In this research effort, we study three exterior penalty function approaches for solving linear programming problems. These methods are an active set l2 penalty approach (ASL2), an inequality-equality based l2 penalty approach (IEL2), and an augmented Lagrangian approach (ALAG). Particular effective variants are presented for each method, along with comments and experience on alternative algorithmic strategies that were empirically investigated. Our motivation is to examine the relative performance of these different approaches based on the basic l2 penalty function in order to provide insights into the viability of these methods for solving linear programs. To test the performance of these algorithms, a set of randomly generated problems as well as a set of NETLIB test problems from the public domain are used. By way of providing a benchmark for comparisons, we also solve the test problems using CPLEX 6.0, an advanced simplex implementation. While a particular variant (ALAG2) of ALAG performed the best for randomly generated test problems, ASL2 performed the best for the NETLIB test problems. Moreover, for test problems having only equality constraints, IEL2, and ASL2 (which is a finer-tuned version of IEL2 in this case) were comparable and yielded a second-best performance in comparison with ALAG2. Furthermore, a set of problems with relatively higher density parameter values, as well as a set of low-density problems were used to determine the effect of density on the relative performances of these methods. This experiment revealed that for linear programs with a high density parameter, ASL2 is the best alternative among the tested algorithms; whereas, for low-density problems ALAG2 is the fastest method. Moreover, although our implementation was rudimentary in comparison with CPLEX, all of the tested methods attained a final solution faster than CPLEX for the set of large-scale low-density problems, sometimes as fast as requiring only 16-23% of the effort consumed by CPLEX. Average rank tests based on the computational results obtained are performed using two different statistics, that assess the speed of convergence and the quality or accuracy of the solution, in order to determine the relative effectiveness of the algorithms and to validate our conclusions. Overall, the results provide insights into selecting algorithmic strategies based on problem structure and indicate that while this class of methods is viable for computing near optimal solutions, more research is needed to design robust and competitive exterior point methods for solving linear programming problems. However, the use of the proposed variant of the augmented Lagrangian method to solve large-scale low-density linear programs is promising and should be explored more extensively. / Master of Science

In this thesis, we first develop a new method called penalty function with memory (PFM). PFM consists of a penalty parameter and a measure of constraint violation and it converts a discrete optimization via simulation (DOvS) problem with stochastic constraints into a series of DOvS problems without stochastic constraints. PFM determines a penalty of a visited solution based on past results of feasibility checks on the solution. Specifically, assuming a minimization problem, a penalty parameter of PFM, namely the penalty sequence, diverges to infinity for an infeasible solution but converges to zero almost surely for any strictly feasible solution under certain conditions. For a feasible solution located on the boundary of feasible and infeasible regions, the sequence converges to zero either with high probability or almost surely. As a result, a DOvS algorithm combined with PFM performs well even when optimal solutions are tight or nearly tight. Second, we design an optimal water quality monitoring network for river systems. The problem is to find the optimal location of a finite number of monitoring devices, minimizing the expected detection time of a contaminant spill event while guaranteeing good detection reliability. When uncertainties in spill and rain events are considered, both the expected detection time and detection reliability need to be estimated by stochastic simulation. This problem is formulated as a stochastic DOvS problem with the objective of minimizing expected detection time and with a stochastic constraint on the detection reliability; and it is solved by a DOvS algorithm combined with PFM. Finally, we improve PFM by combining it with an approximate budget allocation procedure. We revise an existing optimal budget allocation procedure so that it can handle active constraints and satisfy necessary conditions for the convergence of PFM.

The diploma thesis deals with penalty approach to stochastic optimization with chance constraints which are applied to structural mechanics. The problem of optimal design of beam dimensions is modeled and solved. The uncertainty is involved in the form of random load. The corresponding mathematical model contains a condition in the form of ordinary differencial equation that is solved by finite element method. The probability condition is approximated by several types of penalty functions. The results are obtained by computations in the MATLAB software.

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The submitted thesis studies penalty function methods for stochastic programming problems. The main objective of the paper is to examine penalty function methods for deterministic nonlinear programming, in particular exact penalty function methods, in order to enhance penalty function methods for stochastic programming. For this purpose, the equivalence of the original de- terministic nonlinear and the corresponding penalty function problem using arbi- trary vector norm as the penalty function is shown for convex and invex functions occurring in the problems, respectively. The obtained theorems are consequently applied to multiple chance constrained problems under finite discrete probability distribution to show the asymptotic equivalence of the probabilistic and the cor- responding penalty function problems. The practical use of the newly obtained methods is demonstrated on a numerical study, in which a comparison with other approaches is provided as well. 1

No contexto do mundo contemporâneo, os veículos automotores estão cada vez mais integrados ao cotidiano das pessoas, sendo mais de 1 bilhão deles circulando pelo mundo. Por serem controlados por motoristas, estão sujeitos a falhas decorrentes da inerente condição humana, ocasionando acidentes, mortes e outros prejuízos. O controle autônomo de veículos tem se apresentado como alternativa na busca de redução desses prejuízos, sendo utilizado nas mais diferentes abordagens, por distintas instituições ao redor do planeta. Deste modo, torna-se uma pauta fundamental para o estudo de sistemas de controle. Este trabalho, valendo-se da descrição matemática do comportamento veicular, busca o desenvolvimento e a implementação de um método eficiente de controle autônomo de veículos voltado, principalmente, para a modelagem em espaço de estados. Considerando que mudanças de marchas, principalmente em um cenário de dirigibilidade autônoma, são ações aleatórias, o objetivo desta dissertação é utilizar estratégias de controle baseadas em sistemas lineares sujeitos a saltos Markovianos. / In nowadays society, automobile vehicles are getting more and more integrated to people\'s daily activities, as there are more than 1 billion of them on the streets around the world. As they are controlled by drivers, vehicles are subjected to failures caused by human mistakes that lead to accidents, injuries and others. Autonomous vehicle control has shown itself to be an alternative in the pursuit of damage reduction, and it is applied by different institutions in many countries. Therefore, it is a main subject in the area of control systems. This paper, relying on mathematical descriptions of vehicle behavior, aims to develop and apply an efficient autonomous control method that takes into account state-space formulation. This goal will be achieved by the use of control strategies based on Markovian Jump Linear Systems that will describe the highly non-linear dynamics of the vehicle in different operation points.

