Monotone iterations for elliptic variational inequalities

A wide range of free boundary problems occurring in engineering and industry can be rewritten as a minimization problem for a strictly convex, piecewise smooth but non–differentiable energy functional. The fast solution of related discretized problems is a very delicate question, because usual Newton techniques cannot be applied. We propose a new approach based on convex minimization and constrained Newton type linearization. While convex min- imization provides global convergence of the overall iteration, the subsequent constrained Newton type linearization is intended to accelerate the conver- gence speed. We present a general convergence theory and discuss several applications

On constrained Newton linearization and multigrid for variational inequalities

We consider the fast solution of a class of large, piecewise smooth minimization problems. For lack of smoothness, usual Newton multigrid methods cannot be applied. We propose a new approach based on a combination of convex minization with constrained Newton linearization. No regularization is involved. We show global convergence of the resulting monotone multigrid methods and give polylogarithmic upper bounds for the asymptotic convergence rates. Efficiency is illustrated by numerical experiments

Adaptive monotone multigrid methods for some non-smooth optimization problems

We consider the fast solution of non-smooth optimization problems as resulting for example from the approximation of elliptic free boundary problems of obstacle or Stefan type. Combining well-known concepts of successive subspace correction methods with convex analysis, we derive a new class of multigrid methods which are globally convergent and have logarithmic bounds of the asymptotic convergence rates. The theoretical considerations are illustrated by numerical experiments

Adaptive monotone multigrid methods for nonlinear variational problems

A wide range of problems occurring in engineering and industry is characterized by the presence of a free (i.e. a priori unknown) boundary where the underlying physical situation is changing in a discontinuous way. Mathematically, such phenomena can be often reformulated as variational inequalities or related non–smooth minimization problems. In these research notes, we will describe a new and promising way of constructing fast solvers for the corresponding discretized problems providing globally convergent iterative schemes with (asymptotic) multigrid convergence speed. The presentation covers physical modelling, existence and uniqueness results, finite element approximation and adaptive mesh–refinement based on a posteriori error estimation. The numerical properties of the resulting adaptive multilevel algorithm are illustrated by typical applications, such as semiconductor device simulation or continuous casting

Monotone multigrid methods for elliptic variational inequalities II

We derive globally convergent multigrid methods for discrete elliptic variational inequalities of the second kind as obtained from the approximation of related continuous problems by piecewise linear finite elements. The coarse grid corrections are computed from certain obstacle problems. The actual constraints are fixed by the preceding nonlinear fine grid smoothing. This new approach allows the implementation as a classical V-cycle and preserves the usual multigrid efficiency. We give 1−O(j−3) estimates for the asymptotic convergence rates. The numerical results indicate a significant improvement as compared with previous multigrid approaches

Self adaptive computation of the breakdown voltage of planar pn-junctions with multistep field plates

The breakdown voltage highly depends on the electric field in the depletion area whose computation is the most time consuming part of the simulation. We present a self adaptive Finite Element Method which reduces dramatically the required computation time compared to usual Finite Difference Methods. A numerical example illustrates the efficiency and reliability of the algorithm

Adaptive multigrid methods for Signorini's problem in linear elasticity

We derive globally convergent multigrid methods for the discretized Signorini problem in linear elasticity. Special care has to be taken in the case of spatially varying normal directions. In numerical experiments for 2 and 3 space dimensions we observed similar convergence rates as for corresponding linear problems

On adaptive grid refinement in the presence of internal or boundary layers

We propose an anisotropic refinement strategy which is specially designed for the efficient numerical resulution of internal and boundary layers. This strategy is based on the directed refinement of single triangles together with adaptive multilevel grid orientation. Compared to usual methods, the new anisotropic refinement ends up in more stable and more accurate solutions at much less computational cost. This is demonstrated by several numerical examples

Robust multigrid methods for vector-valued Allen-Cahn equations with logarithmic free energy

We present efficient and robust multigrid methods for the solution of large, nonlinear, non-smooth systems as resulting from implicit time discretization of vector-valued Allen-Cahn equations with isotropic interfacial energy and logarithmic potential. The algorithms are shown to be robust in the sense that convergence is preserved for arbitrary values of temperature, including the deep quench limit. Numerical experiments indicate that the convergence speed as well is independent of temperature

BOXES - a program to generate triangulations from a rectangular domain description

BOXES computes a triangulation from a 2D domain description which consists of an arbitrary set of rectangles. Each rectangle may have attributes to control the triangulating process, define subdomain classes, or specify boundary conditions. The output of the program can be used as a coarse grid for KASKADE or one of its variants