Add support for MOI.VariableName and MOI.ConstraintName (#104)

src/MOI_wrapper.jl

@@ -166,6 +166,7 @@ function _F_linear_operator(model::Optimizer)
  M = SparseArrays.sparse(Int32[], Int32[], Float64[], n, n)
  q = zeros(n)
  has_term = fill(false, n)
+ names = String[]
  for index in MOI.get(
  model,
  MOI.ListOfConstraintIndices{
@@ -232,8 +233,16 @@ function _F_linear_operator(model::Optimizer)
  end
  M[row_i, s_term.variable.value] += s_term.coefficient
  end
+ c_name = MOI.get(model, MOI.ConstraintName(), index)
+ if Si.dimension == 2
+ push!(names, c_name)
+ else
+ for i in 1:div(Si.dimension, 2)
+ push!(names, "$(c_name)[$i]")
+ end
+ end
  end
- return M, q, SparseArrays.nnz(M)
+ return M, q, SparseArrays.nnz(M), names
 end
 
 _to_f(f) = convert(MOI.ScalarNonlinearFunction, f)
@@ -255,6 +264,7 @@ end
 function _F_nonlinear_operator(model::Optimizer)
  x = MOI.get(model, MOI.ListOfVariableIndices())
  f_map = Vector{MOI.ScalarNonlinearFunction}(undef, length(x))
+ names = String[]
  for (FType, SType) in MOI.get(model, MOI.ListOfConstraintTypesPresent())
  if SType != MOI.Complements
  continue
@@ -274,6 +284,14 @@ function _F_nonlinear_operator(model::Optimizer)
  end
  f_map[xi.value] = fi
  end
+ c_name = MOI.get(model, MOI.ConstraintName(), ci)
+ if N == 1
+ push!(names, c_name)
+ else
+ for i in 1:N
+ push!(names, "$(c_name)[$i]")
+ end
+ end
  end
  end
  for i in 1:length(x)
@@ -331,7 +349,7 @@ function _F_nonlinear_operator(model::Optimizer)
  MOI.eval_constraint_jacobian(evaluator, view(data, inverse_perm), x)
  return Cint(0)
  end
- return F, J, length(J_structure)
+ return F, J, length(J_structure), names
 end
 
 _finite(x, y) = isfinite(x) ? x : y
@@ -348,7 +366,8 @@ function _bounds_and_starting(model::Optimizer)
  upper[i] = u
  initial[i] = something(z, _finite(l, _finite(u, 0.0)))
  end
- return lower, upper, initial
+ names = MOI.get.(model, MOI.VariableName(), x)
+ return lower, upper, initial, names
 end
 
 # MOI.optimize!
@@ -368,13 +387,13 @@ function MOI.optimize!(model::Optimizer)
  is_nlp =
  (MOI.VectorNonlinearFunction, MOI.Complements) in con_types ||
  (MOI.VectorQuadraticFunction{Float64}, MOI.Complements) in con_types
- F, J, nnz = if is_nlp
+ F, J, nnz, c_names = if is_nlp
  _F_nonlinear_operator(model)
  else
  _F_linear_operator(model)
  end
  model.ext[:solution] = nothing
- lower, upper, initial = _bounds_and_starting(model)
+ lower, upper, initial, names = _bounds_and_starting(model)
  status, x, info = solve_mcp(
  F,
  J,
@@ -385,6 +404,8 @@ function MOI.optimize!(model::Optimizer)
  silent = model.ext[:silent],
  jacobian_structure_constant = true,
  jacobian_data_contiguous = true,
+ variable_names = names,
+ constraint_names = c_names,
  [k => v for (k, v) in model.ext[:kwargs]]...,
  )
  if x === nothing

test/MOI_wrapper.jl

@@ -497,6 +497,60 @@ function test_empty_model()
  return
 end
 
+function test_solve_with_names()
+ model = PATHSolver.Optimizer()
+ MOI.set(model, MOI.RawOptimizerAttribute("output_linear_model"), "yes")
+ MOI.Utilities.loadfromstring!(
+ model,
+ """
+ variables: v, w, x, y, z
+ c2: [0.0, -1.0 * y * y + -1.0 * z + 2, v, w] in Complements(4)
+ c3: VectorNonlinearFunction([ScalarNonlinearFunction(y^3 - 2z^2 + 2), ScalarNonlinearFunction(w^5 - x + 2y - 2z - 2), x, y]) in Complements(4)
+ c4: VectorNonlinearFunction([ScalarNonlinearFunction(w + 2x^3 - 2y + 4z - 6), z]) in Complements(2)
+ v in EqualTo(1.0)
+ w in Interval(0.0, 10.0)
+ x in GreaterThan(0.0)
+ z in Interval(1.0, 2.0)
+ """,
+ )
+ x = MOI.get(model, MOI.ListOfVariableIndices())
+ MOI.set.(model, MOI.VariablePrimalStart(), x, 1.0)
+ MOI.optimize!(model)
+ @test ≈(
+ MOI.get.(model, MOI.VariablePrimal(), x),
+ [1.0, 1.2847523, 0.9729164, 0.9093761, 1.1730350];
+ atol = 1e-6,
+ )
+ return
+end
+
+function test_solve_with_names_nonlinear()
+ model = PATHSolver.Optimizer()
+ MOI.set(model, MOI.RawOptimizerAttribute("output_linear_model"), "yes")
+ MOI.Utilities.loadfromstring!(
+ model,
+ """
+ variables: v, w, x, y, z
+ cnl2: VectorNonlinearFunction([0.0, -1.0 * y * y + -1.0 * z + 2, v, w]) in Complements(4)
+ c3: VectorNonlinearFunction([ScalarNonlinearFunction(y^3 - 2z^2 + 2), ScalarNonlinearFunction(w^5 - x + 2y - 2z - 2), x, y]) in Complements(4)
+ c4: VectorNonlinearFunction([ScalarNonlinearFunction(w + 2x^3 - 2y + 4z - 6), z]) in Complements(2)
+ v in EqualTo(1.0)
+ w in Interval(0.0, 10.0)
+ x in GreaterThan(0.0)
+ z in Interval(1.0, 2.0)
+ """,
+ )
+ x = MOI.get(model, MOI.ListOfVariableIndices())
+ MOI.set.(model, MOI.VariablePrimalStart(), x, 1.0)
+ MOI.optimize!(model)
+ @test ≈(
+ MOI.get.(model, MOI.VariablePrimal(), x),
+ [1.0, 1.2847523, 0.9729164, 0.9093761, 1.1730350];
+ atol = 1e-6,
+ )
+ return
+end
+
 end
 
 TestMOIWrapper.runtests()