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As software systems grow in complexity, the need for efficient automated techniques for design, testing and verification becomes more and more critical. Specification-based testing provides an effective approach for checking the correctness of software in general. Constraint-based analysis using specifications enables checking various rich properties by automating generation of test inputs. However, as specifications get more complex, existing analyses face a scalability problem due to state explosion. This dissertation introduces a novel approach to analyze declarative specifications incrementally; presents a constraint prioritization and partitioning methodology to enable efficient incremental analyses; defines a suite of optimizations to improve the analyses further; introduces a novel approach for testing software product lines; and provides an experimental evaluation that shows the feasibility and scalability of the approach. The key insight behind the incremental technique is declarative slicing, which is a new class of optimizations. The optimizations are inspired by traditional program slicing for imperative languages but are applicable to analyzable declarative languages, in general, and Alloy, in particular. We introduce a novel algorithm for slicing declarative models. Given an Alloy model, our fully automatic tool, Kato, partitions the model into a base slice and a derived slice using constraint prioritization. As opposed to the conventional use of the Alloy Analyzer, where models are analyzed as a whole, we perform analysis incrementally, i.e., using several steps. A satisfying solution to the base slice is systematically extended to generate a solution for the entire model, while unsatisfiability of the base implies unsatisfiability of the entire model. We show how our incremental technique enables different analysis tools and solvers to be used in synergy to further optimize our approach. Compared to the conventional use of the Alloy Analyzer, this means even more overall performance enhancements for solving declarative models. Incremental analyses have a natural application in the software product line domain. A product line is a family of programs built from features that are increments in program functionality. Given properties of features as firstorder logic formulas, we automatically generate test inputs for each product in a product line. We show how to map a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experiments using different data structure product lines show that our approach can provide an order of magnitude speed-up over conventional techniques. / text

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The aim of this thesis is to develop a methodology for obtaining an optimum configuration for the aeroballistic range models. In the design of aeroballistic range models, there are mainly three similarity requirements to be matched between the model and the actual munition: external geometry, location of the centre of gravity and the ratio of axial mass moment of inertia to the transverse mass moment of inertia. Furthermore, it is required to have a model with least possible weight, so that the required test velocities can be obtained with minimum chamber pressure and by use of minimum propellant while withstanding the enormous launch accelerations. This defines an optimization problem: to find the optimum model internal configuration and select materials to be used in the model such that the centre of gravity location and the inertia ratio are matched as closely as possible while the model withstands the launch forces and has minimum mass. To solve this problem a design methodology is devised and an optimization code is developed based on this methodology. Length, radius and end location of an optimum cylinder which has to be drilled out from the model are selected as the design variables for the optimization problem. Built&ndash / in functions from the Optimization Toolbox of Matlab&reg / are used in the optimization routine, and also a graphical user interface is designed for easy access to the design variables. The developed code is a very useful tool for the designer, although the results are not meant to be directly applied to the final product, they form the starting points for the detailed design.

Thesis (Ph. D.)--University of California, San Diego, 2007. / Title from first page of PDF file (viewed October 3, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 68-72).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Resumo: Este trabalho apresenta uma metodologia composta de duas fases para resolver problemas de otimização com restrições usando uma estratégia multiobjetivo. Na primeira fase, o esforço concentra-se em encontrar, pelo menos, uma solução factível, descartando completamente a função objetivo. Na segunda fase, aborda-se o problema como biobjetivo, onde se busca a otimização da função objetivo original e maximizar o cumprimento das restrições. Na fase um propõe-se uma estratégia baseada na diminuição progressiva da tolerância de aceitação das restrições complexas para encontrar soluções factíveis. O desempenho do algoritmo é validado através de 11 casos testes bastantes conhecidos na literatura especializada. / Abstract: This work presents a two-phase framework for solving constrained optimization problems using a multi-objective strategy. In the first phase, the objective function is completely disregarded and entire search effort is directed toward finding a single feasible solution. In the second phase, the problem is treated as a bi-objective optimization problem, where the technique converts constrained optimization to a two-objective optimization: one is the original objective function; the other is the degree function violating the constraints. In the first phase a methodology based on progressive decrease of the tolerance of acceptance of complex constrains is proposed in order to find feasible solutions. The approach is tested on 11 well-know benchmark functions. / Orientador: Rubén Augusto Romero Lázaro / Coorientador: José Roberto Sanches Mantovani / Banca: Antonio Padilha Feltrin / Banca: Marcos Julio Rider Flores / Mestre

