Enhance doc-string of constrained optimization

quadratic_simple_constraint.py

@@ -7,6 +7,10 @@
 deep neural networks fit on your hardware, e.g. 8GB of memory consumption.
 In this case, constrained optimization aims to yield an optimal solution that satisfied such constraints.
 
+Note that Optuna cannot optimize an objective that will not return any results when some constraints violate.
+For example, when we run a memory-intensive algorithm and user sets the memory constraint very close to the limit,
+we may not get any results if the memory constraint is violated.
+However, Optuna cannot handle such situations. 
 Please also check https://optuna.readthedocs.io/en/stable/faq.html#id16 as well.
 
 """
@@ -33,6 +37,7 @@ def constraints(trial):
 
 
 if __name__ == "__main__":
+    # Let us minimize the objective function with soft constraints above.
     sampler = optuna.samplers.TPESampler(constraints_func=constraints)
     study = optuna.create_study(sampler=sampler)
     study.optimize(objective, n_trials=500, timeout=1)
@@ -52,5 +57,5 @@ def constraints(trial):
         print(f"Best trial was found at Trial#{best_trial_id}")
         print(f"  Params: {best_trial.params}")
         print(f"  Value: {best_trial.value}")
-        c1, c2 = best_trial.system_attrs['constraints']
+        c1, c2 = best_trial.system_attrs["constraints"]
         print(f"  Constraints: {c1=}, {c2=}")