  In this paper we study a new approach in optimization that aims to search a
large domain D where a given function takes large, small or specific values via
an iterative optimization algorithm based on the gradient. We show that the
objective function used is not directly optimizable, however, we use a trick to
approximate this objective by another one at each iteration to optimize it.
Then we explore a use case of this algorithm in machine learning to find
domains where the models output large and small values with respect of some
constraints. Experiments demonstrate the efficiency of this algorithm on five
cases with models trained on the titanic dataset.
