Refine
Year of publication
Language
- English (1103) (remove)
Keywords
- optimal control (27)
- stability (14)
- integer programming (11)
- Stochastic programming (9)
- finite elements (9)
- mixed integer programming (9)
- Hamiltonian matrix (8)
- finite element method (8)
- model reduction (8)
- state constraints (8)
Canonical forms are developed for several sets of complex matrices that are normal
with respect to an indefinite inner product induced by a nonsingular Hermitian,
symmetric, or skew-symmetric matrix. The most general result covers the case of
polynomially normal matrices, i.e., matrices whose adjoint with respect to the indefinite
inner product is a polynomial of the original matrix. From this result, canonical
forms for matrices that are selfadjoint, skewadjoint, or unitary with respect to the
given indefinite inner product are derived.
This work is concerned with transparent boundary conditions (TBCs) for systems of Schrödinger-type equations, namely
the time-dependent kp-Schrödinger equations. These TBCs are constructed for the fully discrete scheme (Crank-Nicolson,
finite differences), in order to maintain unconditional stability of the scheme and to avoid numerical reflections. The discrete
transparent boundary conditions (DTBCs) are discrete convolutions in time and are constructed using the Z-transformed
solution of the exterior problem. We will analyse the numerical error of these convolution coeffficients caused by the inverse
Z-transformation. Since the DTBCs are non-local in time and thus very costly to evaluate, we present approximate DTBCs
of a sum-of-exponentials form that allow for a fast calculation of the boundary terms.
We discuss the state of the art in numerical solution methods for large scale polynomial or
rational eigenvalue problems. We present the currently available solution methods such as
the Jacobi-Davidson, Arnoldi or the rational Krylov method and analyze their properties.
We briefly introduce a new linearization technique and demonstrate how it can be used to
improve structure preservation and with this the accuracy and efficiency of linearization based
methods. We present several recent applications where structured and unstructured nonlinear
eigenvalue problems arise and some numerical results.
In Kolodko & Schoenmakers (2004) and Bender & Schoenmakers (2004) a policy iteration was introduced which allows to achieve tight lower approximations of the price for early exercise options via a nested Monte-Carlo simulation in a Markovian setting. In this paper we enhance the algorithm by a scenario selection method. It is demonstrated by numerical examples that the scenario selection can significantly reduce the number of actually performed inner simulations, and thus can heavily speed up the method (up to factor 10 in some examples). Moreover, it is shown that the modified algorithm retains the desirable properties of the original one such as the monotone improvement property, termination after a finite number of iteration steps, and numerical stability.
We propose a valuation method for callable structures in a multi-factor Libor model which are path-dependent in the sense that, after calling, one receives a sequence of cash-flows in the future, instead of a well specified cash-flow at the calling date. The method is based on a Monte Carlo procedure for standard Bermudans recently developed in Kolodko & Schoenmakers (2004), and is applied to the cancelable snowball interest rate swap. The proposed procedure is quite generic, straightforward to implement, and can be easily adapted to other related path-dependent products.
Polzehl and Spokoiny (2000) introduced the adaptive weights smoothing
(AWS) procedure in the context of image denoising. The procedure
has some remarkable properties like preservation of edges and contrast,
and (in some sense) optimal reduction of noise. The procedure is fully
adaptive and dimension free. Simulations with artificial images show
that AWS is superior to classical smoothing techniques especially when
the underlying image function is discontinuous and can be well approximated
by a piecewise constant function. However, the latter assumption
can be rather restrictive for a number of potential applications. Here we
present a new method based on the ideas of propagation and separation
which extends the AWS procedure to the case of an arbitrary local linear
parametric structure. We also establish some important results about
properties of the new ‘propagation-separation’ procedure including rate
optimality in the pointwise and global sense. The performance of the
procedure is illustrated by examples for local polynomial regression and
by applications to artificial and real images.
Our main result is that every n-dimensional polytope can be
described by at most 2n ? 1 polynomial inequalities and, moreover, these
polynomials can explicitly be constructed. For an n-dimensional pointed
polyhedral cone we prove the bound 2n ? 2 and for arbitrary polyhedra we
get a constructible representation by 2n polynomial inequalities.
Traveling wave equations are used to model the dynamics of multisection semiconductor lasers. To perform a bifurcation analysis of this system of 1-D partial differential equations its low dimensional approximations are constructed and considered. Along this paper this analysis is used for the extensive study of the pulsations in a three section distributed feedback laser. Namely, stability of pulsations, different bifurcation scenaria, tunability of the pulsation frequency and its locking by the frequency of electrical modulation are considered. All these pulsation qualities are highly important when applying lasers in optical communication systems.
We present an integer linear programming model for the design of multi-layer telecommunication
networks which are based on connection-oriented routing protocols. The formulation integrates hardware,
capacity, routing, and grooming decisions in any number of network layers. Practical hardware
restrictions and cost can accurately be taken into account.