This is a corner case, where we should probably on the behavior.

We have several ways to access the constraints function: cons!/cons, cons_nln and cons_lin, and objcons. How should these behave when applied on an unconstrained problems? I see 3 options:

• Business as usual: evaluate cons (hoping it doesn't break in case the NLPModel has not implemented this function - but that's a normal error in this case), and increase counters.
• Increase counters (because the user called the function), but try to skip the evaluation.

function cons!(nlp::AbstractNLPModel, x::AbstractVector, cx::AbstractVector)
  @lencheck nlp.meta.nvar x
  @lencheck nlp.meta.ncon cx
  increment!(nlp, :neval_cons)
  nlp.meta.nlin > 0 && cons_lin!(nlp, x, view(cx, nlp.meta.lin))
  nlp.meta.nnln > 0 && cons_nln!(nlp, x, view(cx, nlp.meta.nln))
  return cx
end

• Ignore completely the call to cons.

function cons!(nlp::AbstractNLPModel, x::AbstractVector, cx::AbstractVector)
  @lencheck nlp.meta.nvar x
  @lencheck nlp.meta.ncon cx
  nlp.meta.ncon > 0 && increment!(nlp, :neval_cons)
  nlp.meta.nlin > 0 && cons_lin!(nlp, x, view(cx, nlp.meta.lin))
  nlp.meta.nnln > 0 && cons_nln!(nlp, x, view(cx, nlp.meta.nln))
  return cx
end

• another option?

A related question is: How should objcons react to this situation?

Connected to JuliaSmoothOptimizers/NLPModelsTest.jl#26 and JuliaSmoothOptimizers/CUTEst.jl#327