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- optimal control (9)
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- stochastic programming (4)
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The paper presents a new affine invariant theory on asymptotic mesh
independence of Newton’s method for discretized nonlinear operator equations.
Compared to earlier attempts, the new approach is both much simpler
and more intuitive from the algorithmic point of view. The theory
is exemplified at collocation methods for ODE boundary value problems
and at finite element methods for elliptic PDE problems.
In this paper a nonlocal phase-field model for non-isothermal phase transitions
with a non-conserved order parameter is studied. The paper complements
recent investigations by S. Zheng and the second author and treats
the case when the part of the free energy density forcing the order parameter
to attain values within the physically meaningful range [0; 1] is not given
by a logarithmic expression but by the indicator function of [0; 1] . The resulting
field equations form a system of integro-partial differential inclusions
that are highly nonlinearly coupled. For this system, results concerning global
existence, uniqueness and large-time asymptotic behaviour are derived. The
main results are proved by first transforming the system of inclusions into an
equivalent system of equations in which hysteresis operators occur, and then
employing techniques similar to those recently developed by the authors for
phase-field systems involving hysteresis operators.
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].
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.
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 discuss a parallel library of efficient algorithms for model reduction of largescale
systems with state-space dimension up to O(104). We survey the numerical
algorithms underlying the implementation of the chosen model reduction methods.
The approach considered here is based on state-space truncation of the system
matrices and includes absolute and relative error methods for both stable and unstable
systems. In contrast to serial implementations of these methods, we employ
Newton-type iterative algorithms for the solution of the major computational tasks.
Experimental results report the numerical accuracy and the parallel performance of
our approach on a cluster of Intel Pentium II processors.
An optimal control problem for a 2-d elliptic equation is investigated with pointwise control constraints.
This paper is concerned with discretization of the control by piecewise constant functions. The state and
the adjoint state are discretized by linear finite elements. Approximations of the optimal solution of the continuous
optimal control problem will be constructed by a projection of the discrete adjoint state. It is proved that these
approximations have convergence order h2.
Dynamics of a surface-tension-gradient-driven liquid film rising from a reservoir onto a substrate
(2006)
On a tilted heated substrate, surface tension gradients can draw liquid up
out of a reservoir. The resulting film thickness profile is controlled by the tilt
of the substrate, the imposed temperature gradient, and the thickness of a
postulated thin precursor layer. The evolution of this film in time is studied
using a lubrication model. A number of distinct behaviours are possible as
the substrate tilt angle and other parameters are varied. Recent results for
the multiple stationary profiles possible near the meniscus are used, and the
interaction of these profiles with the advancing front is examined. It is shown
how to systematically determine the evolution of the entire film profile from
the meniscus to the apparent contact line. This allows a categorisation of the
range of behaviours for a transversely uniform profile, in a twodimensional
parameter space. In addition to capillary fronts, and double shock structures,
a new combination of a Type I meniscus with a rarefaction fan, and either an
undercompressive or a classical wave at the advancing front, that arises for
certain ranges of large substrate tilt and precursor thickness is described.
We present an applied mathematical model with stochastic input data for mean-risk optimization of electricity portfolios containing electricity futures as well as several components to satisfy a stochastic electricity demand: electricity spot market, two different types of supply contracts offered by a large power producer, and a combined heat and power production facility with limited capacity. Stochasticity enters the model via uncertain electricity demand, heat demand, spot prices, and future prices. The model is set up as a decision support system for a municipal power utility (price taker) and considers a medium term optimization horizon of one year in hourly discretization. The objective is to maximize the expected overall revenue and, simultaneously, to minimize risk in terms of multiperiod risk measures. Such risk measures take into account intermediate cash values in order to avoid uncertainty and liquidity problems at any time. We compare the effect of different multiperiod risk measures taken from the class of polyhedral risk measures which was suggested in our earlier work.
Modern electricity portfolio and risk management models represent multistage stochastic programs. The input of such programs consists in a finite set of scenarios having the form of a scenario tree. They model the probabilistic information on random data (electrical load, stream flows to hydro units, market prices of fuel and electricity). Since the corresponding deterministic equivalents of multistage stochastic programs are mostly large scale, one has to find significant tree-structured scenarios. Our approach to generate multivariate scenario trees is based on recursive deletion and bundling of scenarios out of some given (possibly large) scenario set originating from historical or simulated data. The procedure makes use of certain Monge-Kantorovich transportation distances for multivariate probability distributions. We report on computational results for generating load-inflow scenario
trees based on realistic data of EDF Electricité de France.
In this paper, the one-dimensional equation for the transversal vibrations of an elastoplastic beam is derived from a general
three-dimensional system. The plastic behavior is modeled using the classical
three-dimensional von Mises plasticity model. It turns out that this single-yield model leads after a dimensional reduction to a multi-yield one-dimensional hysteresis model,
given by a hysteresis operator of Prandtl-Ishlinskii type whose density
function can be determined explicitly. This result indicates that the use
of Prandtl-Ishlinskii hysteresis operators in the modeling of elastoplasticity
is not just a questionable phenomenological approach, but in fact quite natural. In addition to the derivation of the model, it is shown that the resulting partial differential equation with hysteresis can be transformed into an equivalent system for which the existence and uniqueness
of a strong solution is proved. The proof employs techniques from the mathematical theory of hysteresis operators.
We study optimal control problems for general unstructured nonlinear differential-algebraic equations of arbitrary index.
In particular, we derive necessary conditions in the case of linear-quadratic control problems and extend them to the general nonlinear case.
We also present a Pontryagin maximum principle for general unstructured nonlinear DAEs in the case of restricted controls.
Moreover, we discuss the numerical solution of the resulting two-point boundary value problems and present a numerical example.
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.
In this paper, we study an optimal control problem for a singular system of partial differential equations that models a nonisothermal phase transition with a nonconserved order parameter. The control acts through a third boundary condition for the absolute temperature and plays the role of the outside temperature. It is shown that the corresponding control-to-state mapping is well defined, and the existence of an optimal control and the first-order optimality conditions for a quadratic cost functional of Bolza type are established.
We extend earlier work on scenario reduction by relying directly on Fortet-Mourier metrics instead of using upper bounds given in terms of mass transportation problems. The importance of Fortet-Mourier metrics for quantitative stability of two-stage models is reviewed and some numerical results are also provided.
A class of optimal control problem for a semilinear elliptic partial differential equation
with control constraints is considered. It is well known that
sufficient second-order conditions ensure the stability of optimal solutions, the convergence of
numerical methods. Otherwise, such conditions are very difficult to verify (analytically or numerically).
We will propose a new approach: Starting with a numerical solution for a fixed mesh we will
show the existence of a local minimizer of the continuous problem. Moreover, we will prove that
this minimizer satisfies the sufficient second-order conditions.
We prove the existence, uniqueness, thermodynamic consistency,
global boundedness from both above and below, and continuous data
dependence for a strong solution to an
integrodifferential model for nonisothermal phase transitions
under nonhomogeneous mixed boundary conditions.
The specific heat is allowed to depend on the order parameter,
and the convex component of the free energy may or may not
be singular.