In the present paper, we prove that the augmented Lagrangian method converges to KKT points under the quasinormality constraint qualification, which is associated with the external penalty theory. An interesting consequence is that the Lagrange multipliers estimates computed by the method remain bounded in the presence of the quasinormality condition. In order to establish a more general convergence result, a new sequential optimality condition for smooth constrained optimization, called PAKKT, is defined. The new condition takes into account the sign of the dual sequence, constituting an adequate sequential counterpart to the (enhanced) Fritz-John necessary optimality conditions proposed by Hestenes, and later extensively treated by Bertsekas. PAKKT points are substantially better than points obtained by the classical Approximate KKT (AKKT) condition, which has been used to establish theoretical convergence results for several methods. In particular, we present a simple problem with complementarity constraints such that all its feasible points are AKKT, while only the solutions and a pathological point are PAKKT. This shows the efficiency of the methods that reach PAKKT points, particularly the augmented Lagrangian algorithm, in such problems. We also provided the appropriate strict constraint qualification associated with the PAKKT sequential optimality condition, called PAKKT-regular, and we prove that it is strictly weaker than both quasinormality and cone continuity property. PAKKT-regular connects both branches of these independent constraint qualifications, generalizing all previous theoretical convergence results for the augmented Lagrangian method in the literature.where f : R n → R and X is the feasible set composed of equality and inequality constraints of the form X = {x | h(x) = 0, g(x) ≤ 0}, * This work has been partially supported by CEPID-CeMEAI (FAPESP 2013/07375-0), FAPESP (Grant 2013/05475-7) and CNPq (Grant 303013/2013-3).
Skyrmions are topologically protected field configurations with particle-like properties that play important roles in various fields of science. Recently, skyrmions have been directly observed in chiral magnets. Here, we investigate the effects of nonmagnetic impurities (structural point-like defects) on the different initial states (random or helical states) and on the formation of the skyrmion crystal in a discrete lattice. By using first-principle calculations and Monte Carlo techniques, we have shown that even a small percentage of spin vacancies present in the chiral magnetic thin film affects considerably the skyrmion order. The main effects of impurities are somewhat similar to thermal effects. The presence of these spin vacancies also induces the formation of compact merons (bimerons) in both the helical and skyrmion states. We have also investigated the effects of adjacent impurities producing only one hole (with an almost circular shape) intentionally inserted into the plate (forming a non-simply connected manifold) on the skyrmion crystal.
Mathematical Programs with Complementarity Constraints (MPCCs) are difficult optimization problems that do not satisfy the majority of the usual constraint qualifications (CQs) for standard nonlinear optimization. Despite this fact, classical methods behaves well when applied to MPCCs. Recently, Izmailov, Solodov and Uskov proved that first order augmented Lagrangian methods, under a natural adaption of the Linear Independence Constraint Qualification to the MPCC setting (MPCC-LICQ), converge to strongly stationary (S-stationary) points, if the multiplier sequence is bounded. If the multiplier sequence is not bounded, only Clarke stationary (C-stationary) points are recovered. In this paper we improve this result in two ways. For the case of bounded multipliers we are able replace the MPCC-LICQ assumption by the much weaker MPCC-Relaxed Positive Linear Dependence condition (MPCC-RCLPD). For the case with unbounded multipliers, building upon results from Scholtes, Anitescu, and others, we show that a second order augmented Lagrangian method converges to points that are at least Mordukhovich stationary (M-stationary) but we still need the more stringent MPCC-LICQ assumption. Numerical tests, validating the theory, are also presented.
Abstract. Many algorithms that ensure second-order necessary optimality conditions were developed in the literature. To the best of our knownledge, none of them guarantee Strong Second-Order Necessary Condition (SSONC). Gould and Toint [5] showed that we do not expect SSONC in the barrier method. In this paper, we argue by an example that the same is true for the second-order augmented Lagrangian method introduced in [1]. This reinforces the Weak Second-Order Necessary Condition as the appropriate condition for the convergence analysis of second-order optimization algorithms.
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