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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Convergence Analysis of Generalized Primal-Dual Interior-Point Algorithms for Linear Optimization

Wei, Hua January 2002 (has links)
We study the zeroth-, first-, and second-order algorithms proposed by Tuncel. The zeroth-order algorithms are the generalization of the classic primal-dual affine-scaling methods, and have a strong connection with the quasi-Newton method. Although the zeroth-order algorithms have the property of strict monotone decrease in both primal and dual objective values, they may not converge. We give an illustrative example as well as an algebraic proof to show that the zeroth-order algorithms do not converge to an optimal solution in some cases. The second-order algorithms use the gradients and Hessians of the barrier functions. Tuncel has shown that all second-order algorithms have a polynomial iteration bound. The second-order algorithms have a range of primal-dual scaling matrices to be chosen. We give a method to construct such a primal-dual scaling matrix. We then analyze a new centrality measure. This centrality measure appeared in both first- and second-order algorithms. We compare the neighbourhood defined by this centrality measure with other known neighbourhoods. We then analyze how this centrality measure changes in the next iteration in terms of the step length and some other information of the current iteration.
12

Convergence Analysis of Generalized Primal-Dual Interior-Point Algorithms for Linear Optimization

Wei, Hua January 2002 (has links)
We study the zeroth-, first-, and second-order algorithms proposed by Tuncel. The zeroth-order algorithms are the generalization of the classic primal-dual affine-scaling methods, and have a strong connection with the quasi-Newton method. Although the zeroth-order algorithms have the property of strict monotone decrease in both primal and dual objective values, they may not converge. We give an illustrative example as well as an algebraic proof to show that the zeroth-order algorithms do not converge to an optimal solution in some cases. The second-order algorithms use the gradients and Hessians of the barrier functions. Tuncel has shown that all second-order algorithms have a polynomial iteration bound. The second-order algorithms have a range of primal-dual scaling matrices to be chosen. We give a method to construct such a primal-dual scaling matrix. We then analyze a new centrality measure. This centrality measure appeared in both first- and second-order algorithms. We compare the neighbourhood defined by this centrality measure with other known neighbourhoods. We then analyze how this centrality measure changes in the next iteration in terms of the step length and some other information of the current iteration.
13

Constructing an Index Fund Using Interior Point Primal- Dual Method

Celestin, Kamta, Galabe, Sampid Marius January 2011 (has links)
Optimization methods nowadays play a very important role in financial decisions such as portfolio managements, construction of index funds and pension funds.  This Master Thesis is devoted to the problem of an index fund construction. The problem is represented as a linear optimization problem of re-balancing the portfolio at minimum cost and solved using the Primal-Dual interior point method. The index fund is constructed using ten companies from the Dow Jones Industrial Average Index (DJIA). The Primal-Dual interior point method was first implemented in Matlab and later on in Java.
14

