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1
Trujillo-Cortez, Refugio. "Stable convex parametric programming and applications." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=37856.
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This thesis is a study of stable perturbations in convex programming models. Stability of a general model is introduced as lower semicontinuity of the feasible set mapping. This stability is shown to be equivalent to the Robinson notion of stability and regularity. In the convex case, it is also equivalent to the full-rank Slater condition. Then, the relationships between various point-to-set mappings are studied for convex models and new implications between these mappings are established. Also, local and global optimality of parameters is studied. A new result here is a characterization of locally optimal parameters that does not require stable perturbations. This result is valid, in particular, for convex models with LFS constraints. The value of the model can be improved by one of several new formulations of the marginal value formula.The results on stability are applied for bilevel convex models and an algorithm for solving these models, based on a marginal value formula, is suggested and then applied to a real-life problem in the petroleum industry.
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Yue, Hongwei. "First-order affine scaling continuous method for convex quadratic programming." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/39.
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We develop several continuous method models for convex quadratic programming (CQP) problems with di.erent types of constraints. The essence of the continuous method is to construct one ordinary di.erential equation (ODE) system such that its limiting equilibrium point corresponds to an optimal solution of the underlying optimization problem. All our continuous method models share the main feature of the interior point methods, i.e., starting from any interior point, all the solution trajectories remain in the interior of the feasible regions. First, we present an a.ne scaling continuous method model for nonnegativity constrained CQP. Under the boundedness assumption of the optimal set, a thorough study on the properties of the ordinary di.erential equation is provided, strong convergence of the continuous trajectory of the ODE system is proved. Following the features of this ODE system, a new ODE system for solving box constrained CQP is also presented. Without projection, the whole trajectory will stay inside the box region, and it will converge to an optimal solution. Preliminary simulation results illustrate that our continuous method models are very encouraging in obtaining the optimal solutions of the underlying optimization problems. For CQP in the standard form, the convergence of the iterative .rst-order a.ne scaling algorithm is still open. Under boundedness assumption of the optimal set and nondegeneracy assumption of the constrained region, we discuss the properties of the ODE system induced by the .rst-order a.ne scaling direction. The strong convergence of the continuous trajectory of the ODE system is also proved. Finally, a simple iterative scheme induced from our ODE is presented for finding an optimal solution of nonnegativity constrained CQP. The numerical results illustrate the good performance of our continuous method model with this iterative scheme. Keywords: ODE; Continuous method; Quadratic programming; Interior point method; A.ne scaling.
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Yang, Yi. "Sequential convex approximations of chance constrained programming /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?IELM%202008%20YANG.
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Dong, Hongbo. "Copositive programming: separation and relaxations." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2692.
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A large portion of research in science and engineering, as well as in business, concerns one similar problem: how to make things "better”? Once properly modeled (although usually a highly nontrivial task), this kind of questions can be approached via a mathematical optimization problem. Optimal solution to a mathematical optimization problem, when interpreted properly, might corresponds to new knowledge, effective methodology or good decisions in corresponding application area. As already proved in many success stories, research in mathematical optimization has a significant impact on numerous aspects of human life. Recently, it was discovered that a large amount of difficult optimization problems can be formulated as copositive programming problems. Famous examples include a large class of quadratic optimization problems as well as many classical combinatorial optimization problems. For some more general optimization problems, copositive programming provides a way to construct tight convex relaxations. Because of this generality, new knowledge of copositive programs has the potential of being uniformly applied to these cases. While it is provably difficult to design efficient algorithms for general copositive programs, we study copositive programming from two standard aspects, its relaxations and its separation problem.
With regard to constructing computational tractable convex relaxations for copositive programs, we develop direct constructions of two tensor relaxation hierarchies for the completely positive cone, which is a fundamental geometric object in copositive programming. We show connection of our relaxation hierarchies with known hierarchies. Then we consider the application of these tensor relaxations to the maximum stable set problem. With regard to the separation problem for copositive programming. We first prove some new results in low dimension of 5 x 5 matrices. Then we show how a separation procedure for this low dimensional case can be extended to any symmetric matrices with a certain block structure. Last but not least, we provide another approach to the separation and relaxations for the (generalized) completely positive cone. We prove some generic results, and discuss applications to the completely positive case and another case related to box-constrained quadratic programming. Finally, we conclude the thesis with remarks on some interesting open questions in the field of copositive programming.
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Dadush, Daniel Nicolas. "Integer programming, lattice algorithms, and deterministic volume estimation." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44807.
