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- optimal control (27)
- stability (14)
- integer programming (11)
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- finite elements (9)
- mixed integer programming (9)
- Hamiltonian matrix (8)
- finite element method (8)
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We consider quickest flows within a new model that is based on transportation applications. In contrast to other models, it is forbidden to store flow in nodes and to cross nodes with more than one flow unit simultaneously. We work on undirected graphs. Grid graphs are of special interest because they typically arise in practice. Our model allows to close edges temporarily by time windows, and considers waiting on edges.
We solve several quickest s,t–flow problems without time windows polynomially. We prove that time windows make these problems NP–hard and even not approximable. In a multicommodity environment, all quickest flow variants are shown to be NP–hard even in grid graphs with uniform edge transit times. An alternative proof shows NP-hardness already for a small number of commodities in the case that waiting is not allowed and transit times are edge–specific. Finally, we propose two approximation algorithms in the case that time windows do not occur.
Quasistatic small-strain plasticity in the limit of small hardening and its numerical approximation
(2011)
The quasistatic rate-independent evolution of the Prager-Ziegler-type model of linearized plasticity with hardening is shown to converge to the rate-independent evolution of the Prandtl-Reuss elastic/perfectly plastic model. Based on the concept of energetic solutions we study the convergence of the solutions in the limit for hardening coefficients converging to 0 by using the abstract method of Gamma-convergence for rate-independent systems. An unconditionally convergent numerical scheme is devised and 2D and 3D numerical experiments are presented. A two-sided energy inequality is a posteriori verified to document experimental convergence rates.
Mixed-integer two-stage stochastic programs with fixed recourse matrix, random recourse costs, technology matrix, and right-hand sides are considered. Quantitative continuity properties of its optimal value and solution set are derived when the underlying probability distribution is perturbed with respect to an appropriate probability metric.
We consider the solution of a system of stochastic generalized equations (SGE) where the underlying functions are mathematical expectation of random set-valued mappings. SGE has many applications such as characterizing optimality conditions of a nonsmooth stochastic optimization problem and a stochastic equilibrium problem. We derive quantitative continuity of expected value of the set-valued mapping with respect to the variation of the underlying
probability measure in a metric space. This leads to the subsequent qualitative and quantitative stability analysis of solution set mappings of the SGE. Under some metric regularity conditions, we derive Aubin's property of the solution set mapping with respect to the change of probability measure. The established results are
applied to stability analysis of stationary points of classical one stage and two stage stochastic minimization problems, two stage stochastic mathematical programs with equilibrium constraints and stochastic programs with second order dominance constraints.
Flux coupling analysis (FCA) has become a useful tool for aiding metabolic reconstructions and guiding genetic manipulations. Originally, it was introduced for constraint-based models of metabolic networks that are based on the steady-state assumption. Recently, we have shown that the steady-state assumption can be replaced by a much weaker lattice-theoretic property related to the supports of metabolic fluxes. In this paper, we further extend our approach and delevelop an efficient algorithm for general qualitative flux coupling analysis (QFCA). We illustrate our method by thermodynamic flux coupling analysis (tFCA), which allows studying steady-state metabolic models with loop-law thermodynamic constraints. These models do not satisfy the lattice-theoretic properties required in our previous work. For a selection of genome-scale metabolic network reconstructions, we discuss both theoretically and practically, how thermodynamic constraints strengthen the coupling results that can be obtained with classical FCA.
The extent to which catastrophic weather events occur strongly depends on global climate conditions such as average sea surface temperatures (SST) or sea level pressures. Some of the factors can be predicted up to a year in advance, and should therefore be taken into account in any reasonable management of weather related risk. In this paper we first set up a risk model that integrates climate factors. The we show how variance minimizing hedging strategies explicitly depend on the factors' prediction. Our analysis is based on a detailed study of the predictable representation property on the combined Poisson and Wiener spaces. Using tools of the stochastic calculus of variations we derive a representation formula of the Clark-Ocone type. Finally, we exemplify the theory developed in a case study of US hurricane risk. We derive hedging strategies taking into account that US hurricane activity strongly depends on the SST of the Pacific Ocean.
In this paper, we propose and investigate numerical methods based on QR factorization for computing all or some Lyapunov or Sacker-Sell spectral intervals for
linear differential-algebraic equations.
Furthermore, a perturbation and error analysis for these methods is presented. We
investigate how errors in the data and in the numerical integration affect the
accuracy of the approximate spectral intervals. Although we need to integrate
numerically some differential-algebraic systems on usually very long
time-intervals, under certain assumptions, it is shown that the error of the
computed spectral intervals can be controlled by the local error of numerical
integration and the error in solving the algebraic constraint.
Some numerical examples are presented to illustrate the theoretical results.
Can OR methods help the public transport industry to break even?
How would you build a public transport system? For example, have a look at
Berlin. The BVG, Berlin's public transport company, maintains a network
of 2,423 km, operates 197 lines with 3,286 stops, using 1,554 busses, 1,391
subway cars, and 599 trams from 12 depots, and has 13,409 employees [7].
The BVG currently transports about 800 million passengers per year and
covers about 40% of the total non-pedestrian traffic volume of the city [18].
Does Berlin have a "reasonable" public transportation network? Does
the BVG run a "good" transportation system? Is it "efficient"?
These are difficult questions. In fact, politicians, transportation managers,
customers, taxpayers, etc. frequently employ judgments such as "good"
and "efficient", but nobody can give a defiition what this exactly means.
Since almost every public transportation system in the world is in the red,
the cheapest system is no public transportation at all. On the other hand,
the most convenient system for the passenger - a stop in front of every house
with direct connections to everywhere - is much too expensive. What is the
right compromise? Operations Research has no good answer either - so far.
But OR can improve aspects of public transportation significantly, as we
want to demonstrate in the following.
One-shot optimization aims at attaining feasibility and optimality simultane-
ously, especially on problems where even the linearized constraint equations
cannot be resolved economically. Here we consider a scenario where forming
and factoring the active Jacobian is out of the question, as is for example the
case when the constraints represent some discretization of the Navier Stokes
equation. Assuming that the 'user' provides us with a linearly converging solver
that gradually restores feasibility after each change in the design variables, we
derive a corresponding adjoint iteration and attach an optimization (sub)step.
The key question addressed is how the approximate reduced gradient generated
by the adjoint iteration should be preconditioned in order to achieve overall
convergence at a reasonable speed. An eigenvalue analysis yields necessary
conditions on the preconditioning matrix, which are typically not satised by
the familiar reduced Hessian. Some other projection of the Lagrangian Hessian
appears more promising and is found to work very satisfactorily on a nonlinear
test problem.
The analyzed approach is one-step in that the normal, dual and design variables
are always updated simultaneously on the basis of one function evaluation and
its adjoint. Multi-step variants are promising but remain to be investigated.
Markov (state) models (MSMs) have attracted a lot of interest recently as they (1) can probe
long-term molecular kinetics based on short-time simulations, (2) offer a way to analyze great
amounts of simulation data with relatively little subjectivity of the analyst, (3) provide insight into
microscopic quantities such as the ensemble of transition pathways, and (4) allow simulation data
to be reconciled with measurement data in a rigorous and explicit way. Here we sketch our current
perspective of Markov models and explain in short their theoretical basis and assumptions. We
describe transition path theory which allows the entire ensemble of protein folding pathways to be
investigated and that combines naturally with Markov models. Experimental observations can be
naturally linked to Markov models with the dynamical fingerprint theory, by which experimentally
observable timescales can be equipped with an understanding of the structural rearrangement
processes that take place at these timescales. The concepts of this paper are illustrated by a
simple kinetic model of protein folding.
