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- dissipativity (3)
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Given a system of closed convex domains (inclusions) in n-dimensional Euclidean space, new computational meshes are introduced which partition the convex hull of the inclusion set into simple geometric objects. These partitions generalize the concept of Delaunay triangulations by interpreting the inclusions as generalized vertices while the remaining elements of the partition serve as connections between generalized vertices and therefore assume the classical role of edges, faces, etc. The proposed partitions are derived in two different ways: by exploiting duality with respect to certain generalized Voronoi partitions and by generalizing the well known Delaunay (empty circumcircle) criterion.
Generalized Delaunay partitions are of practical importance for the modeling of particle- and fiber-reinforced composite materials
since they enable an efficient conforming resolution of the highly complicated component geometries. The number of elements in the partitions is proportional to the number of inclusions which is minimal.
Functional Magnetic Resonance Imaging inherently involves noisy measurements and a severe multiple test
problem. Smoothing is usually used to reduce the effective number of multiple
comparisons and to locally integrate the signal and hence increase the
signal-to-noise ratio. Here, we provide a new structural adaptive segmentation
algorithm (AS)
that naturally combines the signal detection with noise reduction in one procedure.
Moreover, the new method
is closely related to a recently proposed structural adaptive smoothing
algorithm and preserves shape and spatial extent of activation areas without
blurring the borders.
MINLP Solver Software
(2010)
Motivated by an obstacle problem for a membrane
subject to cohesion forces, constrained minimization problems involving
a non-convex and non-differentiable objective functional
representing the total potential energy are considered. The associated
first order optimality system leads to a hemi-variational inequality,
which can also be interpreted as a special complementarity
problem in function space. Besides an analytical investigation
of first-order optimality, a primal-dual active set solver is introduced.
It is associated to a limit case of a semi-smooth Newton
method for a regularized version of the underlying problem class.
For the numerical algorithms studied in this paper, global as well as
local convergence properties are derived and verified numerically.
A duality based semismooth Newton framework for solving variational inequalities of the second kind
(2010)
In an appropriate function space setting, semismooth Newton methods are proposed
for iteratively computing the solution of a rather general class of variational inequalities (VIs) of the
second kind. The Newton scheme is based on the Fenchel dual of the original VI problem which
is regularized if necessary. In the latter case, consistency of the regularization with respect to the
original problem is studied. The application of the general framework to specific model problems
including Bingham flows, simplified friction, or total variation regularization in mathematical imaging
is described in detail. Finally, numerical experiments are presented in order to verify the theoretical
results.
In this paper a general form of the infinite-horizon linear quadratic control problem is considered. We will discuss quadratic cost functionals which involve not only the state and input-variables but also derivatives of the state and input-variables of arbitrary order under constraints given by linear systems of higher order. We will examine two results that relate the linear quadratic control problem to an optimality system, which is given through a para-Hermitian matrix polynomial. The results can be applied to general rectangular descriptor systems (see Subsection 6.1) to obtain results which so far were only known for quadratic descriptor systems. Also we will see that the notion of dissipativity (when introduced in the proper way) is equivalent to the solvability of the linear quadratic control problem.
The behavior approach and the problem of dissipativity have both been introduced and studied extensively by Willems et al. However, a computationally feasible method to check dissipativity is missing. Current methods will mostly rely on symbolic representations of rational functions. We will discuss a new characterization for linear systems in behavior form that allows to check dissipativity via the solution of a para-Hermitian, polynomial eigenvalue problem. Thus, we can employ standard methods of cubic complexity.
Under market frictions like illiquidity or transaction costs, contingent claims
can incorporate some inevitable intrinsic risk that cannot be completely hedged
away but remains with the holder. In general, they cannot be synthesized by
dynamical trading in liquid assets and hence not be priced by no-arbitrage arguments alone. Still, an agent can determine a valuation with respect to her
preferences towards risk. The utility indifference value for a variation in the
quantity of illiquid assets held by the agent is defined as the compensating variation
of wealth, under which her maximal expected utility remains unchanged.
Arrow Debreu Prices
(2010)
Arrow Debreu prices are the prices of ‘atomic’ time and state contingent
claims which deliver one unit of a specific consumption good if a specific uncertain
state realizes at a specific future date. For instance, claims on the good
‘ice cream tomorrow’ are split into different commodities depending whether the
weather will be good or bad, so that good-weather and bad-weather ice cream
tomorrow can be traded separately. Such claims were introduced by K.J. Arrow
and G. Debreu in their work on general equilibrium theory under uncertainty,
to allow agents to exchange state and time contingent claims on goods. Thereby
the general equilibrium problem with uncertainly can be reduced to a conventional
one without uncertainty. In finite state financial models, Arrow-Debreu
securities delivering one unit of the numeraire good can be viewed as natural
atomic building blocks for all other state-time contingent financial claims; their
prices determine a unique arbitrage-free price system.
We present and compare two different approaches to conditional
risk measures. One approach draws from vector space based convex analysis
and presents risk measures as functions on L^p spaces while the other approach
utilizes module based convex analysis where conditional risk measures are defined on L^p type modules. Both approaches utilize general duality theory for
vector valued convex functions in contrast to the current literature in which
we fi nd ad hoc dual representations. By presenting several applications such
as monotone and sub(cash) invariant hulls with corresponding examples we
illustrate that module based convex analysis is well suited to the concept of
conditional risk measures.