NL.copy_to does not check for attributes

[odow]

MathOptInterface.jl/src/FileFormats/NL/NL.jl

Lines 243 to 430 in 6970f3e

	function MOI.copy_to(
	dest::Model,
	model::MOI.ModelLike;
	copy_names::Bool = false,
	)
	mapping = MOI.Utilities.IndexMap()
	# Initialize the NLP block.
	nlp_block = try
	MOI.get(model, MOI.NLPBlock())
	catch
	MOI.NLPBlockData(MOI.NLPBoundsPair[], _LinearNLPEvaluator(), false)
	end
	if !(:ExprGraph in MOI.features_available(nlp_block.evaluator))
	error(
	"Unable to use AmplNLWriter because the nonlinear evaluator " *
	"does not supply expression graphs.",
	)
	end
	MOI.initialize(nlp_block.evaluator, [:ExprGraph])
	# Objective function.
	if nlp_block.has_objective
	dest.f = _NLExpr(MOI.objective_expr(nlp_block.evaluator))
	else
	F = MOI.get(model, MOI.ObjectiveFunctionType())
	obj = MOI.get(model, MOI.ObjectiveFunction{F}())
	dest.f = _NLExpr(obj)
	end
	# Nonlinear constraints
	for (i, bound) in enumerate(nlp_block.constraint_bounds)
	push!(
	dest.g,
	_NLConstraint(MOI.constraint_expr(nlp_block.evaluator, i), bound),
	)
	end
	dest.nlpblock_dim = length(dest.g)
	starts = MOI.supports(model, MOI.VariablePrimalStart(), MOI.VariableIndex)
	for x in MOI.get(model, MOI.ListOfVariableIndices())
	dest.x[x] = _VariableInfo()
	if starts
	start = MOI.get(model, MOI.VariablePrimalStart(), x)
	MOI.set(dest, MOI.VariablePrimalStart(), x, start)
	end
	mapping[x] = x
	end
	dest.sense = MOI.get(model, MOI.ObjectiveSense())
	resize!(dest.order, length(dest.x))
	# Now deal with the normal MOI constraints.
	for (F, S) in MOI.get(model, MOI.ListOfConstraintTypesPresent())
	_process_constraint(dest, model, F, S, mapping)
	end
	# Correct bounds of binary variables. Mainly because AMPL doesn't have the
	# concept of binary nonlinear variables, but it does have binary linear
	# variables! How annoying.
	for (x, v) in dest.x
	if v.type == _BINARY
	v.lower = max(0.0, v.lower)
	v.upper = min(1.0, v.upper)
	end
	end
	# Jacobian counts. The zero terms for nonlinear constraints should have
	# been added when the expression was constructed.
	for g in dest.g, v in keys(g.expr.linear_terms)
	dest.x[v].jacobian_count += 1
	end
	for h in dest.h, v in keys(h.expr.linear_terms)
	dest.x[v].jacobian_count += 1
	end
	# Now comes the confusing part.
	#
	# AMPL, in all its wisdom, orders variables in a _very_ specific way.
	# The only hint in "Writing NL files" is the line "Variables are ordered as
	# described in Tables 3 and 4 of [5]".
	#
	# Reading these
	#
	# https://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/nlwrite20051130.pdf
	# https://ampl.com/REFS/hooking2.pdf
	#
	# leads us to the following order
	#
	# 1) Continuous variables that appear in a
	# nonlinear objective AND a nonlinear constraint
	# 2) Discrete variables that appear in a
	# nonlinear objective AND a nonlinear constraint
	# 3) Continuous variables that appear in a
	# nonlinear constraint, but NOT a nonlinear objective
	# 4) Discrete variables that appear in a
	# nonlinear constraint, but NOT a nonlinear objective
	# 5) Continuous variables that appear in a
	# nonlinear objective, but NOT a nonlinear constraint
	# 6) Discrete variables that appear in a
	# nonlinear objective, but NOT a nonlinear constraint
	# 7) Continuous variables that DO NOT appear in a
	# nonlinear objective or a nonlinear constraint
	# 8) Binary variables that DO NOT appear in a
	# nonlinear objective or a nonlinear constraint
	# 9) Integer variables that DO NOT appear in a
	# nonlinear objective or a nonlinear constraint
	#
	# Yes, nonlinear variables are broken into continuous/discrete, but linear
	# variables are partitioned into continuous, binary, and integer. (See also,
	# the need to modify bounds for binary variables.)
	if !dest.f.is_linear
	for x in keys(dest.f.linear_terms)
	dest.x[x].in_nonlinear_objective = true
	end
	for x in dest.f.nonlinear_terms
	if x isa MOI.VariableIndex
	dest.x[x].in_nonlinear_objective = true
	end
	end
	end
	for con in dest.g
	for x in keys(con.expr.linear_terms)
	dest.x[x].in_nonlinear_constraint = true
	end
	for x in con.expr.nonlinear_terms
	if x isa MOI.VariableIndex
	dest.x[x].in_nonlinear_constraint = true
	end
	end
	end
	types = dest.types
	for (x, v) in dest.x
	if v.in_nonlinear_constraint && v.in_nonlinear_objective
	push!(v.type == _CONTINUOUS ? types[1] : types[2], x)
	elseif v.in_nonlinear_constraint
	push!(v.type == _CONTINUOUS ? types[3] : types[4], x)
	elseif v.in_nonlinear_objective
	push!(v.type == _CONTINUOUS ? types[5] : types[6], x)
	elseif v.type == _CONTINUOUS
	push!(types[7], x)
	elseif v.type == _BINARY
	push!(types[8], x)
	else
	@assert v.type == _INTEGER
	push!(types[9], x)
	end
	end
	# However! Don't let Tables 3 and 4 fool you, because the ordering actually
	# depends on whether the number of nonlinear variables in the objective only
	# is _strictly_ greater than the number of nonlinear variables in the
	# constraints only. Quoting:
	#
	# For all versions, the first nlvc variables appear nonlinearly in at
	# least one constraint. If nlvo > nlvc, the first nlvc variables may or
	# may not appear nonlinearly in an objective, but the next nlvo – nlvc
	# variables do appear nonlinearly in at least one objective. Otherwise
	# all of the first nlvo variables appear nonlinearly in an objective.
	#
	# However, even this is slightly incorrect, because I think it should read
	# "For all versions, the first nlvb variables appear nonlinearly." The "nlvo
	# - nlvc" part is also clearly incorrect, and should probably read "nlvo -
	# nlvb."
	#
	# It's a bit confusing, so here is the relevant code from Couenne:
	# https://github.com/coin-or/Couenne/blob/683c5b305d78a009d59268a4bca01e0ad75ebf02/src/readnl/readnl.cpp#L76-L87
	#
	# They interpret this paragraph to mean the switch on nlvo > nlvc determines
	# whether the next block of variables are the ones that appear in the
	# objective only, or the constraints only.
	#
	# That makes sense as a design choice, because you can read them in two
	# contiguous blocks.
	#
	# Essentially, what all this means is if !(nlvo > nlvc), then swap 3-4 for
	# 5-6 in the variable order.
	nlvc = length(types[3]) + length(types[4])
	nlvo = length(types[5]) + length(types[6])
	order_i = if nlvo > nlvc
	[1, 2, 3, 4, 5, 6, 7, 8, 9]
	else
	[1, 2, 5, 6, 3, 4, 7, 8, 9]
	end
	# Now we can order the variables.
	n = 0
	for i in order_i
	# Since variables come from a dictionary, there may be differences in
	# the order depending on platform and Julia version. Sort by creation
	# time for consistency.
	for x in sort!(types[i]; by = y -> y.value)
	dest.x[x].order = n
	dest.order[n+1] = x
	n += 1
	end
	end
	return mapping
	end

