Optimal control problem for deflection plate with crack

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A framework for efficient model transformations

The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object oriented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages.

Merkmalsbezogene Verifikation hochaufgelöster Niederschlagsvorhersagen für Deutschland

Die Verifikation bewertet die Güte von quantitativen Niederschlagsvorhersagen(QNV) gegenüber Beobachtungen und liefert Hinweise auf systematische Modellfehler. Mit Hilfe der merkmals-bezogenen Technik SAL werden simulierte Niederschlagsverteilungen hinsichtlich (S)truktur, (A)mplitude und (L)ocation analysiert. Seit einigen Jahren werden numerische Wettervorhersagemodelle benutzt, mit Gitterpunktabständen, die es erlauben, hochreichende Konvektion ohne Parametrisierung zu simulieren. Es stellt sich jetzt die Frage, ob diese Modelle bessere Vorhersagen liefern. Der hoch aufgelöste stündliche Beobachtungsdatensatz, der in dieser Arbeit verwendet wird, ist eine Kombination von Radar- und Stationsmessungen. Zum einem wird damit am Beispiel der deutschen COSMO-Modelle gezeigt, dass die Modelle der neuesten Generation eine bessere Simulation des mittleren Tagesgangs aufweisen, wenn auch mit zu geringen Maximum und etwas zu spätem Auftreten. Im Gegensatz dazu liefern die Modelle der alten Generation ein zu starkes Maximum, welches erheblich zu früh auftritt. Zum anderen wird mit dem neuartigen Modell eine bessere Simulation der räumlichen Verteilung des Niederschlags, durch eine deutliche Minimierung der Luv-/Lee Proble-matik, erreicht. Um diese subjektiven Bewertungen zu quantifizieren, wurden tägliche QNVs von vier Modellen für Deutschland in einem Achtjahreszeitraum durch SAL sowie klassischen Maßen untersucht. Die höher aufgelösten Modelle simulieren realistischere Niederschlagsverteilungen(besser in S), aber bei den anderen Komponenten tritt kaum ein Unterschied auf. Ein weiterer Aspekt ist, dass das Modell mit der gröbsten Auf-lösung(ECMWF) durch den RMSE deutlich am besten bewertet wird. Darin zeigt sich das Problem des ‚Double Penalty’. Die Zusammenfassung der drei Komponenten von SAL liefert das Resultat, dass vor allem im Sommer das am feinsten aufgelöste Modell (COSMO-DE) am besten abschneidet. Hauptsächlich kommt das durch eine realistischere Struktur zustande, so dass SAL hilfreiche Informationen liefert und die subjektive Bewertung bestätigt. rnIm Jahr 2007 fanden die Projekte COPS und MAP D-PHASE statt und boten die Möglich-keit, 19 Modelle aus drei Modellkategorien hinsichtlich ihrer Vorhersageleistung in Südwestdeutschland für Akkumulationszeiträume von 6 und 12 Stunden miteinander zu vergleichen. Als Ergebnisse besonders hervorzuheben sind, dass (i) je kleiner der Gitter-punktabstand der Modelle ist, desto realistischer sind die simulierten Niederschlags-verteilungen; (ii) bei der Niederschlagsmenge wird in den hoch aufgelösten Modellen weniger Niederschlag, d.h. meist zu wenig, simuliert und (iii) die Ortskomponente wird von allen Modellen am schlechtesten simuliert. Die Analyse der Vorhersageleistung dieser Modelltypen für konvektive Situationen zeigt deutliche Unterschiede. Bei Hochdrucklagen sind die Modelle ohne Konvektionsparametrisierung nicht in der Lage diese zu simulieren, wohingegen die Modelle mit Konvektionsparametrisierung die richtige Menge, aber zu flächige Strukturen realisieren. Für konvektive Ereignisse im Zusammenhang mit Fronten sind beide Modelltypen in der Lage die Niederschlagsverteilung zu simulieren, wobei die hoch aufgelösten Modelle realistischere Felder liefern. Diese wetterlagenbezogene Unter-suchung wird noch systematischer unter Verwendung der konvektiven Zeitskala durchge-führt. Eine erstmalig für Deutschland erstellte Klimatologie zeigt einen einer Potenzfunktion folgenden Abfall der Häufigkeit dieser Zeitskala zu größeren Werten hin auf. Die SAL Ergebnisse sind für beide Bereiche dramatisch unterschiedlich. Für kleine Werte der konvektiven Zeitskala sind sie gut, dagegen werden bei großen Werten die Struktur sowie die Amplitude deutlich überschätzt. rnFür zeitlich sehr hoch aufgelöste Niederschlagsvorhersagen gewinnt der Einfluss der zeitlichen Fehler immer mehr an Bedeutung. Durch die Optimierung/Minimierung der L Komponente von SAL innerhalb eines Zeitfensters(+/-3h) mit dem Beobachtungszeit-punkt im Zentrum ist es möglich diese zu bestimmen. Es wird gezeigt, dass bei optimalem Zeitversatz die Struktur und Amplitude der QNVs für das COSMO-DE besser werden und damit die grundsätzliche Fähigkeit des Modells die Niederschlagsverteilung realistischer zu simulieren, besser gezeigt werden kann.