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The main subject of this thesis is the development of new geometric tools and techniques for solving classic problems within the geometry of
numbers and convex geometry. At a high level, the problems considered in this thesis concern the varied interplay between the continuous and
the discrete, an important theme within computer science and operations research. The first subject we consider is the study of cutting planes for non-linear integer programs. Cutting planes have been implemented to great
effect for linear integer programs, and so understanding their properties in more general settings is an important subject of study. As our contribution to this area, we show that Chvatal-Gomory closure of any compact convex set is a rational polytope. As a consequence, we
resolve an open problem of Schrijver (Ann. Disc. Math. `80) regarding the same question for irrational polytopes. The second subject of study is that of ellipsoidal approximation of convex bodies. Different such notions have been important to the
development of fundamental geometric algorithms: e.g. the ellipsoid method for convex optimization (enclosing ellipsoids), or random walk
methods for volume estimation (inertial ellipsoids). Here we consider the construction of an ellipsoid with good "covering" properties with respect to a convex body, known in convex geometry as the M-ellipsoid. As our contribution, we give two algorithms for constructing
M-ellipsoids, and provide an application to near-optimal deterministic volume estimation in the oracle model. Equipped with this new geometric tool, we move to the study of classic lattice problems in the geometry of numbers, namely the Shortest
(SVP) and Closest Vector Problems (CVP). Here we use M-ellipsoid coverings, combined with an algorithm of Micciancio and Voulgaris for CVP in the ℓ₂ norm (STOC `10), to obtain the first deterministic 2^O(ⁿ) time algorithm for the SVP in general norms. Combining this
algorithm with a novel lattice sparsification technique, we derive the first deterministic 2^O(ⁿ)(1+1/ϵ)ⁿ time algorithm for
(1+ϵ)-approximate CVP in general norms. For the next subject of study, we analyze the geometry of general integer programs. A central structural result in this area is Kinchine's flatness theorem, which states that every lattice free convex body has integer width bounded by a function of dimension. As our contribution, we build on the work Banaszczyk, using tools from lattice based cryptography, to give a new and tighter proof of the flatness theorem. Lastly, combining all the above techniques, we consider the study of algorithms for the Integer Programming Problem (IP). As our main
contribution, we give a new 2^O(ⁿ)nⁿ time algorithm for IP, which yields the fastest currently known algorithm for IP and improves on the classic works of Lenstra (MOR `83) and Kannan (MOR `87).
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Potaptchik, Marina. "Portfolio Selection Under Nonsmooth Convex Transaction Costs." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2940.
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We consider a portfolio selection problem in the presence of transaction costs. Transaction costs on each asset are assumed to be a convex function of the amount sold or bought. This function can be nondifferentiable in a finite number of points. The objective function of this problem is a sum of a convex twice differentiable function and a separable convex nondifferentiable function. We first consider the problem in the presence of linear constraints and later generalize the results to the case when the constraints are given by the convex piece-wise linear functions. Due to the special structure, this problem can be replaced by an equivalent differentiable problem in a higher dimension. It's main drawback is efficiency since the higher dimensional problem is computationally expensive to solve.
We propose several alternative ways to solve this problem which do not require introducing new variables or constraints. We derive the optimality conditions for this problem using subdifferentials. First, we generalize an active set method to this class of problems. We solve the problem by considering a sequence of equality constrained subproblems, each subproblem having a twice differentiable objective function. Information gathered at each step is used to construct the subproblem for the next step. We also show how the nonsmoothness can be handled efficiently by using spline approximations. The problem is then solved using a primal-dual interior-point method.
If a higher accuracy is needed, we do a crossover to an active set method. Our numerical tests show that we can solve large scale problems efficiently and accurately.
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Lehmann, Sonja [Verfasser], and Klaus [Akademischer Betreuer] Schittkowski. "A strictly feasible sequential convex programming method / Sonja Lehmann. Betreuer: Klaus Schittkowski." Bayreuth : Universitätsbibliothek Bayreuth, 2011. http://d-nb.info/1018017712/34.
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Li, Xinxin. "Some operator splitting methods for convex optimization." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/43.