Provably efficient algorithms for decentralized optimization

Liu, Changxin 31 August 2021 (has links)
Decentralized multi-agent optimization has emerged as a powerful paradigm that finds broad applications in engineering design including federated machine learning and control of networked systems. In these setups, a group of agents are connected via a network with general topology. Under the communication constraint, they aim to solving a global optimization problem that is characterized collectively by their individual interests. Of particular importance are the computation and communication efficiency of decentralized optimization algorithms. Due to the heterogeneity of local objective functions, fostering cooperation across the agents over a possibly time-varying network is challenging yet necessary to achieve fast convergence to the global optimum. Furthermore, real-world communication networks are subject to congestion and bandwidth limit. To relieve the difficulty, it is highly desirable to design communication-efficient algorithms that proactively reduce the utilization of network resources. This dissertation tackles four concrete settings in decentralized optimization, and develops four provably efficient algorithms for solving them, respectively. Chapter 1 presents an overview of decentralized optimization, where some preliminaries, problem settings, and the state-of-the-art algorithms are introduced. Chapter 2 introduces the notation and reviews some key concepts that are useful throughout this dissertation. In Chapter 3, we investigate the non-smooth cost-coupled decentralized optimization and a special instance, that is, the dual form of constraint-coupled decentralized optimization. We develop a decentralized subgradient method with double averaging that guarantees the last iterate convergence, which is crucial to solving decentralized dual Lagrangian problems with convergence rate guarantee. Chapter 4 studies the composite cost-coupled decentralized optimization in stochastic networks, for which existing algorithms do not guarantee linear convergence. We propose a new decentralized dual averaging (DDA) algorithm to solve this problem. Under a rather mild condition on stochastic networks, we show that the proposed DDA attains an $\mathcal{O}(1/t)$ rate of convergence in the general case and a global linear rate of convergence if each local objective function is strongly convex. Chapter 5 tackles the smooth cost-coupled decentralized constrained optimization problem. We leverage the extrapolation technique and the average consensus protocol to develop an accelerated DDA algorithm. The rate of convergence is proved to be $\mathcal{O}\left( \frac{1}{t^2}+ \frac{1}{t(1-\beta)^2} \right)$, where $\beta$ denotes the second largest singular value of the mixing matrix. To proactively reduce the utilization of network resources, a communication-efficient decentralized primal-dual algorithm is developed based on the event-triggered broadcasting strategy in Chapter 6. In this algorithm, each agent locally determines whether to generate network transmissions by comparing a pre-defined threshold with the deviation between the iterates at present and lastly broadcast. Provided that the threshold sequence is summable over time, we prove an $\mathcal{O}(1/t)$ rate of convergence for convex composite objectives. For strongly convex and smooth problems, linear convergence is guaranteed if the threshold sequence is diminishing geometrically. Finally, Chapter 7 provides some concluding remarks and research directions for future study. / Graduate
15

Une méthode de dualité pour des problèmes non convexes du Calcul des Variations / A duality method for non-convex problems in Calculus of Variations

Phan, Tran Duc Minh 28 June 2018 (has links)
Dans cette thèse, nous étudions un principe général de convexification permettant de traiter certainsproblèmes variationnels non convexes sur Rd. Grâce à ce principe nous pouvons mettre en oeuvre lespuissantes techniques de dualité et ramener de tels problèmes à des formulations de type primal–dualdans Rd+1, rendant ainsi efficace la recherche numérique de minima globaux. Une théorie de ladualité et des champs de calibration est reformulée dans le cas de fonctionnelles à croissance linéaire.Sous certaines hypothèses, cela nous permet de généraliser un principe d’exclusion découvert parVisintin dans les années 1990 et de réduire le problème initial à la minimisation d’une fonctionnelleconvexe sur Rd. Ce résultat s’applique notamment à une classe de problèmes à frontière libre oumulti-phasique donnant lieu à des tests numériques très convaincants au vu de la qualité des interfacesobtenues. Ensuite nous appliquons la théorie des calibrations à un problème classique de surfacesminimales avec frontière libre et établissons de nouveaux résultats de comparaison avec sa varianteoù la fonctionnelle des surfaces minimales est remplacée par la variation totale. Nous généralisonsla notion de calibrabilité introduite par Caselles-Chambolle et Al. et construisons explicitementune solution duale pour le problème associé à la seconde fonctionnelle en utilisant un potentiellocalement Lipschitzien lié à la distance au cut-locus. La dernière partie de la thèse est consacrée auxalgorithmes d’optimisation de type primal-dual pour la recherche de points selle, en introduisant denouvelles variantes plus efficaces en précision et temps calcul. Nous avons en particulier introduit unevariante semi-implicite de la méthode d’Arrow-Hurwicz qui permet de réduire le nombre d’itérationsnécessaires pour obtenir une qualité satisfaisante des interfaces. Enfin nous avons traité la nondifférentiabilité structurelle des Lagrangiens utilisés à l’aide d’une méthode géométrique de projectionsur l’épigraphe offrant ainsi une alternative aux méthodes classiques de régularisation. / In this thesis, we study a general principle of convexification to treat certain non convex variationalproblems in Rd. Thanks to this principle we are able to enforce the powerful duality techniques andbring back such problems to primal-dual formulations in Rd+1, thus making efficient the numericalsearch of a global minimizer. A theory of duality and calibration fields is reformulated in the caseof linear-growth functionals. Under suitable assumptions, this allows us to revisit and extend anexclusion principle discovered by Visintin in the 1990s and to reduce the original problem to theminimization of a convex functional in Rd. This result is then applied successfully to a class offree boundary or multiphase problems that we treat numerically obtaining very accurate interfaces.On the other hand we apply the theory of calibrations to a classical problem of minimal surfaceswith free boundary and establish new results related to the comparison with its variant where theminimal surfaces functional is replaced by the total variation. We generalize the notion of calibrabilityintroduced by Caselles-Chambolle and Al. and construct explicitly a dual solution for the problemassociated with the second functional by using a locally Lipschitzian potential related to the distanceto the cut-locus. The last part of the thesis is devoted to primal-dual optimization algorithms forthe search of saddle points, introducing new more efficient variants in precision and computationtime. In particular, we experiment a semi-implicit variant of the Arrow-Hurwicz method whichallows to reduce drastically the number of iterations necessary to obtain a sharp accuracy of theinterfaces. Eventually we tackle the structural non-differentiability of the Lagrangian arising fromour method by means of a geometric projection method on the epigraph, thus offering an alternativeto all classical regularization methods.
16