A STUDY OF PENALTY FORMULATIONS USED IN THE NUMERICAL APPROXIMATION OF A RADIALLY SYMMETRIC ELASTICITY PROBLEM

We consider a class of two-dimensional problems in classical linear elasticity for which material overlapping occurs in the absence of singularities. Of course, material overlapping is not physically realistic, and one possible way to prevent it uses a constrained minimization theory. In this theory, a minimization problem consists of minimizing the total potential energy of a linear elastic body subject to the constraint that the deformation field must be locally invertible. Here, we use an interior and an exterior penalty formulation of the minimization problem together with both a standard finite element method and classical nonlinear programming techniques to compute the minimizers. We compare both formulations by solving a plane problem numerically in the context of the constrained minimization theory. The problem has a closed-form solution, which is used to validate the numerical results. This solution is regular everywhere, including the boundary. In particular, we show numerical results which indicate that, for a fixed finite element mesh, the sequences of numerical solutions obtained with both the interior and the exterior penalty formulations converge to the same limit function as the penalization is enforced. This limit function yields an approximate deformation field to the plane problem that is locally invertible at all points in the domain. As the mesh is refined, this field converges to the exact solution of the plane problem.

Solving MPCC problem with the hyperbolic penalty function

The main goal of this work is to solve mathematical program with complementarity constraints (MPCC) using nonlinear programming techniques (NLP). An hyperbolic penalty function is used to solve MPCC problems by including the complementarity constraints in the penalty term. This penalty function [1] is twice continuously differentiable and combines features of both exterior and interior penalty methods. A set of AMPL problems from MacMPEC [2] are tested and a comparative study is performed.

Solving a signalized traffic intersection problem with an hyperbolic penalty function

Mathematical Program with Complementarity Constraints (MPCC) finds many applications in fields such as engineering design, economic equilibrium and mathematical programming theory itself. A queueing system model resulting from a single signalized intersection regulated by pre-timed control in traffic network is considered. The model is formulated as an MPCC problem. A MATLAB implementation based on an hyperbolic penalty function is used to solve this practical problem, computing the total average waiting time of the vehicles in all queues and the green split allocation. The problem was codified in AMPL.

Classification of some penalty methods

Optimization problems arise in science, engineering, economy, etc. and we need to find the best solutions for each reality. The methods used to solve these problems depend on several factors, including the amount and type of accessible information, the available algorithms for solving them, and, obviously, the intrinsic characteristics of the problem. There are many kinds of optimization problems and, consequently, many kinds of methods to solve them. When the involved functions are nonlinear and their derivatives are not known or are very difficult to calculate, these methods are more rare. These kinds of functions are frequently called black box functions. To solve such problems without constraints (unconstrained optimization), we can use direct search methods. These methods do not require any derivatives or approximations of them. But when the problem has constraints (nonlinear programming problems) and, additionally, the constraint functions are black box functions, it is much more difficult to find the most appropriate method. Penalty methods can then be used. They transform the original problem into a sequence of other problems, derived from the initial, all without constraints. Then this sequence of problems (without constraints) can be solved using the methods available for unconstrained optimization. In this chapter, we present a classification of some of the existing penalty methods and describe some of their assumptions and limitations. These methods allow the solving of optimization problems with continuous, discrete, and mixing constraints, without requiring continuity, differentiability, or convexity. Thus, penalty methods can be used as the first step in the resolution of constrained problems, by means of methods that typically are used by unconstrained problems. We also discuss a new class of penalty methods for nonlinear optimization, which adjust the penalty parameter dynamically.

