Zero Duality Gap and Attainment with Possibly Non-Convex Data

A newly defined notion of convex closedness regarding a set is used in order to state a necessary and sufficient criterion for the min-sup property in non necessarily convex primal-dual optimization problems, generalizing well-known theorems valid in the convex setting. Our main result is then applied to the classical penalty method.