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Many applications arising in various areas can be well modeled as convex optimization models with separable objective functions and linear coupling constraints. Such areas include signal processing, image processing, statistical learning, wireless networks, etc. If these well-structured convex models are treated as generic models and their separable structures are ignored in algorithmic design, then it is hard to effectively exploit the favorable properties that the objective functions possibly have. Therefore, some operator splitting methods have regained much attention from different areas for solving convex optimization models with separable structures in different contexts. In this thesis, some new operator splitting methods are proposed for convex optimiza- tion models with separable structures. We first propose combining the alternating direction method of multiplier with the logarithmic-quadratic proximal regulariza- tion for a separable monotone variational inequality with positive orthant constraints and propose a new operator splitting method. Then, we propose a proximal version of the strictly contractive Peaceman-Rachford splitting method, which was recently proposed for the convex minimization model with linear constraints and an objective function in form of the sum of two functions without coupled variables. After that, an operator splitting method suitable for parallel computation is proposed for a convex model whose objective function is the sum of three functions. For the new algorithms, we establish their convergence and estimate their convergence rates measured by the iteration complexity. We also apply the new algorithms to solve some applications arising in the image processing area; and report some preliminary numerical results. Last, we will discuss a particular video processing application and propose a series of new models for background extraction in different scenarios; to which some of the new methods are applicable. Keywords: Convex optimization, Operator splitting method, Alternating direction method of multipliers, Peaceman-Rachford splitting method, Image processing
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Theußl, Stefan, Florian Schwendinger, and Kurt Hornik. "ROI: An extensible R Optimization Infrastructure." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/5858/1/ROI_StatReport.pdf.
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Optimization plays an important role in many methods routinely used in statistics, machine learning and data science. Often, implementations of these methods rely on highly specialized optimization algorithms, designed to be only applicable within a specific application. However, in many instances recent advances, in particular in the field of convex optimization, make it possible to conveniently and straightforwardly use modern solvers instead with the advantage of enabling broader usage scenarios and thus promoting reusability.
This paper introduces the R Optimization Infrastructure which provides an extensible infrastructure to model linear, quadratic, conic and general nonlinear optimization problems in a consistent way.
Furthermore, the infrastructure administers many different solvers, reformulations, problem collections and functions to read and write optimization problems in various formats.Series: Research Report Series / Department of Statistics and Mathematics
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Wright, Stephen E. "Convergence and approximation for primal-dual methods in large-scale optimization /." Thesis, Connect to this title online; UW restricted, 1990. http://hdl.handle.net/1773/5751.
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Zeng, Shangzhi. "Algorithm-tailored error bound conditions and the linear convergence rae of ADMM." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/474.
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In the literature, error bound conditions have been widely used for studying the linear convergence rates of various first-order algorithms and the majority of literature focuses on how to sufficiently ensure these error bound conditions, usually posing more assumptions on the model under discussion. In this thesis, we focus on the alternating direction method of multipliers (ADMM), and show that the known error bound conditions for studying ADMM's linear convergence, can indeed be further weakened if the error bound is studied over the specific iterative sequence generated by ADMM. A so-called partial error bound condition, which is tailored for the specific ADMM's iterative scheme and weaker than known error bound conditions in the literature, is thus proposed to derive the linear convergence of ADMM. We further show that this partial error bound condition theoretically justifies the difference if the two primal variables are updated in different orders in implementing ADMM, which had been empirically observed in the literature yet no theory is known so far.
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Luedtke, James. "Integer Programming Approaches for Some Non-convex and Stochastic Optimization Problems." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19711.
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In this dissertation we study several non-convex and stochastic optimization problems. The common theme is the use of mixed-integer programming (MIP) techniques including valid inequalities and reformulation to solve these problems.
We first study a strategic capacity planning model which captures the trade-off between the incentive to delay capacity installation to wait for improved technology and the need for some capacity to be installed to meet current demands. This problem is naturally formulated as a MIP with a bilinear objective. We develop several linear MIP formulations, along with classes of strong valid inequalities. We also present a specialized branch-and-cut algorithm to solve a compact concave formulation. Computational results indicate that these formulations can be used to solve large-scale instances.
We next study methods for optimization with joint probabilistic constraints. These problems are challenging because evaluating solution feasibility requires multidimensional integration and the feasible region is not convex. We propose and analyze a Monte Carlo sampling scheme to simplify the probabilistic structure of such problems. Computational tests of the approach indicate that it can yield good feasible solutions and reasonable bounds on their quality. Next, we study a MIP formulation of the non-convex sample approximation problem. We obtain two strengthened formulations. As a byproduct of this analysis, we obtain new results for the previously studied mixing set, subject to an additional knapsack inequality. Computational results indicate that large-scale instances can be solved using the strengthened formulations.
Finally, we study optimization problems with stochastic dominance constraints. A stochastic dominance constraint states that a random outcome which depends on the decision variables should stochastically dominate a given random variable. We present new formulations for both first and second order stochastic dominance which are significantly more compact than existing formulations. Computational tests illustrate the benefits of the new formulations.