Discrete-state Markov (or master equation) models provide a useful simplified representation for
characterizing the long-time statistical evolution of biomolecules in a manner that allows direct
comparison with experiments as well as the elucidation of mechanistic pathways for an inherently
stochastic process. A vital part of meaningful comparison with experiment is the characterization of
the statistical uncertainty in the predicted experimental measurement, which may take the form of
an equilibrium measurement of some spectroscopic signal, the time-evolution of this signal following
a perturbation, or the observation of some statistic (such as the correlation function) of the equilib-
rium dynamics of a single molecule. Without meaningful error bars (which arise due to the finite
quantity of data used to construct the model), there is no way to determine whether the deviations
between model and experiment are statistically meaningful. Previous work has demonstrated that
a Bayesian method that enforces microscopic reversibility can be used to characterize the correlated
uncertainties in state-to-state transition probabilities (and functions thereof) for a model inferred from
molecular simulation data. Here, we extend this approach to include the uncertainty in observables
that are functions of molecular conformation (such as surrogate spectroscopic signals) characteriz-
ing each state, permitting the full statistical uncertainty in computed spectroscopic experiments to be
assessed. We test the approach in a simple model system to demonstrate that the computed uncer-
tainties provide a useful indictor of statistical variation, and then apply it to the computation of the
fluorescence autocorrelation function measured for a dye-labeled peptide previously studied by both
experiment and simulation.
Large-scale stochastic models are relevant in many different fields such as com- putational biology, finance, social sciences, communication and traffic networks. In order to both efficiently simulate and analyze such models and to understand the essential properties of the sys- tem, it is desirable to have model reduction techniques that much reduce the dimensionality of the model while at the same time preserving the system’s essential dynamical properties. In this paper, a general model reduction technique for the class of discrete space and time Hidden Markov Models is presented, thereby also including the more special class discrete Markov Chains. The method is illustrated on some model applications.
Primal-dual linear Monte Carlo algorithm for multiple stopping - An application to flexible caps
(2012)
In this paper we consider the valuation of Bermudan callable derivatives with
multiple exercise rights. We present in this context a new primal-dual linear
Monte Carlo algorithm that allows for ecient simulation of lower and upper price
bounds without using nested simulations (hence the terminology). The algorithm
is essentially an extension of a primal{dual Monte Carlo algorithm for standard
Bermudan options proposed in Schoenmakers et al. (2011), to the case of multiple
exercise rights. In particular, the algorithm constructs upwardly a system of dual
martingales to be plugged into the dual representation of Schoenmakers (2010).
At each level the respective martingale is constructed via a backward regression
procedure starting at the last exercise date. The thus constructed martingales are
nally used to compute an upper price bound. At the same time, the algorithm
also provides approximate continuation functions which may be used to construct
a price lower bound. The algorithm is applied to the pricing of
exible caps
in a Hull and White (1990) model setup. The simple model choice allows for
comparison of the computed price bounds with the exact price which is obtained
by means of a trinomial tree implementation. As a result, we obtain tight price
bounds for the considered application. Moreover, the algorithm is generically
designed for multi-dimensional problems and is tractable to implement.
Primal heuristics are an important component of state-of-the-art codes for mixed integer nonlinear programming (MINLP). In this article we give a compact overview of primal heuristics for MINLP that have been suggested in the literature of recent years. We sketch the fundamental concepts of different classes of heuristics and discuss specific implementations. A brief computational experiment shows that primal heuristics play a key role in achieving feasibility and finding good primal bounds within a global MINLP solver.
We present two approximation methods for pricing of CMS spread options in Libor market models. Both approaches are based on approximating the underlying swap rates with lognormal processes under suitable measures. The first method is derived straightforwardly from the Libor market model. The second one uses a convexity adjustment technique under a linear swap model assumption. A numerical study demonstrates that both methods provide satisfactory approximations of spread option prices and can be used for calibration of a Libor market model to the CMS spread option market.
We show that pricing a big class of relevant options by hedging
and no-arbitrage can be extended beyond semimartingale models. To
this end we construct a subclass of self-financing portfolios that
contains hedges for these options, but does not contain arbitrage
opportunities, even if the stock price process is a
non-semimartingale of some special type.
Moreover, we show that the option prices depend
essentially only on a path property of the stock price process,
viz. on the quadratic variation. As a consequence, we can
incorporate many stylized facts to a pricing model without
changing the option prices.
This paper is concerned with the study of insurance related derivatives on financial markets that are based on non-tradable underlyings, but are correlated with tradable assets. We calculate exponential utility-based indifference prices, and corresponding derivative hedges. We use the fact that they can be represented in terms of solutions of forward-backward stochastic differential equations (FBSDE) with quadratic growth generators. We derive the Markov property of such FBSDE and generalize results on the differentiability relative to the initial value of their forward components. In this case the optimal hedge can be represented by the price gradient multiplied with the correlation coefficient. This way we obtain a generalization of the classical ‘delta hedge’ in complete markets.
Expected suprema of a function f observed along the paths of a nice Markov process define an excessive function, and in
fact a potential if f vanishes at the boundary. Conversely, we show under mild regularity conditions that any
potential admits a representation in terms of expected suprema. Moreover, we identify the maximal and the minimal
representing function in terms of probabilistic potential theory. Our results are motivated by the work of El Karoui and
Meziou on the max-plus decomposition of supermartingales, and they provide a singular analogue to
the non-linear Riesz representation in El Karoui and Föllmer.
We propose a model reduction method for positive systems that ensures the positivity of the reduced-order model. In the standard as well as in the descriptor case, for continuous-time and discrete-time systems, our approach is based on constructing diagonal solutions of Lyapunov inequalities. These are linear matrix inequalities (LMIs), which are shown to be feasible. Positivity and stability are preserved and an error bound in the $\mathcal{H}_\infty$-norm is provided.
We propose a model reduction method for positive systems that ensures the positivity of the reduced model. For both, continuous-time and discrete-time systems, our approach is based on constructing diagonal solutions of Lyapunov inequalities. These are linear matrix inequalities (LMIs), which are shown to be feasible. Stability is preserved and an error bound in the $\mathcal{H}_\infty$-norm is provided.
We introduce an algorithm for
diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction.
The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models.
It works by embedding the measurement space into a space with defined metric and group operations, in this case the Lie group of three-dimensional Euclidean motion SE(3).
Subsequently, pairwise comparisons of the values of the diffusion
weighted signal are used for adaptation.
The position-orientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures.
The POAS-algorithm is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and
variability of quantities derived from the data for specific models.
We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and
hence acquisition time while achieving similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.
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.
We consider stochastic programs with risk measures in the objective and study
stability properties as well as decomposition structures. Thereby we place emphasis on dynamic
models, i.e., multistage stochastic programs with multiperiod risk measures. In this context, we
define the class of polyhedral risk measures such that stochastic programs with risk measures taken
from this class have favorable properties. Polyhedral risk measures are defined as optimal values of
certain linear stochastic programs where the arguments of the risk measure appear on the right-hand
side of the dynamic constraints. Dual representations for polyhedral risk measures are derived and
used to deduce criteria for convexity and coherence. As examples of polyhedral risk measures we
propose multiperiod extensions of the Conditional-Value-at-Risk.
We compare different multiperiod risk measures taken from the class of polyhedral risk measures with respect to the effect
they show when used in the objective of a stochastic program. For this purpose, simulation results of a stochastic programming
model for optimizing the electricity portfolio of a German municipal power utility are presented and analyzed. This model
aims to minimize risk and expected overall cost simultaneously.
We prove global convergence of an inexact polyhedral Gau\ss--Seidel method for the minimization of strictly convex functionals that are continuously differentiable on each polyhedron of a polyhedral decomposition of
their domains of definition. While being known to be very slow by themselves, such methods are a cornerstone for fast, globally convergent multigrid methods. Our result generalizes the proof of Kornhuber and Krause [2006] for differentiable functionals on the Gibbs simplex. Example applications are given that require the generality of our approach.