Causes AmplNLWriter to fail tests:

test_model_copy_to_UnsupportedConstraint: Test Failed at /Users/oscar/.julia/packages/MathOptInterface/2NqE9/src/Test/test_model.jl:605
  Expression: MOI.copy_to(model, BadConstraintModel())
    Expected: MathOptInterface.UnsupportedConstraint
  No exception thrown
Stacktrace:
 [1] test_model_copy_to_UnsupportedConstraint(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{MathOptInterface.Utilities.CachingOptimizer{AmplNLWriter.Optimizer, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.GenericModel{Float64, MathOptInterface.Utilities.ModelFunctionConstraints{Float64}}}}}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.GenericModel{Float64, MathOptInterface.Utilities.ModelFunctionConstraints{Float64}}}}, #unused#::MathOptInterface.Test.Config{Float64})
   @ MathOptInterface.Test ~/.julia/packages/MathOptInterface/2NqE9/src/Test/test_model.jl:605
 [2] macro expansion
   @ ~/.julia/packages/MathOptInterface/2NqE9/src/Test/Test.jl:187 [inlined]
 [3] macro expansion
   @ /Users/julia/buildbot/worker/package_macos64/build/usr/share/julia/stdlib/v1.6/Test/src/Test.jl:1151 [inlined]
 [4] runtests(model::MathOptInterface.Utilities.CachingOptimizer{MathOptInterface.Bridges.LazyBridgeOptimizer{MathOptInterface.Utilities.CachingOptimizer{AmplNLWriter.Optimizer, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.GenericModel{Float64, MathOptInterface.Utilities.ModelFunctionConstraints{Float64}}}}}, MathOptInterface.Utilities.UniversalFallback{MathOptInterface.Utilities.GenericModel{Float64, MathOptInterface.Utilities.ModelFunctionConstraints{Float64}}}}, config::MathOptInterface.Test.Config{Float64}; include::Vector{String}, exclude::Vector{String}, warn_unsupported::Bool)
   @ MathOptInterface.Test ~/.julia/packages/MathOptInterface/2NqE9/src/Test/Test.jl:181