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Visagie, S. E. "Algoritmes vir die maksimering van konvekse en verwante knapsakprobleme /." Link to the online version, 2007. http://hdl.handle.net/10019.1/1082.
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Ferreira, Fialho dos Anjos Miguel Nuno. "New convex relaxations for the maximum cut and VLSI layout problems." Thesis, Waterloo, Ont. : University of Waterloo, 2001. http://etd.uwaterloo.ca/etd/manjos2001.pdf.
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Thesis (Ph.D.) - University of Waterloo, 2001."A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Doctor of Philosophy in Combinatorics and Optimization". Includes bibliographical references. Also available in microfiche format.
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Oliveira, Rubia Mara de. "Algoritmos de busca global para problemas de otimização geometricos e multiplicativos." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260215.
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Orientador: Paulo Augusto Valente FerreiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação
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Resumo: Nesta tese são propostos novos algoritmos de otimização baseados na busca global para duas importantes classes de problemas de programação não-linear: problemas geométricos, nos quais as funções envolvidas são descritas por somas de polinômios generalizados, e problemas de programação multiplicativa convexa, os quais, por sua vez, apresentam funções objetivos e/ou restrições expressas como produtos de funções convexas. Uma abordagem multiobjetivo para problemas geométricos posinomiais, que admitem reformulações convexas, é apresentada. Para problemas geométricos signomiais, que não possuem reformulações convexas conhecidas, propõe-se incorporar um procedimento de busca local a um algoritmo branch-and-bound, visando acelerar a convergência deste tipo de algoritmo. Elementos de análise convexa e programação multiobjetivo são usados para abordar problemas de programação multiplicativa quando estes apresentam produtos e somas de produtos de funções convexas positivas nas suas funções objetivos. Um mínimo global para o primeiro caso é obtido como o limite das soluções de uma seqüência de minimizações quase-côncavas sobre politopos, resolvidas eficientemente por meio de enumeração de vértices. Um mínimo global para o segundo caso é obtido como o limite das soluções de uma seqüência de problemas quadráticos indefinidos com características especiais, resolvidos por enumeração de restrições. O desempenho computacional dos algoritmos propostos nesta tese é avaliado por meio de problemas-testes e comparado com algoritmos alternativos existentes na literatura
Abstract: In this thesis new optimization algorithms based on global search are proposed for two important classes of nonlinear programming problems: geometric problems, in which the functions involved are described by a sum of generalized polynomials, and convex multiplicative problems, in which, in turn, objective functions and/or constraints are expressed as a product of convex functions. A multiobjective approach for posinomial geometric problems, which admit convex reformulations, is presented. As convex reformulations for signomial geometric problems are unknown, a local search procedure with the purpose of speeding up the convergence of branchand-bound algorithms is proposed. Elements of convex analysis and multiobjective programming are used for dealing with multiplicative programming problems presenting products and sums of products of positive convex functions in their objective functions. A global minimum in the first case is obtained as the limit of a sequence of quasi-concave minimizations on polytopes, efficiently solved by vertex enumeration. A global minimum for the second case is obtained as the limit of a sequence of special indefinite quadratic problems, solved by constraint enumeration. The computational performance of the algorithms proposed in this thesis has been evaluated by means of test problems and compared with alternate algorithms from the literature
Doutorado
Automação
Doutor em Engenharia Elétrica
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Xiao, Zhifu. "A Comparative Analysis of an Interior-point Method and a Sequential Quadratic Programming Method for the Markowitz Portfolio Management Problem." Oberlin College Honors Theses / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1463008420.
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Pinheiro, Ricardo Bento Nogueira [UNESP]. "Um método previsor-corretor primal-dual de pontos interiores barreira logarítmica modificada, com estratégias de convergência global e de ajuste cúbico, para problemas de programação não-linear e não-convexa." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/87189.