The randomized k-number partitioning problem is the task to distribute N i.i.d. random variables into k groups in such a way that the sums of the variables in each group are as similar as possible. The restricted k-partitioning problem refers to the case where the number of elements in each group is fixed to N/k. In the case k = 2 it has been shown that the properly rescaled differences of the two sums in the close to optimal partitions converge to a Poisson point process, as if they were independent random variables. We generalize this result to the case k > 2 in the restricted problem and show that the vector of differences between the k sums converges to a k - 1-dimensional Poisson point process.
The main focus of this paper is on an a-posteriori analysis for the method of proper orthogonal decomposition (POD) applied to optimal control problems governed by
parabolic and elliptic PDEs. Based on a perturbation method it is deduced how far the suboptimal
control, computed on the basis of the POD model, is from the (unknown)
exact one. Numerical examples illustrate the realization of the proposed approach for linear-quadratic problems governed by parabolic and elliptic partial differential equations.
Millionen von Menschen werden allein in Deutschland täglich von
Bussen, Bahnen und Flugzeugen transportiert. Der öffentliche
Personenverkehr (ÖV) ist von großer Bedeutung für die
Lebensqualität einzelner aber auch für die Leistungsfähigkeit ganzer
Regionen. Qualität und Effizienz von ÖV-Systemen hängen ab von
politischen Rahmenbedingungen (staatlich geplant,
wettbewerblich organisiert) und der Eignung der Infrastruktur
(Schienensysteme, Flughafenstandorte), vom vorhandenen
Verkehrsangebot (Fahr- und Flugplan), von der Verwendung
angemessener Technologien (Informations-, Kontroll- und
Buchungssysteme) und dem bestmöglichen Einsatz der
Betriebsmittel (Energie, Fahrzeuge und Personal). Die hierbei
auftretenden Entscheidungs-, Planungs- und
Optimierungsprobleme sind z.T. gigantisch und "schreien"
aufgrund ihrer hohen Komplexität nach Unterstützung durch Mathematik.
Dieser Artikel skizziert den Stand und die Bedeutung des Einsatzes von
Mathematik bei der Planung und Durchführung von öffentlichem
Personenverkehr, beschreibt die bestehenden Herausforderungen und
regt zukunftsweisende Maßnahmen an.
Every day, millions of people are transported by buses, trains, and airplanes
in Germany. Public transit (PT) is of major importance for the quality of
life of individuals as well as the productivity of entire regions. Quality and
efficiency of PT systems depend on the political framework (state-run, market
oriented) and the suitability of the infrastructure (railway tracks, airport
locations), the existing level of service (timetable, flight schedule), the use
of adequate technologies (information, control, and booking systems), and
the best possible deployment of equipment and resources (energy, vehicles,
crews). The decision, planning, and optimization problems arising in this
context are often gigantic and “scream” for mathematical support because of
their complexity.
This article sketches the state and the relevance of mathematics in planning
and operating public transit, describes today’s challenges, and suggests a
number of innovative actions.
The current contribution of mathematics to public transit is — depending
on the transportation mode — of varying depth. Air traffic is already well
supported by mathematics. Bus traffic made significant advances in recent
years, while rail traffic still bears significant opportunities for improvements.
In all areas of public transit, the existing potentials are far from being exhausted.
For some PT problems, such as vehicle and crew scheduling in bus and
air traffic, excellent mathematical tools are not only available, but used in
many places. In other areas, such as rolling stock rostering in rail traffic,
the performance of the existing mathematical algorithms is not yet sufficient.
Some topics are essentially untouched from a mathematical point
of view; e.g., there are (except for air traffic) no network design or fare
planning models of practical relevance. PT infrastructure construction is
essentially devoid of mathematics, even though enormous capital investments
are made in this area. These problems lead to questions that can only be
tackled by engineers, economists, politicians, and mathematicians in a joint
effort.
Among other things, the authors propose to investigate two specific topics,
which can be addressed at short notice, are of fundamental importance not
only for the area of traffic planning, should lead to a significant improvement
in the collaboration of all involved parties, and, if successful, will be of real
value for companies and customers:
• discrete optimal control: real-time re-planning of traffic systems in case
of disruptions,
• model integration: service design in bus and rail traffic.
Work on these topics in interdisciplinary research projects could be funded
by the German ministry of research and education (BMBF), the German
ministry of economics (BMWi), or the German science foundation (DFG).
For selfadjoint matrices in an indefinite inner product, possible canonical forms are identified that arise when the matrix is subjected to a selfadjoint generic rank one perturbation. Genericity is understood in
the sense of algebraic geometry. Special attention is paid to the perturbation
behavior of the sign characteristic. Typically, under such a perturbation,
for every given eigenvalue, the largest Jordan block of the eigenvalue is
destroyed and (in case the eigenvalue is real) all other Jordan blocks
keep their sign characteristic. The new eigenvalues, i.e., those eigenvalues of
the perturbed matrix that are not eigenvalues of the original matrix,
are typically simple, and in some cases information is provided about their sign
characteristic (if the new eigenvalue is real). The main results are proved by using
the well known canonical forms of selfadjoint matrices in an indefinite inner product, a version of the Brunovsky
canonical form and on general results concerning rank one perturbations.
Motivated by the analysis of passive control systems, we undertake a detailed perturbation analysis of Hamiltonian matrices that have eigenvalues on the imaginary axis. We construct minimal Hamiltonian perturbations that move and coalesce eigenvalues of opposite sign characteristic to form multiple eigenvalues with mixed sign characteristics, which are then moved from the imaginary axis to specific locations in the complex plane by small Hamiltonian perturbations. We also present a numerical method to compute upper bounds for the minimal perturbations that move all eigenvalues of a given Hamiltonian matrix outside a vertical strip along the imaginary axis.
Perturbation of Purely Imaginary Eigenvalues of Hamiltonian Matrices under Structured Perturbations
(2007)
We discuss the perturbation theory for purely imaginary eigenvalues of Hamiltonian matrices under Hamiltonian and non-Hamiltonian perturbations. We
show that there is a substantial difference in the behavior under these perturbations. We also discuss the perturbation of real eigenvalues of real
skew-Hamiltonian matrices under structured perturbations and use these results to analyze the properties of the URV method of computing the
eigenvalues of Hamiltonian matrices.
Lagrangian invariant subspaces for symplectic matrices play an important role in the numerical solution of discrete time, robust and optimal control problems. The sensitivity (perturbation) analysis of these subspaces, however, is a difficult problem, in particular, when the eigenvalues are on or close to some critical regions in the complex plane, such as the unit circle.
We present a detailed perturbation analysis for several different cases of real and complex symplectic matrices. We analyze stability and conditional stability
as well as the index of stability for these subspaces.
We discuss the perturbation analysis for
eigenvalues and eigenvectors of structured homogeneous matrix polynomials with
Hermitian, skew-Hermitian, H-even and H-odd structure.
We construct minimal structured perturbations (structured backward errors) such that an
approximate eigenpair is an exact eigenpair of an appropriate perturbed structured matrix
polynomial. We present various comparisons with unstructured backward
errors and previous error bounds derived for the non-homogeneous case
and show that our bounds present a significant improvement.
In this work we propose a general framework for the structured perturbation
analysis of several classes of structured matrix polynomials in homogeneous
form, including complex symmetric, skew-symmetric, even and odd matrix polynomials. We introduce structured backward errors for approximate eigenvalues and eigenvectors and we construct minimal structured perturbations such that an approximate eigenpair is an exact eigenpair of an appropriately perturbed matrix polynomial. This work extends previous work for the non-homogeneous case (we include infinite eigenvalues) and we show that the structured backward errors improve the known unstructured backward errors.
