Convergence towards a local minimum by direct search methods with a covering step

This paper introduces a new step to the Direct Search Method (DSM) to strengthen its convergence analysis. By design, this so-called coveringstep may ensure that, for any refined point of the sequence of incumbent solutions generated by the resulting cDSM (covering dsm), the set of all evaluated trial points is dense in a neighborhood of that refined point. We prove that this additional property guarantees that all refined points are local solutions to the optimization problem. This new result holds true even for a discontinuous objective function, under a mild assumption that we discuss in details. We also provide a practical construction scheme for the covering step that works at low additional cost per iteration. Finally, we show that the covering step may be adapted to classes of algorithms differing from the DSM.