Orientadores: Jose Mario Martinez, Maria Aparecida Diniz Ehrhardt / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-14T08:44:35Z (GMT). No. of bitstreams: 1 Pedroso_LucasGarcia_D.pdf: 1569234 bytes, checksum: 22491a86b6f7cc218acc26f3c2cb768a (MD5) Previous issue date: 2009 / Resumo: Neste trabalho propomos um algoritmo Lagrangiano Aumentado sem derivadas para o problema geral de otimização. Consideramos o método introduzido por Andreani, Birgin, Martínez e Schuverdt, eliminando os cálculos de derivadas inerentes ao algoritmo através de modificações adequadas no critério de parada. Foram mantidos os bons resultados teóricos do método, como convergência sob a condição de qualificação CPLD e a limitação do parâmetro de penalidade. Experimentos numéricos são apresentados, entre os quais destacamos um exemplo de problema sem derivadas baseado na simulação de áreas de figuras no plano. / Abstract: We propose in this work a derivative-free Augmented Lagrangian algorithm for the general problem of optimization. We consider the method due to Andreani, Birgin, Martínez and Schuverdt, eliminating the derivative computations in the algorithm by making suitable modifications on the stopping criterion. The good theoretical results of the method were mantained, as convergence under the CPLD constraint qualification and the limitation of the penalty parameter. Numerical experiments are presented, and the most relevant of them is an example of derivative-free problem based on the simulation of areas of figures on the plane. / Doutorado / Otimização Matematica / Doutor em Matemática Aplicada

Orientador: José Mario Martínez Pérez / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-20T09:19:13Z (GMT). No. of bitstreams: 1 Prudente_LeandrodaFonseca_D.pdf: 1307430 bytes, checksum: 6ac8a3a70af28dce0b2cd6d839b227ef (MD5) Previous issue date: 2012 / Resumo: Algoritmos de programação não-linear práticos podem convergir para pontos inviáveis mesmo quando o problema a ser resolvido é viável. Quando isso ocorre, é natural que o usuário mude o ponto inicial e/ou parâmetros algorítmicos e reaplique o método na tentativa de encontrar uma solução viável e ótima. Desta forma, o ideal é que um algoritmo não só seja eficiente em encontrar soluções viáveis, mas também que detecte rapidamente quando ele está fadado a convergir para um ponto inviável. Na tentativa de atingir esse objetivo, apresentamos modificações em um algoritmo baseado em Lagrangiano aumentado de modo que, no caso de convergência para um ponto inviável, os subproblemas são resolvidos com tolerâncias moderadas e, mesmo assim, as propriedades de convergência global são mantidas. Experimentos numéricos são apresentados / Abstract Practical Nonlinear Programming algorithms may converge to infeasible points even when the problem to be solved is feasible. When this occurs, it is natural for the user to change the starting point and/or algorithmic parameters and reapply the method in an attempt to find a feasible and optimal solution. Thus, the ideal is that an algorithm is eficient not only in finding feasible solutions, but also in quickly detecting when it is fated to converge to an infeasible point. In pursuit of this goal, we present modifications of an algorithm based on Augmented Lagrangians so that, in the case of convergence to an infeasible point, the subproblems are solved with moderate tolerances and, even then, the global convergence properties are maintained. Numerical experiments are presented / Doutorado / Matematica Aplicada / Doutor em Matemática Aplicada

In this dissertation we investigate methods of solving various optimization problems with PDE constraints, i.e. optimization problems that have a system of partial differential equations in the set of constraints, and develop frameworks for a number of practically inspired problems that were not considered in the literature before. Such problems arise in areas like fluid mechanics, chemical engineering, finance, and other areas where a physical system needs to be optimized. In most of the literature sources on PDE-constrained optimization only relatively simple systems of PDEs are considered, they are either linear, or the size of the system is small. On the contrary, in our case, we search for solution methods to problems constrained by large (8 to 10 equations) and non-linear systems of PDEs. More specifically, in the first part of the dissertation we consider a multiphysics phenomenon where electromagnetic and mechanical fields interact within an electrically conductive body, and develop the optimization framework to find an efficient way to control one field through another. We also apply the developed PDE-constrained optimization framework to a financial options portfolio optimization problem, and more specifically consider the case that to the best of our knowledge is not covered in the literature.