Persistence of rogue waves in extended nonlinear Schrödinger equations: Integrable Sasa-Satsuma case
(2012)
We present the lowest order rogue wave solution of the Sasa-Satsuma equation (SSE) which is one of the integrable extensions of the nonlinear Schrödinger equation (NLSE). In contrast to the Peregrine solution of the NLSE, it is significantly more involved and contains polynomials of fourth order rather than second order in the corresponding expressions. The correct limiting case of Peregrine solution appears when the extension parameter of the SSE is reduced to zero.
Periodic Solutions to Dissipative Hyperbolic Systems. II: Hopf Bifurcation for Semilinear Problems
(2013)
We consider boundary value problems for semilinear hyperbolic systems of the type
$$
\partial_tu_j + a_j(x,\la)\partial_xu_j + b_j(x,\la,u) = 0, \; x\in(0,1), \;j=1,\dots,n
$$
with smooth coefficient functions $a_j$
and $b_j$
such that
$b_j(x,\la,0) = 0$ for all $x \in [0,1]$, $\la \in \R$, and $j=1,\ldots,n$.
We state conditions for Hopf bifurcation, i.e.,
for existence, local uniqueness (up to phase shifts), smoothness and smooth dependence
on $\la$
of time-periodic solutions bifurcating from the zero stationary solution. Furthermore,
we derive a formula which determines the bifurcation direction.
The proof is done by means of a Liapunov-Schmidt reduction procedure.
For this purpose, Fredholm properties of the linearized
system and implicit function
theorem techniques are used.
There are at least two distinguishing features of Hopf bifurcation theorems for hyperbolic PDEs in comparison with those for parabolic PDEs or for ODEs:
First, the question if a non-degenerate time-periodic solution depends smoothly on the system parameters
is much more delicate. And second,
a sufficient amount of dissipativity is needed in the system, and a priori
it is not clear how to verify this in terms of the data of the PDEs and of the boundary conditions.
Periodic Solutions to Dissipative Hyperbolic Systems. I: Fredholm Solvability of Linear Problems
(2013)
This paper concerns linear first-order hyperbolic systems in one space dimension of the type
$$
\partial_tu_j + a_j(x,t)\partial_xu_j + \sum\limits_{k=1}^nb_{jk}(x,t)u_k = f_j(x,t),\; x \in (0,1),\; j=1,\ldots,n,
$$
with periodicity conditions in time and reflection boundary conditions in space. We state a kind of dissipativity condition (depending on the coefficients $a_j$ and $b_{jj}$ and the boundary reflection coefficients), which implies Fredholm solvability of the problem, i.e., either there is a nontrivial solution to the homogeneous problem (in this case the space of such solutions has finite dimension) or the nonhomogeneous problem is uniquely solvable for any right-hand side (in this case the solution depends continuously on the right-hand side). In particular, under those conditions no small denominator effects occur.
Our results work for many non-strictly hyperbolic systems, but they are new even in the case of strict hyperbolicity.
Finally, in the case that all coefficients $a_j$ are $t$-independent, we show that the solutions are $C^\infty$-smooth if the data are $C^\infty$-smooth.
We describe the appearance and stability of spatio-temporal periodic
patterns (rotating waves) in unidirectional rings of coupled oscillators
with delayed couplings. We show how delays in the coupling lead
to a splitting of each rotating wave into several new ones. The appearance
of rotating waves is mediated by Hopf bifurcations of the symmetric
equilibrium.
We also conclude that the coupling delays can be effectively
replaced by increasing the number of oscillators in the chain.
The phenomena are shown for Stuart-Landau
oscillators as well as for coupled FitzHugh-Nagumo systems interacting
via excitatory chemical synapses.
During the last 15 years, there have been proposed many solution methods
for the important task of constructing periodic timetables for public transportation
companies. We first point out the importance of an objective function, where we
observe that in particular a linear objective function turns out to be a good compromise
between essential practical requirements and computational tractability. Then,
we enter into a detailed empirical analysis of various Mixed Integer Programming
procedures { such using nodes variables and such using arcs variables { genetic algorithms,
simulated annealing and constraint programming. To our knowledge, this
is the first comparison of five conceptually different solution approaches.
On rather small instances, an arc-based MIP formulation behaves best, when
refined by additional valid inequalities. On bigger instances, the solutions obtained
by a genetic algorithm are competitive to the solutions CPLEX was investigating
until it reached a time or memory limit. For Deutsche Bahn AG, the genetic algorithm
was most convincing on their various data sets, and it will become the first
automated timetable optimization software in use.
This paper is concerned with a PDE-constrained optimization problem of induction heating, where the state equations consist of 3D time--dependent heat equations coupled with 3D time--harmonic eddy current equations. The control parameters are given by finite real numbers representing applied alternating voltages which enter the eddy current equations via impressed current. The optimization problem is to find optimal voltages so that, under certain constraints on the voltages and the temperature, a desired temperature can be optimally achieved. As there are finitely many control parameters but the state constraint has to be satisfied in an infinite number of points, the problem belongs to a class of semi--infinite programming problems. We present a rigorous analysis of the optimization problem and a numerical strategy based on our theoretical result.
We consider backward stochastic differential equations with drivers of quadratic growth (qgBSDE). We prove several statements concerning path regularity and stochastic smoothness of the solution processes of the qgBSDE, in particular we prove an extension of Zhang's path regularity theorem to the quadratic growth setting. We give explicit convergence rates for the difference between the solution of a qgBSDE and its truncation, filling an important gap in numerics for qgBSDE. We give an alternative proof of second order Malliavin differentiability for BSDE with drivers that are Lipschitz continuous (and differentiable), and then derive the same result for qgBSDE.
We consider backward stochastic differential equations with drivers of quadratic growth (qgBSDE). We prove several statements concerning path regularity and stochastic smooth-
ness of the solution processes of the qgBSDE, in particular we prove an extension of Zhang's path regularity theorem to the quadratic growth setting. We give explicit convergence rates for the difference between the solution of a qgBSDE and its truncation, filling an important gap in numerics for qgBSDE. We give an alternative proof of second order Malliavin differentiability for BSDE with drivers that are Lipschitz continuous (and differentiable), and
then derive the same result for qgBSDE.
An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented.
The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is
solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set
strategy is added to the resolution technique.
Structuring of surface meshes is a labor intensive task in reverse engineering. For example in CAD, scanned triangle meshes must be divided into characteristic/uniform patches to enable conversion into high-level spline surfaces. Typical industrial techniques, like rolling ball blends, are very labor intensive.
We provide a novel, robust and quick algorithm for the automatic generation of a patch layout based on a topology consistent feature graph. The graph separates the surface along feature lines into functional and geometric building blocks. Our algorithm then thickens thickens the edges of the feature graph and forms new regions with low varying curvature. Further these new regions - so called fillets and node patches - will have highly smooth boundary curves making it an ideal preprocessor for a subsequent spline fitting algorithm.
Passivation of LTI systems
(2007)
In this paper we consider a passivation procedure for linear time-invariant systems.
This procedure is based on the spectral properties of related Hamiltonian matrices.
We also present a structure-preserving algorithm for computing the imaginary
eigenvalues and the corresponding eigenvectors of Hamiltonian matrices.
Numerical examples are given.
In this paper we formulate a boundary layer approximation
for an Allen--Cahn-type equation involving a small parameter $\eps$. Here, $\eps$ is related to the thickness of the boundary layer and we are interested in the limit when $\eps$ tends to $0$ in order to derive nontrivial boundary conditions. The evolution of the system is written as an energy balance formulation of the L^2-gradient flow with the corresponding Allen--Cahn energy functional. By transforming the boundary layer to a fixed domain we show the convergence of the solutions to a solution of a limit system. This is done by using concepts related to Gamma- and Mosco convergence. By considering different scalings in the boundary layer we obtain different boundary conditions.
We consider financial markets with agents exposed to an external source of
risk which cannot be hedged through investments on the capital market alone.
