Approximate stationarity conditions provide necessary optimality conditions without requiring additional assumptions by demanding that a perturbed stationarity system possesses solutions as the involved perturbations tend to zero. Together with associated approximate constraint qualifications, which are typically rather mild, they raised much interest in the optimization community during the last decade. In parallel, directional stationarity conditions became quite popular as they sharpen standard stationarity conditions by incorporating data associated with underlying critical directions. The purpose of this paper is twofold. First, we melt the aforementioned concepts of approximate and directional stationarity to formulate and study so-called approximate directional stationarity. For the underlying model problem, an optimization problem with nonsmooth geometric constraints is chosen, which covers diverse practically relevant applications. The role of approximate directional stationarity as a necessary optimality condition is investigated in much detail, complementing results from the literature. Second, we formulate a qualification condition which, based on an approximately directionally stationary point, can be exploited to infer its directional stationarity. The latter condition depends on one particular sequence verifying approximate directional stationarity and merely requires to check a simple condition of Mangasarian--Fromovitz type stated in terms of the directional tools of limiting variational analysis. This contrasts standard approximate constraint qualifications that typically demand a certain stable behavior of all sequences validating approximate stationarity. Throughout, various approaches to verify directional stationarity of local minimizers are established, and illustrative examples are presented to make the theoretical results more accessible.
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