Estudo de técnicas eficientes para a resolução do problema de fluxo de potência para sistemas de distribuição radial / Study of efficient techniques for the resolution of power flow problem for distribution radial systems

Carvalho, Marcus Rodrigo 02 June 2006 (has links)
Este trabalho descreve uma abordagem do método primal-dual barreira logarítmica (MPDBL) associado ao método de Newton modificado para a resolução do problema de fluxo de potência para sistemas de distribuição radial. Também foi realizado um estudo comparativo com duas técnicas clássicas de solução do problema de fluxo potência para redes de distribuição radial. São os métodos: Backward/Forward Sweep e o método proposto por M. Baran e F. Wu, que é baseado na técnica de Newton-Raphson. Este método utiliza uma matriz Jacobiana modificada que atende a característica radial dos sistemas de distribuição. Nos testes comparativos serão considerados todos os parâmetros do sistema. Os algoritmos de solução serão analisados em suas propriedades de convergência e será realizado um teste de robustez. Os resultados dos testes realizados em 4 sistemas (4, 10, 34 e 70 barras) e o teste comparativo entre os métodos evidenciam a melhor metodologia na solução do problema de fluxo de potência para sistemas radiais / This work describes an approach on primal-dual logarithmic barrier method (PDLBM) associate to the method of Newton modified for the resolution of the problem of power flow for radial distribution systems. Also a comparative study with two classic techniques of solution of the flow problem was carried through power for nets of radial distribution. They are the methods: Backward/Forward Sweep and the method considered for M. Baran and F. Wu, that is based on the technique of Newton-Raphson. This method uses modified Jacobiana matrix that takes care of the radial characteristic of the distribution systems. In the comparative tests all will be considered the parameters of the system. The solution algorithms will be analyzed in its properties of convergence and will be carried through a robustness test. The results of the tests carried through in 4 systems (4, 10, 34 and 70 bus) and the comparative test between the methods evidence the best methodology in the solution of the problem of power flow for radial systems
17

Alocação de unidades de geração termoelétrica em sistemas elétricos de potência / Thermoelectrical generation allocation in electric power systems

Canola, Saulo Ricardo 16 January 2006 (has links)
Este trabalho tem como objetivo realizar um estudo de alocação de unidades termoelétricas em sistemas elétricos de potência (SEP). O fluxo de potencia ótimo (FPO) foi utilizado para se obter o ponto ótimo de operação para o sistema e os multiplicadores de Lagrange associados às restrições. Os multiplicadores de Lagrange indicam a sensibilidade entre a função objetivo e a restrição a ele associada. Esta sensibilidade indica, quais as barras do sistema, são candidatas à alocação de novas usinas termoelétricas. Testes nos sistemas de 5 barras, IEEE 14 barras, IEEE 30 barras, equivalente CESP 440 kV de 53 barras e IEEE 118 barras comprovam a eficiência da abordagem, a qual poderá ser utilizada em estudos de planejamento da expansão do sistema. / The aim of this paper is to present a study of thermoelectrical generation allocation in electric power systems. The optimal power flow (OPF) was used to evaluate the optimal operation point for the power system and also Lagrange multipliers associated with the constraints. The Lagrange multipliers are the sensitivity between the objective function and its constraints. This sensitivity is used to verify in a power system where is the best place to incentive the allocation of new thermoelectrical power plants. Tests on the systems: 5 buses, IEEE 14 buses, IEEE 30 buses, equivalent CESP 440kV 53 buses and IEEE 118 buses showed the efficiency of the presented approach. This method of analyzing the system can be used in study of expansion planning system.
18