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Neste trabalho apresentamos o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada e estratégia de ajuste cúbico (MPIBLM-EX) e o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada, com estratégias de ajuste cúbico e de convergência global (MPIBLMCG-EX). Na definição do algoritmo proposto, a função barreira logarítmica modificada auxilia o método em sua inicialização com pontos inviáveis. Porém, a inviabilidade pode ocorrer em pontos tais que o logaritmo não está definido, consequentemente, isso implica na não existência de função barreira logarítmica modificada. Para suprir essa dificuldade um polinômio cúbico ajustado ao logaritmo, que preserva as derivadas de primeira e segunda do mestre definido a partir de um ponto da região ampliada ao método previsor-corretor primal-dual de pontos interiores com barreira logarítmica modificada (MPIBML); no processo previsor são realizadas atualizações do parâmetro de barreira nos resíduos das restrições de complementaridade, considerando aproximações de primeira ordem do sistema de direções de busca, enquanto que no procedimento corretor, incluímos os termos quadráticos não-lineares dos resíduos citados, que foram desprezados no procedimento previsor. Considerando também a estratégia de convergência global para o MPIBLM-EX, a qual utiliza uma variante do método de Levenberg-Marquardt para ajustar a matriz dual normal da função lagrangiana, caso esta não seja definida positiva. A matriz dual normal é redefinida para as restrições primais de igualdade, de desigualdade e para as variáveis canalizadas, incorporando variáveis duais e matrizes diagonais relativas às restrições de complementariade. Desse estudo, o MPIBLM-EX é transformado no MPIBLMCG-EX e mostramos...
This work presents a predictor primal-dual interior point method with modified log-barrier and third order extrapolation strategy (IPMLBM-EX) and also and extension of this method with the inclusion of the global convergence strategy (IPMLBGCM-EX). In the definition of the proposed algorithm, the modified log-barrier function helps the method initialize with infeasible points. However, infeasibility may occur for some point where the logarithm is not defined. The implicates in non-existence of the modified log-barrier function. To cope with such as problem, a cubic polynomial function is adjusted to the logarithmic function. Sucha polynomial function preserves first and second order derivatives in certain point defined in the extended region. This function is applied to the predictor-corretor primal-dual interior point method with modified log-barrier function. In the predictor procedure, the barrier parameter is updated in the complementarity conditions considering first-order approximations of the search direction, while the corrector procedure includes the nonlinear quadratic terms of the mentioned residuals, which were neglected in the predictor procedure. We also consider the global convergence strategy for the method, which uses a variant of the Levenberg-Marquardt method to update the normal dual matrix of the Langrangian function, should it fail to be positively defined. In this case, this matrix is redefined for equality primal constraints, bounded inequality primal constraints and bounded variables, incorporating dual variables and diagonal matrices of the complementarity constraints. From such studies, the IPMLBM-EX method is extended to include the global convergence strategy (IPMLBGCM-EX). We have show that both methods are projected gradient methods. An implementation performed with Matlab 6.1 has shown the... (Complete abstract click electronic access below)
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Pinheiro, Ricardo Bento Nogueira. "Um método previsor-corretor primal-dual de pontos interiores barreira logarítmica modificada, com estratégias de convergência global e de ajuste cúbico, para problemas de programação não-linear e não-convexa /." Bauru : [s.n.], 2012. http://hdl.handle.net/11449/87189.
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Orientador: Antonio Roberto BalboBanca: Edilaine Martins Soler
Banca: Leonardo Nepomuceno
Resumo: Neste trabalho apresentamos o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada e estratégia de ajuste cúbico (MPIBLM-EX) e o método previsor-corretor primal-dual de pontos interiores, com barreira logarítmica modificada, com estratégias de ajuste cúbico e de convergência global (MPIBLMCG-EX). Na definição do algoritmo proposto, a função barreira logarítmica modificada auxilia o método em sua inicialização com pontos inviáveis. Porém, a inviabilidade pode ocorrer em pontos tais que o logaritmo não está definido, consequentemente, isso implica na não existência de função barreira logarítmica modificada. Para suprir essa dificuldade um polinômio cúbico ajustado ao logaritmo, que preserva as derivadas de primeira e segunda do mestre definido a partir de um ponto da região ampliada ao método previsor-corretor primal-dual de pontos interiores com barreira logarítmica modificada (MPIBML); no processo previsor são realizadas atualizações do parâmetro de barreira nos resíduos das restrições de complementaridade, considerando aproximações de primeira ordem do sistema de direções de busca, enquanto que no procedimento corretor, incluímos os termos quadráticos não-lineares dos resíduos citados, que foram desprezados no procedimento previsor. Considerando também a estratégia de convergência global para o MPIBLM-EX, a qual utiliza uma variante do método de Levenberg-Marquardt para ajustar a matriz dual normal da função lagrangiana, caso esta não seja definida positiva. A matriz dual normal é redefinida para as restrições primais de igualdade, de desigualdade e para as variáveis canalizadas, incorporando variáveis duais e matrizes diagonais relativas às restrições de complementariade. Desse estudo, o MPIBLM-EX é transformado no MPIBLMCG-EX e mostramos... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: This work presents a predictor primal-dual interior point method with modified log-barrier and third order extrapolation strategy (IPMLBM-EX) and also and extension of this method with the inclusion of the global convergence strategy (IPMLBGCM-EX). In the definition of the proposed algorithm, the modified log-barrier function helps the method initialize with infeasible points. However, infeasibility may occur for some point where the logarithm is not defined. The implicates in non-existence of the modified log-barrier function. To cope with such as problem, a cubic polynomial function is adjusted to the logarithmic function. Sucha polynomial function preserves first and second order derivatives in certain point defined in the extended region. This function is applied to the predictor-corretor primal-dual interior point method with modified log-barrier function. In the predictor procedure, the barrier parameter is updated in the complementarity conditions considering first-order approximations of the search direction, while the corrector procedure includes the nonlinear quadratic terms of the mentioned residuals, which were neglected in the predictor procedure. We also consider the global convergence strategy for the method, which uses a variant of the Levenberg-Marquardt method to update the normal dual matrix of the Langrangian function, should it fail to be positively defined. In this case, this matrix is redefined for equality primal constraints, bounded inequality primal constraints and bounded variables, incorporating dual variables and diagonal matrices of the complementarity constraints. From such studies, the IPMLBM-EX method is extended to include the global convergence strategy (IPMLBGCM-EX). We have show that both methods are projected gradient methods. An implementation performed with Matlab 6.1 has shown the... (Complete abstract click electronic access below)
Mestre
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Visagie, Stephan E. "Algoritmes vir die maksimering van konvekse en verwante knapsakprobleme." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/1082.
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Thesis (PhD (Logistics))--University of Stellenbosch, 2007.In this dissertation original algorithms are introduced to solve separable resource allocation problems (RAPs) with increasing nonlinear functions in the objective function, and lower and upper bounds on each variable. Algorithms are introduced in three special cases. The first case arises when the objective function of the RAP consists of the sum of convex functions and all the variables for these functions range over the same interval. In the second case RAPs with the sum of convex functions in the objective function are considered, but the variables of these functions can range over different intervals. In the last special case RAPs with an objective function comprising the sum of convex and concave functions are considered. In this case the intervals of the variables can range over different values. In the first case two new algorithms, namely the fraction and the slope algorithm are presented to solve the RAPs adhering to the conditions of the case. Both these algorithms yield far better solution times than the existing branch and bound algorithm. A new heuristic and three new algorithms are presented to solve RAPs falling into the second case. The iso-bound heuristic yields, on average, good solutions relative to the optimal objective function value in faster times than exact algorithms. The three algorithms, namely the iso-bound algorithm, the branch and cut algorithm and the iso-bound branch and cut algorithm also yield considerably beter solution times than the existing branch and bound algorithm. It is shown that, on average, the iso-bound branch and cut algorithm yields the fastest solution times, followed by the iso-bound algorithm and then by die branch and cut algorithm. In the third case the necessary and sufficient conditions for optimality are considered. From this, the conclusion is drawn that search techniques for points complying with the necessary conditions will take too long relative to branch and bound techniques. Thus three new algorithms, namely the KL, SKL and IKL algorithms are introduced to solve RAPs falling into this case. These algorithms are generalisations of the branch and bound, branch and cut, and iso-bound algorithms respectively. The KL algorithm was then used as a benchmark. Only the IKL algorithm yields a considerable improvement on the KL algorithm.
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Lan, Guanghui. "Convex optimization under inexact first-order information." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29732.
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Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009.Committee Chair: Arkadi Nemirovski; Committee Co-Chair: Alexander Shapiro; Committee Co-Chair: Renato D. C. Monteiro; Committee Member: Anatoli Jouditski; Committee Member: Shabbir Ahmed. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Martins, Rafael de Castro Duarte. "Filtragem robusta via combinação convexa de filtros de kalman." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260191.