Adaptive Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

The Part-time pay penalty in a segmented labor market

This paper is the first to examine the implications of switching to PT work for women's subsequent earnings trajectories, distinguishing by their type of contract: permanent or fixedterm. Using a rich longitudinal Spanish data set from Social Security records of over 76,000 prime-aged women strongly attached to the Spanish labor market, we find that PT work aggravates the segmentation of the labor market insofar there is a PT pay penalty and this penalty is larger and more persistent in the case of women with fixed-term contracts. The paper discusses problems arising in empirical estimation (including a problem not discussed in the literature up to now: the differential measurement error of the LHS variable by PT status), and how to address them. It concludes with policy implications relevant for Continental Europe and its dual structure of employment protection.

A modified Primal-Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Penalty-based nonlinear solver for optimal reactive power dispatch with discrete controls

The optimal reactive dispatch problem is a nonlinear programming problem containing continuous and discrete control variables. Owing to the difficulty caused by discrete variables, this problem is usually solved assuming all variables as continuous variables, therefore the original discrete variables are rounded off to the closest discrete value. This approach may provide solutions far from optimal or even unfeasible solutions. This paper presents an efficient handling of discrete variables by penalty function so that the problem becomes continuous and differentiable. Simulations with the IEEE test systems were performed showing the efficiency of the proposed approach. © 1969-2012 IEEE.

Augmented Lagrangian method with nonmonotone penalty parameters for constrained optimization

At each outer iteration of standard Augmented Lagrangian methods one tries to solve a box-constrained optimization problem with some prescribed tolerance. In the continuous world, using exact arithmetic, this subproblem is always solvable. Therefore, the possibility of finishing the subproblem resolution without satisfying the theoretical stopping conditions is not contemplated in usual convergence theories. However, in practice, one might not be able to solve the subproblem up to the required precision. This may be due to different reasons. One of them is that the presence of an excessively large penalty parameter could impair the performance of the box-constraint optimization solver. In this paper a practical strategy for decreasing the penalty parameter in situations like the one mentioned above is proposed. More generally, the different decisions that may be taken when, in practice, one is not able to solve the Augmented Lagrangian subproblem will be discussed. As a result, an improved Augmented Lagrangian method is presented, which takes into account numerical difficulties in a satisfactory way, preserving suitable convergence theory. Numerical experiments are presented involving all the CUTEr collection test problems.

A modified Primal-Dual Logarithmic-Barrier Method for solving the Optimal Power Flow problem with discrete and continuous control variables

The aim of solving the Optimal Power Flow problem is to determine the optimal state of an electric power transmission system, that is, the voltage magnitude and phase angles and the tap ratios of the transformers that optimize the performance of a given system, while satisfying its physical and operating constraints. The Optimal Power Flow problem is modeled as a large-scale mixed-discrete nonlinear programming problem. This paper proposes a method for handling the discrete variables of the Optimal Power Flow problem. A penalty function is presented. Due to the inclusion of the penalty function into the objective function, a sequence of nonlinear programming problems with only continuous variables is obtained and the solutions of these problems converge to a solution of the mixed problem. The obtained nonlinear programming problems are solved by a Primal-Dual Logarithmic-Barrier Method. Numerical tests using the IEEE 14, 30, 118 and 300-Bus test systems indicate that the method is efficient. (C) 2012 Elsevier B.V. All rights reserved.

Symmetric Interior Penalty DG Methods for the Compressible Navier-Stokes Equations II: Goal--Oriented A Posteriori Error Estimation

In this article we consider the application of the generalization of the symmetric version of the interior penalty discontinuous Galerkin finite element method to the numerical approximation of the compressible Navier--Stokes equations. In particular, we consider the a posteriori error analysis and adaptive mesh design for the underlying discretization method. Indeed, by employing a duality argument (weighted) Type I a posteriori bounds are derived for the estimation of the error measured in terms of general target functionals of the solution; these error estimates involve the product of the finite element residuals with local weighting terms involving the solution of a certain dual problem that must be numerically approximated. This general approach leads to the design of economical finite element meshes specifically tailored to the computation of the target functional of interest, as well as providing efficient error estimation. Numerical experiments demonstrating the performance of the proposed approach will be presented.