Resolução do problema de fluxo de potência ótimo reativo via método da função lagrangiana barreira modificada / Resolution of reactive optimal power flow problem via method of Lagrangian modified barrier function

Sousa, Vanusa Alves de 08 June 2006 (has links)
Este trabalho propõe uma abordagem que utiliza uma associação dos métodos de barreira modificada e de pontos interiores primal-dual para a resolução do problema de fluxo de potência ótimo (FPO) reativo. Para isso, foi realizado um levantamento bibliográfico que explicitou os conceitos de otimização aplicados ao sistema estático de energia elétrica e os métodos dual-Lagrangiano, Newton-Lagrangiano, primal-dual barreira logarítmica e de barreira modificada. Na abordagem proposta, as restrições canalizadas são desmembradas em duas desigualdades. Estas são transformadas em igualdades a partir do acréscimo de variáveis de folga ou de excesso, as quais são relaxadas e tratadas pela função barreira modificada. Associa-se a esse problema uma função Lagrangiana. O sistema de equações resultantes das condições de estacionaridade da função Lagrangiana foi resolvido pelo método de Newton. Na implementação computacional foram usadas técnicas de esparsidade. Os sistemas elétricos de potência utilizados para verificar a eficiência da abordagem proposta na solução do problema de FPO reativo em três tipos de testes foram o de 3 barras, os do IEEE 14, 30, 118, 162 e 300 barras, o equivalente CESP 440 kV com 53 barras e o equivalente brasileiro sul-sudeste com 787 barras / This work proposes an approach that uses an association of the methods of modified barrier and primal-dual interior points for the resolution of the reactive optimal power flow (OPF) problem. On this purpose, a bibliographical review was accomplished, which enlightened the optimization concepts applied to the static system of electrical energy and the methods dual-Lagrangian, Newton-Lagrangian, primal-dual logarithmic barrier and modified barrier. In this approach, the bounded constraints are transformed in equalities by adding the non-negative slack variables. Those slack variables are relaxed and handled by the modified barrier function. A Lagrangian function is associated to this problem. The equation sets generated by the first-order necessary conditions of the Lagrangian function, were solved by Newton's method. In the computational implementation, sparsity techniques were used. The electric systems used to verify the efficiency of the approach proposed in the solution of the reative OPF problem in three types of tests were of the 3, IEEE 14, 30, 118, 162 and 300 buses, equivalent CESP 440 kV with 53 buses and the equivalent brazilian south-southeast with 787 buses
19

Estudos de casos em sistemas de energia elétrica por meio do fluxo de potência ótimo e da análise de sensibilidade / Studies of cases in power systems by optimal power flow and sensitivity analysis

Souza, Alessandra Macedo de 21 February 2005 (has links)
Este trabalho propõe estudos de casos em sistemas de energia elétrica por meio do Fluxo de Potência Ótimo (FPO) e da Análise de Sensibilidade em diferentes cenários de operação. Para isso, foram obtidos dados teóricos, a partir de levantamento bibliográfico, que explicitaram os conceitos de otimização aplicados ao sistema estático de energia elétrica. A pesquisa fundamentou-se metodologicamente no método primal-dual barreira logarítmica e nas condições necessárias de primeira-ordem de Karush-Kuhn-Tucker (KKT) para o problema de FPO, e no teorema proposto por Fiacco (1976) para a Análise de Sensibilidade. Os sistemas de equações resultantes das condições de estacionaridade, da função Lagrangiana, foram resolvidos pelo método de Newton. Na implementação computacional foram usadas técnicas de esparsidade. Estudos de casos foram realizados nos sistemas 3, IEEE 14, 30, 118, 300 barras e no equivalente CESP 440 kV com 53 barras, em que foi verificada a eficiência das técnicas apresentadas. / This work proposes a study of cases in power systems by Optimal Power Flow (OPF) and Sensitivity Analysis in different operation scenarios. For this purpose, theoretical data were obtained, starting from a bibliographical review, which enlightened the optimization concepts applied to the static system of electrical energy. The research was methodologically based on the primal-dual logarithmic barrier method and in the first-order necessary Karush-Kuhn-Tucker conditions to the OPF problem and in the theorem proposed by Fiacco (1976) to the Sensitivity Analysis. The equation sets generated by the first-order necessary conditions of the Lagrangian function, were solved by Newton\'s method. In the computational implementation, sparsity techniques were used. Studies of cases were carried out in the 3, IEEE 14, 30, 118, 300 buses and in the equivalent CESP 440 kV 53 bus, where the efficiency of the presented techniques was verified.
20