Esta tese de doutorado aborda os projetos robustos de controle e estimativa de estados para Sistemas Lineares sujeitos a Saltos Markovianos (SLSM) de tempo discreto sob a influência de incertezas paramétricas. Esses projetos são desenvolvidos por meio de extensões dos critérios quadráticos clássicos para SLSM nominais. Os critérios de custo quadrático para os SLSM incertos são formulados na forma de problemas de otimização min-max que permitem encontrar a melhor solução para o pior caso de incerteza (máxima influência de incerteza). Os projetos robustos correspondem às soluções ótimas obtidas por meio da combinação dos métodos de funções penalidade e mínimos quadrados regularizados robustos. Duas situações são investigadas: regular e estimar os estados quando os modos de operações são observados; e estimar os estados sob a hipótese de desconhecimento da cadeia de Markov. Estruturalmente, o regulador e as estimativas de estados assemelham-se às respectivas versões nominais. A recursividade é estabelecida em termos de equações de Riccati sem a necessidade de ajuste de parâmetros auxiliares e dependente apenas das matrizes de parâmetros e ponderações conhecidas. / This thesis deals with recursive robust designs of control and state estimates for discretetime Markovian Jump Linear Systems (MJLS) subject to parametric uncertainties. The designs are developed considering extensions of the standard quadratic cost criteria for MJLS without uncertainties. The quadratic cost criteria for uncertain MJLS are formulated in the form of min-max optimization problems to get the best solution for the worst uncertainty case. The optimal robust schemes correspond to the optimal solution obtained by the combination of penalty function and robust regularized least-squares methods. Two cases are investigated: to control and estimate the states when the operation modes are observed; and, to estimate the states when the Markov chain is unobserved. The optimal robust LQR and Kalman-type state estimates resemble the respective nominal versions. The recursiveness is established by Riccati equations in terms of parameter and weighting matrices previously known and without extra offline computations.

Neste trabalho são estudadas três metodologias de otimização não-linear: o Método da Função Lagrangeana, o Método da Função Penalidade e o Método da Função Lagrangeana Aumentada. Com o estudo da Função Lagrangeana e do Método da Função Penalidade, foi possível alcançar a formulação da Função Lagrangeana Aumentada com o objetivo de resolver problemas de programação não-linear não-convexos. Testes numéricos são apresentados para o problema não-convexo de programação não-linear conhecido como Fluxo de Potência Ótimo. / In this dissertation, three nonlinear optimization methodologies are studied: the Lagrangian Function Method, the Penalty Function Method and Augmented Lagrangian Function Method. Through the studies ofthe Lagrangian Function and the Penalty function Method, it was possible to reach the formulation of the Augmented Lagrangian Function aiming to solve nonlinear nonconvex programming problems. Numerical tests are presented for the nonconvex nonlinear programming problem known as optimal power flow.

Esta tese de doutorado aborda os projetos robustos de controle e estimativa de estados para Sistemas Lineares sujeitos a Saltos Markovianos (SLSM) de tempo discreto sob a influência de incertezas paramétricas. Esses projetos são desenvolvidos por meio de extensões dos critérios quadráticos clássicos para SLSM nominais. Os critérios de custo quadrático para os SLSM incertos são formulados na forma de problemas de otimização min-max que permitem encontrar a melhor solução para o pior caso de incerteza (máxima influência de incerteza). Os projetos robustos correspondem às soluções ótimas obtidas por meio da combinação dos métodos de funções penalidade e mínimos quadrados regularizados robustos. Duas situações são investigadas: regular e estimar os estados quando os modos de operações são observados; e estimar os estados sob a hipótese de desconhecimento da cadeia de Markov. Estruturalmente, o regulador e as estimativas de estados assemelham-se às respectivas versões nominais. A recursividade é estabelecida em termos de equações de Riccati sem a necessidade de ajuste de parâmetros auxiliares e dependente apenas das matrizes de parâmetros e ponderações conhecidas. / This thesis deals with recursive robust designs of control and state estimates for discretetime Markovian Jump Linear Systems (MJLS) subject to parametric uncertainties. The designs are developed considering extensions of the standard quadratic cost criteria for MJLS without uncertainties. The quadratic cost criteria for uncertain MJLS are formulated in the form of min-max optimization problems to get the best solution for the worst uncertainty case. The optimal robust schemes correspond to the optimal solution obtained by the combination of penalty function and robust regularized least-squares methods. Two cases are investigated: to control and estimate the states when the operation modes are observed; and, to estimate the states when the Markov chain is unobserved. The optimal robust LQR and Kalman-type state estimates resemble the respective nominal versions. The recursiveness is established by Riccati equations in terms of parameter and weighting matrices previously known and without extra offline computations.