add comment on duals of fixed vars to TNLP::finalize_solution

- ref #308

src/Interfaces/IpTNLP.hpp

@@ -501,6 +501,10 @@ class IPOPTLIB_EXPORT TNLP : public ReferencedObject
    /** @name Solution Methods */
    ///@{
    /** This method is called when the algorithm has finished (successfully or not) so the TNLP can digest the outcome, e.g., store/write the solution, if any.
+    *
+    *  @note If fixed_variable_treatment is make_parameter (the default), then zero is returned for the bound multipliers of fixed variables.
+    *     Therefore, in an optimal solution, the gradient of the Lagrangian w.r.t. fixed variables may not appear to be zero.
+    *     If this is a problem, setting parameter fixed_variable_treatment to make_constraint could be workaround.
     *
     *  @param status @parblock (in) gives the status of the algorithm
     *   - SUCCESS: Algorithm terminated successfully at a locally optimal

src/Interfaces/IpTNLPAdapter.cpp

@@ -1131,7 +1131,7 @@ bool TNLPAdapter::GetSpaces(
       jac_c_jCol = NULL;
 
       // ... build the nonzero structure for jac_d
-      // ... (the permuation from rows in jac_g to jac_c is the
+      // ... (the permutation from rows in jac_g to jac_c is the
       // ...  the same as P_d_g_)
       Index* jac_d_iRow = new Index[nz_full_jac_g_];
       Index* jac_d_jCol = new Index[nz_full_jac_g_];
@@ -2824,7 +2824,7 @@ bool TNLPAdapter::CheckDerivatives(
          }
       }
 #endif
-      // Obtain values at reference pont
+      // Obtain values at reference point
       jac_g = new Number[nz_jac_g];
       retval = tnlp_->eval_jac_g(nx, xref, new_x, ng, nz_jac_g, NULL, NULL, jac_g);
       ASSERT_EXCEPTION(retval, ERROR_IN_TNLP_DERIVATIVE_TEST,