Uma abordagem primal-dual de reescalamento não-linear integrado para problemas de programação matemática discreta-mista com restrições de equilíbrio e suas aplicações ao problema de fluxo de potência ótimo reativo / A primal-dual integrated nonlinear rescaling approach for mixed-discrete mathematical problems with equilibrium constraints and its application to the reactive optimal power flow problems

Pinheiro, Ricardo Bento Nogueira Mori 03 May 2017 (has links)
Neste trabalho propomos uma abordagem computacional especificamente talhada para a solução de problemas de programação matemática discreta-mista com restrições de equilíbrio (MPEC). Para isso, inicialmente, transformamos o MPEC discreto-misto em uma sequência de MPECs contínuos. Na formulação dos MPECs contínuos, inserimos restrições de igualdade e de desigualdades artificias, as quais nos permitem considerar as variáveis discretas como contínuas. Cada MPEC contínuo é transformado em um problema de programação não-linear (PNL) padrão. Isso é feito por meio da reformulação das restrições de complementaridade originais do MPEC contínuo em um conjunto equivalente de restrições usuais de desigualdade. As restrições de igualdade originais do PNL são tratadas por meio da função lagrangiana clássica, as restrições de igualdade artificiais associada às variáveis discretas do PNL são tratadas por meio de uma técnica variante do método de penalidades clássico e as restrições de desigualdade artificias e originais do problema são tratadas por meio do método de reescalamento não-linear integrado proposto neste trabalho. Cada PNL é resolvido por meio de uma abordagem primal-dual do método de reescalamento não-linear integrado (PDRNLI) com atualização dinâmica dos parâmetros e com a estratégia de convergência global proposta. O método PDRNLI é aplicado ao problema de fluxo de potência ótimo reativo com restrições de atuação de dispositivo de controle de tensão associado aos sistemas elétricos IEEE-14, IEEE-30 e IEEE-118 barras. Os resultados numéricos comprovam a eficiência do método PDRNLI proposto para a solução do problema. / In this work we propose a computational approach specifically tailored for solving mixed-discrete mathematical problems with equilibrium constraints (MPEC). For such a purpose, we initially transform the mixed-discrete MPEC problem into a sequence of continuous MPEC problems. In the formulation of the continuous MPECs, we insert artificial equality and inequality constraints, which allow us handling discrete variables as continuous ones. Each continuous MPEC is transformed into a standard nonlinear programming problem (NLP). This is performed by reformulating the original complementarity constraints of the continuous MPEC problems into an equivalent system of standard inequality constraints. The original equality constraints of the NLP problem are handled by means of the classical lagrangian function, while the artificial equality constraints associated with the discrete variables are handled by means of a variant of the classic penalty method. The original and artificial inequality constraints are handled by means of the integrated nonlinear rescaling method proposed in this work. Each NLP is solved by means of a primal-dual version of the integrated nonlinear rescaling approach (PDINLR), with dynamic updating of parameters together with proposed a global convergence strategy. The PDINLR method is applied to the reactive optimal power flow problem with additional constraints associated with the actuation of voltage control devices for the associated with IEEE-14, 30 and 118 bus electrical systems. Numerical results assure the efficiency of the method PDINLR proposed for solving the problem.

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