Non-monotone Inexact Restoration Method for Nonlinear Programming.

This paper deals with a new variant of the inexact restoration method of Fischer and Friedlander (Comput Optim Appl 46:333–346, 2010) for nonlinear programming. We propose an algorithm that replaces the monotone line search performed in the tangent phase by a non-monotone one, using the sharp Lagrangian as merit function. Convergence to feasible points satisfying the convex approximate gradient projection condition is proved under mild assumptions. Numerical results on representative test problems show that the proposed approach outperforms the monotone version when a suitable non-monotone parameter is chosen and is also competitive against other globalization strategies for inexact restoration.