The sources of risk we think of may be weather and climate. Therefore we face
a typical example of an incomplete financial market. We design a model of a
market on which the external risk becomes tradable. In a first step we complete
the market by introducing an extra security which valuates the external risk
through a process parameter describing its market price. If this parameter is
fixed, risk has a price and every agent can maximize the expected exponential
utility with individual risk aversion obtained from his risk exposure on the one
hand and his investment into the financial market consisting of an exogenous set
of stocks and the insurance asset on the other hand. In the second step, the
market price of risk parameter has to be determined by a partial equilibrium
condition which just expresses the fact that in equilibrium the market is cleared
of the second security. This choice of market price of risk is performed in the
framework of nonlinear backwards stochastic differential equations.
Mixed integer programming (MIP) has become one of the most important techniques in Operations Research and Discrete Optimization. SCIP (Solving Constraint Integer Programs) is currently one of the fastest non-commercial MIP solvers. It is based on the branch-and-bound procedure in which the problem is recursively split into smaller subproblems, thereby creating a so-called branching tree. We present ParaSCIP, an extension of SCIP, which realizes a parallelization on a distributed memory computing environment. ParaSCIP uses SCIP solvers as independently running processes to solve subproblems (nodes of the branching tree) locally. This makes the parallelization development independent of the SCIP development. Thus, ParaSCIP directly profits from any algorithmic progress in future versions of SCIP. Using a first implementation of ParaSCIP, we were able to solve two previously unsolved instances from MIPLIB2003, a standard test set library for MIP solvers. For these computations, we used up to 2048 cores of the HLRN~II supercomputer.
Classical surface parameterization algorithms often place singularities
in order to enhance the quality of the resulting parameter map. Unfortunately, singularities of positive integral index (as the north pole of a sphere) were not handled since they cannot be described with piecewise linear parameter functions on a triangle mesh. Preprocessing is needed to adapt the mesh connectivity. We present an extension to the QuadCover parameterization algorithm [KNP07], which allows to handle those singularities. A singularity of positive integral index can be resolved using bilinear parameter functions on quadrilateral elements. This generalization
of piecewise linear functions for quadrilaterals enriches the space of parameterizations. The resulting parameter map can be visualized by textures using a rendering system which supports quadrilateral elements, or it can be used for remeshing into a pure quad mesh.
Structure-preserving generic low-rank perturbations are studied for classes of structured matrix pencils, including real symmetric, complex symmetric, and complex Hermitian pencils. For singular pencils it is analyzed which characteristic quantities stay invariant in the perturbed canonical form, and it is shown that the regular part of a structured matrix pencil is not affected by generic perturbations of rank one. When the rank one perturbations involve a scaling parameter, the behavior of the canonical forms in dependence of this parameter is analyzed as well.
We study non-isothermal nucleation and growth phase transformations, which are described by a generalized Avrami model for the phase transition coupled with an energy balance to account for recalescence
effects. The main novelty of our work is the identification of temperature dependent nucleation rates. We prove that such rates can be uniquely identified from measurements in a subdomain and apply an optimal control approach to develop a numerical strategy for its computation.
Motivation. Modelling, parameter identification, and simulation play an important role in systems biology. Usually, the goal is to determine parameter values that minimise the difference between experimental measurement values and model predictions in a least-squares sense. Large-scale biological networks, however, often suffer from missing data for parameter identification. Thus, the least-squares problems are rank-deficient and solutions are not unique. Many common optimisation methods ignore this detail because they do not take into account the structure of the underlying inverse problem. These algorithms simply return a “solution” without additional information on identifiability or uniqueness. This can yield misleading results, especially if parameters are co-regulated and data are noisy.
Results. The Gauss-Newton method presented in this paper monitors the numerical rank of the Jacobian and converges locally, for the class of adequate problems, to a solution that is unique within the subspace of identifiable parameters. This method has been implemented in BioPARKIN, a software package that combines state-of-the-art numerical algorithms with compliance to system biology standards, most importantly SBML, and an accessible interface.
Availability. The software package BioPARKIN is available for download at http://bioparkin.zib.de .
Time-stepping procedures for the solution of evolution equations can be performed on parallel architecture by parallelizing the space computation at each time step. This, however, requires heavy communication between processors and becomes inefficient when many time-steps are to be computed and many processors are available. In such
cases parallelization in time is advantageous.
In this paper we present a method for parallelization in time of linear multistep discretizations of linear evolution problems; we consider a model parabolic and a model hyperbolic problem, and their, respectively, A(theta)-stable and A-stable linear multistep discretizations. The method consists of a discrete decoupling procedure, whereby N+1 decoupled Helmholtz problems with complex frequencies are obtained; N being the number of time steps computed in parallel. The usefulness of the method rests on our ability to solve these Helmholtz problems efficiently. We discuss the theory and give numerical examples for multigrid preconditioned iterative solvers of relevant
complex frequency Helmholtz problems. The parallel
approach can easily be combined with a time-stepping procedure, thereby obtaining a block time-stepping method where each block of steps is computed in parallel. In this way we are able to optimize the algorithm with respect to the number of processors available, the difficulty of solving the Helmholtz problems, and the possibility of both time and space adaptivity. Extensions to other linear evolution problems and to Runge-Kutta time discretization
are briefly mentioned.
In this paper we investigate the use of parallel computing to deal with the high computational cost of numerical algorithms for model reduction of large linear descriptor systems. The state-space truncation methods considered here are composed of iterative schemes which can be efficiently implemented on parallel architectures using existing parallel linear algebra libraries. Our experimental results on a cluster of Intel Pentium processors show the performance of the parallel algorithms.
Palindromic polynomial eigenvalue problems and related classes of structured eigenvalue problems are
considered. These structures generalize the concepts of symplectic and Hamiltonian matrices to matrix polynomials.
We discuss several applications where these matrix polynomials arise, and show how linearizations can be derived that
re
ect the structure of all these structured matrix polynomials and therefore preserve symmetries in the spectrum.
We introduce orbitopes as the convex hulls of 0/1-matrices that are lexicographically maximal subject to a group acting on the
columns. Special cases are packing and partitioning orbitopes, which
arise from restrictions to matrices with at most or exactly one 1-entry
in each row, respectively. The goal of investigating these polytopes is to
gain insight into ways of breaking certain symmetries in integer programs
by adding constraints, e.g., for a well-known formulation of the graph
coloring problem.
We provide a thorough polyhedral investigation of packing and partitioning orbitopes for the cases in which the group acting on the columns
is the cyclic group or the symmetric group. Our main results are complete linear inequality descriptions of these polytopes by facet-defining
inequalities. For the cyclic group case, the descriptions turn out to be
totally unimodular, while for the symmetric group case both the description and the proof are more involved. Nevertheless, the associated
separation problem can be solved in linear time also in this case.
In large-area Organic Light-Emitting Diodes (OLEDs) spatially inhomogeneous luminance at high power due to inhomogeneous current flow and electrothermal feedback can be observed. To describe these self-heating effects in organic semiconductors we present a stationary thermistor model based on the heat equation for the temperature coupled to a p-Laplace-type equation for the electrostatic potential with mixed boundary conditions. The p-Laplacian describes the non-Ohmic electrical behavior of the organic material. Moreover, an Arrhenius-like temperature dependency of the electrical conductivity is considered.
We introduce a finite-volume scheme for the system and discuss its relation to recent network models for OLEDs. In two spatial dimensions we derive a priori estimates for the temperature and the electrostatic potential and prove the existence of a weak solution by Schauder's fixed point theorem.
Orbitopal Fixing
(2007)
The topic of this paper are integer programming models in which a subset of 0/1-variables encode a partitioning of a set of objects into disjoint subsets. Such models can be surprisingly hard to solve by branch-and-cut algorithms if the order of the subsets of the partition is irrelevant. This kind of symmetry unnecessarily blows up the branch-and-cut tree.
