic_linear_VectorAffineFunction",
"test_cpsat_Circuit",
"test_cpsat_CountAtLeast",
"test_cpsat_Table",
"test_linear_Semicontinuous_integration",
"test_linear_Semiinteger_integration",
"test_linear_integer_solve_twice",
"test_linear_integration",
"test_quadratic_duplicate_terms",
"test_quadratic_integration",
"test_quadratic_nonhomogeneous",
"test_quadratic_constraint_LessThan",
"test_quadratic_constraint_GreaterThan",
"test_modification_affine_deletion_edge_cases",
"test_solve_SOS2_add_and_delete",
# MOI bridge issues
"test_basic_VectorNonlinearFunction_AllDifferent",
"test_basic_VectorNonlinearFunction_BinPacking",
"test_basic_VectorNonlinearFunction_Circuit",
"test_basic_VectorNonlinearFunction_Complements",
"test_basic_VectorNonlinearFunction_CountAtLeast",
"test_basic_VectorNonlinearFunction_CountBelongs",
"test_basic_VectorNonlinearFunction_CountDistinct",
"test_basic_VectorNonlinearFunction_CountGreaterThan",
"test_basic_VectorNonlinearFunction_GeometricMeanCone",
"test_basic_VectorNonlinearFunction_HyperRectangle",
"test_basic_VectorNonlinearFunction_Nonnegatives",
"test_basic_VectorNonlinearFunction_Nonpositives",
"test_basic_VectorNonlinearFunction_NormInfinityCone",
"test_basic_VectorNonlinearFunction_NormOneCone",
"test_basic_VectorNonlinearFunction_RotatedSecondOrderCone",
"test_basic_VectorNonlinearFunction_SecondOrderCone",
"test_basic_VectorNonlinearFunction_SOS1",
"test_basic_VectorNonlinearFunction_SOS2",
"test_basic_VectorNonlinearFunction_Table",
"test_basic_VectorNonlinearFunction_Zeros",
# TODO: bug related to unbounded_check
"test_nonlinear_expression_quartic",
# TODO: bug related to reform_epigraph_min
"test_nonlinear_expression_overrides_objective",
# Remove after MOI v1.31.3
"test_nonlinear_expression_hs110",
# TODO: wrong error thrown. Likely (trivial) bug in MOI wrapper
"test_model_LowerBoundAlreadySet",
"test_model_UpperBoundAlreadySet",
],
exclude_tests_after = v"1.22.0",
exclude_tests_after = v"1.31.2",
)
return
end
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