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Orientador: Jose C. GeromelDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
Made available in DSpace on 2018-08-09T14:56:40Z (GMT). No. of bitstreams: 1 Martins_RafaeldeCastroDuarte_M.pdf: 331846 bytes, checksum: 23104cfddf85c27b47361e2f3ba52327 (MD5) Previous issue date: 2007
Resumo: Neste trabalho, é proposto um novo método para o projeto de filtros robustos em norma H2, que consiste na utilização de uma combinação linear dos filtros de Kalman obtidos para os vértices do politopo de incertezas. Para esta classe de filtros, são obtidos problemas, expressos na forma de LMIs, para a determinação dos coeficientes que produzem o melhor filtro robusto. Inicialmente, uma sub-classe de sistemas politópicos é considerada e, em seguida, os resultados são generalizados para sistemas a tempo contínuo e discreto com incertezas paramétricas politópicas. São definidos limitantes inferior e superior para a norma do erro de estimação que permitem avaliar a qualidade do filtro proposto. Sua ordem é geralmente maior que a do sistema em estudo, o que contribui para melhorar o seu desempenho
Abstract: In this work, a new method to H2robust filtrer design is proposed. A convex combination of Kalman filters, calculated in each vertex of the uncertainty polytope, is used to synthesize the robust filter. For this model, the best one is calculated through a convex programming problem, expressed in terms of LMIs. Inicially a sub-class of polytopic systems is considerated and later it is widened to cope with both continuous and discrete time systems subject to polytopic parameter uncertainty. Lower and upper bounds of the estimation error norm are defined in order to evaluate the quality of the proposed filter. Its order generally is greater than the order of the plant, which contributes to reduce conservatism
Mestrado
Automação
Mestre em Engenharia Elétrica
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Qian, Xun. "Continuous methods for convex programming and convex semidefinite programming." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/422.
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In this thesis, we study several interior point continuous trajectories for linearly constrained convex programming (CP) and convex semidefinite programming (SDP). The continuous trajectories are characterized as the solution trajectories of corresponding ordinary differential equation (ODE) systems. All our ODE systems are closely related to interior point methods.. First, we propose and analyze three continuous trajectories, which are the solutions of three ODE systems for linearly constrained convex programming. The three ODE systems are formulated based on an variant of the affine scaling direction, the central path, and the affine scaling direction in interior point methods. The resulting solutions of the first two ODE systems are called generalized affine scaling trajectory and generalized central path, respectively. Under some mild conditions, the properties of the continuous trajectories, the optimality and convergence of the continuous trajectories are all obtained. Furthermore, we show that for the example of Gilbert et al. [Math. Program., { 103}, 63-94 (2005)], where the central path does not converge, our generalized central path converges to an optimal solution of the same example in the limit.. Then we analyze two primal dual continuous trajectories for convex programming. The two continuous trajectories are derived from the primal-dual path-following method and the primal-dual affine scaling method, respectively. Theoretical properties of the two interior point continuous trajectories are fully studied. The optimality and convergence of both interior point continuous trajectories are obtained for any interior feasible point under some mild conditions. In particular, with proper choice of some parameters, the convergence for both continuous trajectories does not require the strict complementarity or the analyticity of the objective function.. For convex semidefinite programming, four interior continuous trajectories defined by matrix differential equations are proposed and analyzed. Optimality and convergence of the continuous trajectories are also obtained under some mild conditions. We also propose a strategy to guarantee the optimality of the affine scaling algorithm for convex SDP.
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Huang, Xin. "Some Topics in Roc Curves Analysis." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/3.
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The receiver operating characteristic (ROC) curves is a popular tool for evaluating continuous diagnostic tests. The traditional definition of ROC curves incorporates implicitly the idea of "hard" thresholding, which also results in the empirical curves being step functions. The first topic is to introduce a novel definition of soft ROC curves, which incorporates the idea of "soft" thresholding. The softness of a soft ROC curve is controlled by a regularization parameter that can be selected suitably by a cross-validation procedure. A byproduct of the soft ROC curves is that the corresponding empirical curves are smooth.
The second topic is on combination of several diagnostic tests to achieve better diagnostic accuracy. We consider the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a non-parametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests.
However, the above best-subset variable selection method is not practical when the number of diagnostic tests is large. The third topic is to further develop a LASSO-type procedure for variable selection. To solve the non-convex maximization problem in the proposed procedure, an efficient algorithm is developed based on soft ROC curves, difference convex programming, and coordinate descent algorithm.
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Kilinc-Karzan, Fatma. "Tractable relaxations and efficient algorithmic techniques for large-scale optimization." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41141.