We present a general tool, called orbitopal fixing, for enhancing the capabilities of branch-and-cut algorithms in solving such symmetric integer programming models. We devise a linear time algorithm that,
applied at each node of the branch-and-cut tree, removes redundant parts
of the tree produced by the above mentioned symmetry. The method relies on certain polyhedra, called orbitopes, which have been investigated in (Kaibel and Pfetsch 2007). It does, however, not add inequalities to the model, and thus, it does not increase the difficulty of solving the linear programming relaxations. We demonstrate the computational power of orbitopal fixing at the example of a graph partitioning problem motivated from frequency planning in mobile telecommunication networks.
We use a numerical optimization method to determine the control parameters
frequency, power, and coil position for the radio frequency (RF) induction
heating of the growth apparatus during sublimation growth of SiC single crystals
via physical vapor transport (PVT) (also called the modified Lely method). The
control parameters are determined to minimize a functional, tuning the radial
temperature gradient on the single crystal surface as well as the vertical temperature
gradient between SiC source and seed, both being crucial for high-quality
growth. The optimization is subject to constraints with respect to a required
temperature difference between source and seed, a required temperature range at
the seed, and an upper bound for the temperature in the entire apparatus. The
numerical computations use a stationary mathematical model for the heat transport,
including heat conduction, radiation, and RF heating to solve the forward
problem, and a Nelder-Mead method for optimization. A minimal radial temperature
gradient is found to coincide with a minimal temperature at the single
crystal surface, and a maximal temperature gradient between source and seed is
found to coincide with a low coil position.
This paper concerns the problem of operating a landside container exchange area that is serviced by multiple semi-automated rail mounted gantry cranes (RMGs) that are moving on a single bi-directional traveling lane. Such a facility is being built by Patrick Corporation at the Port Botany terminal in Sydney. The gantry cranes are a scarce resource and handle the bulk of container
movements. Thus, they require a sophisticated analysis to achieve near optimal utilization. We present a three stage algorithm to manage the container exchange facility, including the scheduling of cranes, the control of associated short-term container stacking, and
the allocation of delivery locations for trucks and other container transporters. The key components of our approach are a time scale decomposition, whereby an integer program controls decisions across a long time horizon to produce a balanced plan that is fed to a series of short time scale online subproblems, and a highly efficient space-time divisioning of short term storage areas.
A computational evaluation shows that our heuristic can find effective solutions for the planning problem; on real-world data it yields a solution at most 8% above a lower bound on optimal RMG utilization.
The paper examines the applicability of mathematical programming methods to the simultaneous optimization of the structure and the operational parameters of a combined-cycle-based cogeneration plant. The optimization problem is formulated as a nonconvex mixed-integer nonlinear problem (MINLP) and solved by the MINLP solver LaGO. The algorithm generates a convex relaxation of the MINLP and applies a Branch and Cut algorithm to the relaxation. Numerical results for different demands for electric power and process steam are discussed and a sensitivity analysis is performed.
We study the optimization of three dimensional curved rods and of shells
under minimal regularity assumptions for the geometry. The results that we
establish concern the existence of optimal shapes and the sensitivity analysis.
We also compute several numerical examples for the curved rods. The models
that we use have been investigated in our previous work [11], [16] and a
complete study of the Kirchhoff-Love arches and their optimization has been
performed in [10].
In commodity transport networks such as natural gas, hydrogen and water networks, flows arise from nonlinear potential differences between the nodes, which can be represented by so-called “potential-driven” network models. When operators of these networks face increasing demand or the need to handle more diverse transport situations, they regularly seek to expand the capacity of their network by building new pipelines parallel to existing ones (“looping”). The paper introduces a new mixed-integer non-linear programming (MINLP) model and a new non-linear programming (NLP) model and compares these with existing models for the looping problem and related problems in the literature, both theoretically and experimentally. On this basis, we give recommendations about the circumstances under which a certain model should be used. In particular, it turns out that one of our novel models outperforms the existing models.
Moreover, the paper is the first to include the practically relevant option that a particular pipeline may be looped several times.
The UMTS radio network planning problem poses the challenge of designing a cost-effective network that provides users with sufficient coverage and capacity. We describe an optimization model for this problem that is based on comprehensive planning data of the EU project MOMENTUM. We present heuristic mathematical methods for this realistic model, including computational results.
Im Zuge der Übernahme von 6 Linien der Havelbus Verkehrsgesellschaft mbH durch die ViP Verkehr in Potsdam GmbH ergab sich 2009 die Notwendigkeit der Entwicklung eines neuen Linien- und Taktplans für das Jahr 2010. Das Konrad-Zuse-Zentrum für Informationstechnik Berlin (ZIB) entwickelt in einem Projekt des DFG-Forschungszentrums Matheon ein Verfahren zur mathematischen Linienoptimierung. Dieses Tool wurde bei der Optimierung des ViP Linienplans 2010 in einer projektbegleitenden Studie eingesetzt, um Alternativen bei verschiedenen Planungs- und Zielvorgaben auszuloten. In dem Artikel wird eine Auswertung der Ergebnisse mit dem Verkehrsanalysesystem Visum der PTV AG beschrieben. Die Auswertungen bestätigen, dass mit Hilfe von mathematischer Optimierung eine weitere Verkürzung der Reisezeit um 1%, eine als um 6% verkürzt empfundene Reisezeit, 10% weniger Fahrzeit im Fahrzeug und eine gleichzeitige Kostenreduktion um 5% möglich sind
Optimality Conditions for State-Constrained PDE Control Problems with Time-Dependent Controls
(2008)
The paper deals with optimal control problems for semilinear
elliptic and parabolic PDEs subject to pointwise state constraints.
The main issue is that the controls are taken from a restricted
control space. In the parabolic case, they are vector-valued
functions of the time, while they are vectors in elliptic
problems. Under natural assumptions, first- and second-order
sufficient optimality conditions are derived. The main result is the
extension of second-order sufficient conditions to semilinear
parabolic equations in domains of arbitrary dimension. In the
elliptic case, the problems can be handled by known results of
semi-infinite optimization. Here, different examples are discussed
that exhibit different forms of active sets and where second-order
sufficient conditions are satisfied at the optimal solution.
We discuss first order optimality conditions for state constrained
optimal control problems. Our concern is the treatment of problems,
where the solution of the state equation is not known to be continuous,
as in the case of boundary control in three space dimensions or optimal
control with parabolic partial differential equations. We show existence of
measure valued Lagrangian multipliers, which have just enough additional
regularity to be applicable to all possibly discontinuous solutions of the
state equation.
Optimality conditions for a class of optimal control problems with quasilinear elliptic equations
(2008)
A class of optimal control problems for quasilinear elliptic
equations is considered, where the coefficients of the elliptic
differential operator depend on the state function. First- and
second-order optimality conditions are discussed for an associated
control-constrained optimal control problem. In particular, the
Pontryagin maximum principle and second-order sufficient
optimality conditions are derived. One of the main difficulties is
the non-monotone character of the state equation.
Es wird eine notwendige Optimalitäts-Bedingung für das linearquadratische
Optimierungsproblem mit differentiell-algebraischen Gleichungen
im Fall Index 2 bewiesen. Im ersten Schritt wird die kausale
DAE betrachtet. Im zweiten Schritt wird ein neuer Lösungs-Begriff für
nicht-kausale DAEs eingeführt und ein spezielles Kostenfunktional betrachtet,
welches für stetige Steuer-Funktionen nur den stetigen Teil
der Zustands-Variablen bewertet. Es stellt sich heraus, dass die adjungierte
Gleichung dann kausal ist und somit eine stetige Lösung besitzt.
Es wird die erweiterte Hessenberg-Form für DAEs eingeführt, die es
unter anderem ermöglicht, Beziehungen der DAE zu ihrer adjungierten
Gleichung im Optimierungs-Problem einfacher zu untersuchen.
