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The paper presents a new affine invariant theory on asymptotic mesh
independence of Newton’s method for discretized nonlinear operator equations.
Compared to earlier attempts, the new approach is both much simpler
and more intuitive from the algorithmic point of view. The theory
is exemplified at collocation methods for ODE boundary value problems
and at finite element methods for elliptic PDE problems.
The paper proposes goal-oriented error estimation and mesh refinement
for optimal control problems with elliptic PDE constraints using the value
of the reduced cost functional as quantity of interest. Error representation,
hierarchical error estimators, and greedy-style error indicators are derived and
compared to their counterparts when using the all-at-once cost functional as
quantity of interest. Finally, the efficiency of the error estimator and generated
meshes are demonstrated on numerical examples.
This paper presents concepts and implementation of the finite element toolbox Kaskade 7, a flexible C++ code for solving elliptic and parabolic PDE systems. Issues such as problem formulation, assembly and adaptivity are discussed at the example of optimal control problems. Trajectory compression for parabolic optimization problems is considered as a case study.
We consider Large Deformation Diffeomorphic Metric Mapping of general $m$-currents. After stating an optimization algorithm in the function space of admissable morph generating velocity fields, two innovative aspects in this framework are presented and numerically investigated: First, we spatially discretize the velocity field with conforming adaptive finite elements and discuss advantages of this new approach. Second, we directly compute the temporal evolution of discrete $m$-current attributes.
In optimal control problems with nonlinear time-dependent 3D PDEs, full 4D discretizations are usually prohibitive due to the storage requirement. For this reason gradient and Newton type methods working on the reduced functional are often employed. The computation of the reduced gradient requires one solve of the state equation forward in time, and one backward solve of the adjoint equation. The state enters into the adjoint equation, again requiring the storage of a full 4D data set. We propose a lossy compression algorithm using an inexact but cheap predictor for the state data, with additional entropy coding of prediction errors. As the data is used inside a discretized, iterative algorithm, lossy compression
maintaining a certain error bound turns out to be sufficient.
We consider a shape implant design problem that arises in the context of facial surgery. We introduce a reformulation as an optimal control problem, where the control acts as a boundary force. The state is modelled as a minimizer of a polyconvex hyperelastic energy functional. We show existence of optimal solutions and derive - on a formal level - first order optimality conditions. Finally, preliminary numerical results are presented.
The paper addresses primal interior point method for state constrained PDE optimal
control problems. By a Lavrentiev regularization, the state constraint is transformed to a mixed
control-state constraint with bounded Lagrange multiplier. Existence and convergence of the central
path are established, and linear convergence of a short-step pathfollowing method is shown. The
behaviour of the regularizations are demonstrated by numerical examples.
A primal interior point method for control constrained optimal control problems
with PDE constraints is considered. Pointwise elimination of the control
leads to a homotopy in the remaining state and dual variables, which is addressed
by a short step pathfollowing method. The algorithm is applied to the
continuous, infinite dimensional problem, where discretization is performed
only in the innermost loop when solving linear equations. The a priori elimination
of the least regular control permits to obtain the required accuracy with
comparatively coarse meshes. Convergence of the method and discretization
errors are studied, and the method is illustrated at two numerical examples.
The paper introduces an identification problem arising in modern regional hyperthermia, a cancer
therapy aiming at heating the tumor by microwave radiation. The task is to identify the highly
individual perfusion, which affects the resulting temperature distribution, from MR measurements.
The identification problem is formulated as an optimization problem. Existence of a solution and
optimality conditions are analyzed. Different regularizations and problem variants are considered. For
the numerical solution, a standard SQP method is used. Sufficient conditions for the convergence of
the method are derived. Finally, numerical examples on artificial as well as clinical data are presented.