65N30 Finite elements, Rayleigh-Ritz and Galerkin methods, finite methods
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- finite element method (6)
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- AFEM (3)
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- mixed finite elements (3)
- optimal control (3)
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A refined a posteriori error analysis for symmetric eigenvalue problems and the convergence of the first-order adaptive finite element method (AFEM) is presented. The $H^1$ stability of the $L^2$ projection provides reliability and efficiency of the edge-contribution of standard residual-based error estimators for $P_1$ finite element methods. In fact, the volume contributions and even oscillations can be omitted for Courant finite element methods. This allows for a refined averaging scheme and so improves [Dong Mao, Lihua Shen and Aihui Zhou, Adaptive finite element algorithms for eigenvalue problems based on local averaging type a posteriori error estimates, Advanced in Computational Mathematics, 2006, 25: 135-160]. The proposed AFEM
monitors the edge-contributions in a bulk criterion and so enables a contraction property up to higher-order terms and global convergence. Numerical experiments exploit the remaining $L^2$ error contributions and confirm our theoretical findings. The averaging schemes show a high accuracy and the AFEM leads to optimal empirical convergence rates.
We construct and analyze multigrid methods
for discretized self-adjoint elliptic problems on triangular surfaces in $\RR^3$.
The methods involve the same weights for restriction and prolongation as in the case of planar triangulations
and therefore are easy to implement. We prove logarithmic bounds of the convergence
rates with constants solely depending on the ellipticity, the smoothers and on the
regularity of the triangles forming the triangular surface.
Our theoretical results are illustrated by numerical computations.
We show how time-dependent optimal control for partial differential equations can be realized in a modern high-level modeling and simulation package. We summarize the general formulation for distributed and boundary control for initial-boundary value problems for parabolic PDEs and derive the optimality system including the adjoint equation. The main difficulty therein is that the latter has to be integrated backwards in time. This implies that complicated implementation effort is necessary to couple state and adjoint equations to compute an optimal solution. Furthermore a large amount of computational effort or storage is required to provide the needed information (i.e the trajectories) of the state and adjoint variables. We show how this can be realized in the modeling and simulation package COMSOL MULTIPHYSICS, taking advantage of built-in discretization, solver and post-processing technologies and thus minimizing the implementation effort. We present two strategies: The treatment of the coupled optimality system in the space-time cylinder, and the iterative approach by sequentially solving state and adjoint system and updating the controls. Numerical examples show the elegance of the implementation and the efficiency of the two strategies.
We present globally convergent multigrid methods for the nonsymmetric
obstacle problems as arising from the discretization of Black–Scholes models of
American options with local volatilities and discrete data. No tuning or regularization
parameters occur. Our approach relies on symmetrization by transformation
and data recovery by superconvergence.
A class of optimal control problem for a semilinear elliptic partial differential equation
with control constraints is considered. It is well known that
sufficient second-order conditions ensure the stability of optimal solutions, the convergence of
numerical methods. Otherwise, such conditions are very difficult to verify (analytically or numerically).
We will propose a new approach: Starting with a numerical solution for a fixed mesh we will
show the existence of a local minimizer of the continuous problem. Moreover, we will prove that
this minimizer satisfies the sufficient second-order conditions.