We develop a generic method for constructing a weak static minimum
variance hedge for a wide range of derivatives that may involve optimal exercise features or contingent cash flow streams, to provide a hedge along a
sequence of future hedging dates. The optimal hedge is constructed using
a portfolio of preselected hedge instruments which could be derivatives
with different maturities. The hedge portfolio is weakly static in that
it is initiated at time zero, does not involve intermediate re-balancing,
but hedges may be gradually unwound over time. We study the static
hedging of a convertible bond to demonstrate the method by an example
that involves equity and credit risk. We investigate the robustness of the
hedge performance with respect to parameter and model risk by numerical
experiments.
A mathematical model is set up that can be useful for controlled voltage excitation in time-dependent electromagnetism.
The well-posedness of the model is proved and an associated optimal control problem is investigated. Here, the control
function is a transient voltage and the aim of the control is the best approximation of desired electric and magnetic fields in
suitable $L^2$-norms.
Special emphasis is laid on an adjoint calculus for first-order necessary optimality conditions.
Moreover, a {peculiar attention is devoted to propose a formulation for which the computational complexity of the finite element solution method is substantially reduced}.
We discuss shape optimization problems for cylindrical tubes that are loaded by time-dependent applied force. This is a problem of shape optimization that leads to optimal control in linear elasticity theory. We determine the optimal thickness of a cylindrical tube minimizing the deformation of the tube under the influence of the external force. The main difficulty is that the state equation is a hyperbolic partial differential equation of 4th order. First order necessary conditions for the optimal solution are derived. Based on them, a numerical method is set up and numerical examples are presented.
Optimal Thickness of a Cylindrical Shell -- An Optimal Control Problem in Linear Elasticity Theory
(2012)
In this paper we discuss optimization problems for cylindrical tubes which are loaded by an applied force. This is a problem of optimal control in linear elasticity theory (shape optimization). We are looking for an optimal thickness minimizing the deflection (deformation) of the tube under the influence of an external force.
From basic equations of mechanics, we derive the equation of deformation. We apply the displacement approach from shell theory and make use of the hypotheses of
Mindlin and Reissner. A corresponding optimal control problem is formulated and first order necessary conditions for the optimal solution (optimal thickness) are derived.
We present numerical examples which were solved by the finite element method.
We apply theoretical results of S. Peng on supersolutions for BS-DEs
to the problem of finding optimal superhedging strategies in a
Black-Scholes market under constraints. Constraints may be imposed
simultaneously on wealth process and portfolio. They may be nonconvex,
time-dependent, and random. Constraints on the portfolio may
e.g. be formulated in terms of the amount of money invested, the portfolio
proportion, or the number of shares held.
We prove an optimal regularity result for elliptic operators $-\nabla \cdot \mu \nabla:W^1,q_0 \rightarrow W^-1,q$ for a $q>3$ in the case when the coefficient function $\mu$ has a jump across a $C^1$ interface and is continuous elsewhere. A counterexample shows that the $C^1$ condition cannot be relaxed in general. Finally, we draw some conclusions for corresponding parabolic operators.
We study an optimal control problem (OCP) subject to a PDE of elliptic type as well as state constraints. The resulting optimality system contains two PDEs, one algebraic equation and the so called complementary slackness conditions, i.e. dual products between function spaces. At this point different regularization techniques come into use.
In this paper we introduce a Barrier method as one possible way to regularize state constraints, which leads to an easily implementable path-following algorithm.
To illustrate this method, we solve first a constructed problem with known solution. Here, we can verify the rate of convergence of the path-following method. Second, a simplified hyperthermia problem in 3D is solved by using COMSOL Multiphysics.
We introduce a systematic approach to the problem of maximizing the robust
utility of the terminal wealth of an admissible strategy in a general complete market
model, where the robust utility functional is defined by a set Q of probability measures.
Our main result shows that this problem can be reduced to determining a “least favorable”
measure Q0 2 Q, which is universal in the sense that it does not depend on the
particular utility function. The robust problem is thus equivalent to a standard utility
maximization problem with respect to the “subjective” probability measure Q0. By using
the Huber-Strassen theorem from robust statistics, it is shown that Q0 always exists if Q
is the core of a 2-alternating upper probability. We also discuss the problem of robust
utility maximization with uncertain drift in a Black-Scholes market and the case of “weak
information” as studied by Baudoin (2002).
Optimal Identification of Semi-Rigid Domains in Macromolecules from Molecular Dynamics Simulation
(2012)
Biological function relies on the fact that biomolecules can switch between different conformations and aggregation states.
Such transitions involve a rearrangement of parts of the biomolecules involved that act as dynamic domains. The reliable
identification of such domains is thus a key problem in biophysics. In this work we present a method to identify semi-rigid
domains based on dynamical data that can be obtained from molecular dynamics simulations or experiments. To this end
the average inter-atomic distance-deviations are computed. The resulting matrix is then clustered by a constrained
quadratic optimization problem. The reliability and performance of the method are demonstrated for two artificial peptides.
Furthermore we correlate the mechanical properties with biological malfunction in three variants of amyloidogenic
transthyretin protein, where the method reveals that a pathological mutation destabilizes the natural dimer structure of the
protein. Finally the method is used to identify functional domains of the GroEL-GroES chaperone, thus illustrating the
efficiency of the method for large biomolecular machines.
The fare planning problem for public transport is to design a system of fares that maximize the revenue. We introduce a nonlinear optimization model to approach this problem. It is based on a discrete choice logit model that expresses demand as a function of the fares. We illustrate our approach by computing and comparing two different fare systems for the intercity network of the Netherlands.
We investigate optimal elliptic
regularity (within the scale of Sobolev spaces) of anisotropic
div--grad operators in three dimensions at a multi-material vertex on
the Neumann boundary part of a polyhedral spatial domain. The
gradient of a solution to the corresponding elliptic PDE (in a
neighbourhood of the vertex) is integrable to an index greater than
three.
Optimal dual martingales, their analysis and application to new algorithms for Bermudan products
(2012)
In this paper we introduce and study the concept of optimal and surely
optimal dual martingales in the context of dual valuation of Bermudan
options, and outline the development of new algorithms in this context.
We provide a characterization theorem, a theorem which gives conditions
for a martingale to be surely optimal, and a stability theorem concerning martingales which are near to be surely optimal in a sense. Guided
by these results we develop a framework of backward algorithms for constructing such a martingale. In turn this martingale may then be utilized
for computing an upper bound of the Bermudan product. The methodology is pure dual in the sense that it doesn't require certain (input)
approximations to the Snell envelope.
In an Ito-Levy environment we outline a particular regression based
backward algorithm which allows for computing dual upper bounds with-
out nested Monte Carlo simulation. Moreover, as a by-product this algorithm also provides approximations to the continuation values of the
product, which in turn determine a stopping policy. Hence, we may obtain lower bounds at the same time.
In a first numerical study we demonstrate a backward dual regression algorithm in a Wiener environment that is easy to implement and
is regarding accuracy comparable with the method of Belomestny et. al.
(2009).
Optimal dual martingales, their analysis and application to new algorithms for Bermudan products
(2012)
In this paper we introduce and study the concept of optimal and surely
optimal dual martingales in the context of dual valuation of Bermudan
options, and outline the development of new algorithms in this context.
We provide a characterization theorem, a theorem which gives conditions
for a martingale to be surely optimal, and a stability theorem concerning martingales which are near to be surely optimal in a sense. Guided
by these results we develop a framework of backward algorithms for constructing such a martingale. In turn this martingale may then be utilized
for computing an upper bound of the Bermudan product. The methodology is pure dual in the sense that it doesn’t require certain (input)
approximations to the Snell envelope.
In an Ito-Levy environment we outline a particular regression based
backward algorithm which allows for computing dual upper bounds without nested Monte Carlo simulation. Moreover, as a by-product this algorithm also provides approximations to the continuation values of the
product, which in turn determine a stopping policy. Hence, we may obtain lower bounds at the same time.
In a first numerical study we demonstrate a backward dual regression algorithm in a Wiener environment that is easy to implement and
is regarding accuracy comparable with the method of Belomestny et. al.
(2009).
We prove new properties for the linear isotropic elasticity system and for
thickness minimization problems. We also present very recent results concerning
shape optimization problems for three-dimensional curved rods and
for shells. The questions discussed in this paper are related to the control
variational method and to control into coefficients problems.
In this paper, we study the influence of technology, traffic properties and price trends on optimized
design of a reference IP-over-WDM network with rich underlying fiber topology. In each network node,
we investigate the optimal degree of traffic switching in an optical (lambda) domain versus an electrical
(packet) domain, also known as measure of \emph{node transparency}. This measure is studied in connection to changes in
traffic volume,
demand affinity, optical circuit speeds and equipment cost. By applying variable design constraints,
we assess the relative roles of the two distinct equipment groups, IP routers and optical
cross-connects, with respect to resulting changes in cost-sensitive network architectures
We consider insurance derivatives depending on an external physical risk process, for example a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference prices of the derivatives, and interpret them in terms of diversification pressure. Moreover we check the optimal investment strategies for standard admissibility criteria. Finally we compare the static risk connected with an insurance derivative to the reduced risk due to a dynamic investment into the correlated asset. We show that dynamic hedging reduces the risk aversion in terms of entropic risk measures by a factor related to the correlation.
This paper is concerned with the state-constrained optimal control of the
two-dimensional thermistor problem, a quasi-linear coupled system
of a parabolic and elliptic PDE with mixed boundary conditions.
This system models the heating of a conducting material by means of direct current.
Existence, uniqueness and continuity for the state system are derived by employing
maximal elliptic and parabolic regularity. By similar arguments the
linearized state system is discussed, while the adjoint system involving measures
is investigated using a duality argument. These results allow to derive
first-order necessary conditions for the optimal control problem.
An optimal control problem is studied for a quasilinear Maxwell equation of nondegenerate parabolic
type. Well-posedness of the quasilinear state equation, existence of an optimal control, and weak G\^ateaux-differentiability of the control-to-state mapping are proved. Based on these results, first-order necessary optimality conditions and
an associated adjoint calculus are derived.
This paper addresses the regularization of pointwise state constraints in optimal
control problems. By analyzing the associated dual problem, it is shown that the regularized problems
admit Lagrange multipliers in L2-spaces. Under a certain boundedness assumption, the solution of
the regularized problem converges to the one of the original state constrained problem. The results
of our analysis are confirmed by numerical tests.
This paper is devoted to an optimal control problem of Maxwell's equations in the presence of pointwise state constraints. The control is given by a divergence--free three--dimensional vector function representing an applied current density. To cope with the divergence--free constraint on the control, we consider a vector potential ansatz. Due to the lack of regularity of the control--to--state mapping, existence of Lagrange multipliers cannot be guaranteed. We regularize the optimal control problem by penalizing the pointwise state constraints. Optimality conditions for the regularized problem can be derived straightforwardly. It also turns out that the solution of the regularized problem enjoys higher regularity which then allows us to establish its convergence towards the solution of the unregularized problem. The second part of the paper focuses on the numerical analysis of the regularized optimal control problem. Here the state and the control are discretized by N\'ed\'elec's curl--conforming edge elements. Employing the higher regularity property of the optimal control, we establish an a priori error estimate for the discretization error in the $\boldsymbol{H}(\bold{curl})$--norm. The paper ends by numerical results including a numerical verification of our theoretical results.
Several classes of optimal control of electromagnetic fields are considered. Special emphasis is
laid on a non-standard $H$-based formulation of the equations of electromagnetism in multiply connected conductors. By this technique, the Maxwell equations can be solved with reduced computational complexity. While the magnetic field $H$ in the conductor is obtained from an elliptic equation
with the $\curl \sigma^{-1} \curl$ operator, an elliptic equation with the $\div \mu \nabla$ operator is set up for a potential $\psi$ in the isolator.
Both equations are coupled by appropriate interface conditions. In all problems, the
electrical current is controlled in the conducting domain. Several types of control functions are discussed. In particular, the problem of sparse optimal control is investigated in a package of electrical wires. For all problems, the associated sensitivity
analysis is performed.
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.
We consider a control constrained optimal control problem governed by a semilinear
elliptic equation with nonlocal interface conditions. These conditions occur during the modeling of
diffuse-gray conductive-radiative heat transfer. The problem arises from the aim to optimize the
temperature gradient within crystal growth by the physical vapor transport (PVT) method. Based
on a minimum principle for the semilinear equation as well as L1-estimates for the weak solution,
we establish the existence of an optimal solution as well as necessary optimality conditions. The
theoretical results are illustrated by results of numerical computations.
This paper is concerned with the distributed optimal control of a
time-discrete Cahn--Hilliard/Navier--Stokes system with variable
densities.
It focuses on the double-obstacle potential which yields an optimal
control problem for a family of coupled systems in each time instance of a
variational inequality of fourth order and the Navier--Stokes equation.
By proposing a suitable time-discretization, energy estimates are proved
and the existence of solutions to the primal system and of optimal
controls is established for the original problem as well as for a family
of regularized problems. The latter correspond to Moreau--Yosida type
approximations of the double-obstacle potential. The consistency of these
approximations is shown and first order optimality conditions for the
regularized problems are derived. Through a limit process, a stationarity
system for the original problem is established which is related to a
function space version of C-stationarity.
This paper is concerned with the distributed optimal control of a
time-discrete Cahn--Hilliard/Navier--Stokes system with variable
densities.
It focuses on the double-obstacle potential which yields an optimal
control problem for a family of coupled systems in each time instance of a
variational inequality of fourth order and the Navier--Stokes equation.
By proposing a suitable time-discretization, energy estimates are proved
and the existence of solutions to the primal system and of optimal
controls is established for the original problem as well as for a family
of regularized problems. The latter correspond to Moreau--Yosida type
approximations of the double-obstacle potential. The consistency of these
approximations is shown and first order optimality conditions for the
regularized problems are derived. Through a limit process, a stationarity
system for the original problem is established which is related to a
function space version of C-stationarity.
An optimal control problem arising in the context of 3D electromagnetic induction heating is investigated. The state equation is given by a quasilinear stationary heat equation coupled with a semilinear time-harmonic eddy current equation. The temperature-dependent electrical conductivity and the presence of pointwise inequality state-constraints represent the main challenge of the paper. In the first part of the paper, the existence and regularity of the state are addressed. The second part of the paper deals with the analysis of the corresponding linearized equation. Some sufficient conditions are presented which guarantee the solvability of the linearized system. The final part of the paper is concerned with the optimal control. The aim of the optimization is to find the optimal voltage such that a desired temperature can be achieved optimally. The corresponding first-order necessary optimality condition is presented.
Optimal control of 3D state-constrained induction heating problems with nonlocal radiation effects
(2009)
The paper is concerned with a class of optimal heating problems in semiconductor single crystal growth processes. To model the heating process, time-harmonic Maxwell equations are considered in the system of the state. Due to the high temperatures characterizing crystal growth, it is necessary to include nonlocal radiation boundary conditions and a temperature-dependent heat conductivity in the description of the heat transfer process. The first goal of this paper is to prove the existence and uniqueness of the solution to the state equation. The regularity analysis associated with the time harmonic Maxwell equations is also studied. In the second part of the paper, the existence and uniqueness of the solution to the corresponding linearized equation is shown. With this result at hand, the differentiability of the control-to-state mapping operator associated with the state equation is derived. Finally, based on the theoretical results, first oder necessary optimality conditi!
ons for an associated optimal control problem are established.