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In this thesis, we develop tractable relaxations and efficient algorithms for large-scale optimization. Our developments are motivated by a recent paradigm, Compressed Sensing (CS), which consists of acquiring directly low-dimensional linear projections of signals, possibly corrupted with noise, and then using sophisticated recovery procedures for signal reconstruction. We start by analyzing how to utilize a priori information given in the form of sign restrictions on part of the entries. We propose necessary and sufficient on the sensing matrix for exact recovery of sparse signals, utilize them in deriving error bounds under imperfect conditions, suggest verifiable sufficient conditions and establish their limits of performance. In the second part of this thesis, we study the CS synthesis problem -selecting the minimum number of rows from a given matrix, so that the resulting submatrix possesses certifiably good recovery properties. We express the synthesis problem as the problem of approximating a given matrix by a matrix of specified low rank in the uniform norm and develop a randomized algorithm for this problem. The third part is dedicated to efficient First-Order Methods (FOMs) for large-scale, well-structured convex optimization problems. We propose FOMs with stochastic oracles that come with exact guarantees on solution quality, achieve sublinear time behavior, and through extensive simulations, show considerable improvement over the state-of-the-art deterministic FOMs. In the last part, we examine a general sparse estimation problem -estimating a block sparse linear transform of a signal from the undersampled observations of the signal corrupted with nuisance and stochastic noise. We show that an extension of the earlier results to this more general framework is possible. In particular, we suggest estimators that have efficiently verifiable guaranties of performance and provide connections to well-known results in CS theory.
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Benacer, Rachid. "Contribution à l'étude des algorithmes de l'optimisation non convexe et non différentiable." Phd thesis, Grenoble 1, 1986. http://tel.archives-ouvertes.fr/tel-00320986.
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Etude théorique et algorithmique des problèmes d'optimisation non convexes et non différentiables des types suivants: maximiser f(x) sur C, minimiser f(x)-g(x) sur C, minimiser f(x) lorsque x appartient à C et g(x) positive, où f, g sont convexes définies sur rn et C est une partie compacte convexe non vide de rn. Un étudie les conditions nécessaires d'optimalité du premier ordre la dualité, les méthodes de sous-gradients qui convergent vers des solutions optimales locales et les algorithmes qui permettent d'obtenir les solutions globales. On donne, quelques résultats numériques et applications des algorithmes présentés
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Hou, Liangshao. "Solving convex programming with simple convex constraints." HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/739.
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The problems we studied in this thesis are linearly constrained convex programming (LCCP) and nonnegative matrix factorization (NMF). The resolutions of these two problems are all closely related to convex programming with simple convex constraints. The work can mainly be described in the following three parts. Firstly, an interior point algorithm following a parameterized central path for linearly constrained convex programming is proposed. The convergence and polynomial-time complexity are proved under the assumption that the Hessian of the objective function is locally Lipschitz continuous. Also, an initialization strategy is proposed, and some numerical results are provided to show the efficiency of the proposed algorithm. Secondly, the path following algorithm is promoted for general barrier functions. A class of barrier functions is proposed, and their corresponding paths are proved to be continuous and converge to optimal solutions. Applying the path following algorithm to these paths provide more flexibility to interior point methods. With some adjustments, the initialization method is equipped to validate implementation and convergence. Thirdly, we study the convergence of hierarchical alternating least squares algorithm (HALS) and its fast form (Fast HALS) for nonnegative matrix factorization. The coordinate descend idea for these algorithms is restated. With a precise estimation of objective reduction, some limiting properties are illustrated. The accumulation points are proved to be stationary points, and some adjustments are proposed to improve the implementation and efficiency
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Ilyes, Amy Louise. "Using linear programming to solve convex quadratic programming problems." Case Western Reserve University School of Graduate Studies / OhioLINK, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=case1056644216.
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Edwards, Teresa Dawn. "The box method for minimizing strictly convex functions over convex sets." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/30690.
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Vargyas, Emese Tünde. "Duality for convex composed programming problems." Doctoral thesis, Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401793.
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The goal of this work is to present a conjugate duality treatment of composed programming as well as to give an overview of some recent developments in both scalar and multiobjective optimization. In order to do this, first we study a single-objective optimization problem, in which the objective function as well as the constraints are given by composed functions. By means of the conjugacy approach based on the perturbation theory, we provide different kinds of dual problems to it and examine the relations between the optimal objective values of the duals. Given some additional assumptions, we verify the equality between the optimal objective values of the duals and strong duality between the primal and the dual problems, respectively. Having proved the strong duality, we derive the optimality conditions for each of these duals. As special cases of the original problem, we study the duality for the classical optimization problem with inequality constraints and the optimization problem without constraints. The second part of this work is devoted to location analysis. Considering first the location model with monotonic gauges, it turns out that the same conjugate duality principle can be used also for solving this kind of problems. Taking in the objective function instead of the monotonic gauges several norms, investigations concerning duality for different location problems are made. We finish our investigations with the study of composed multiobjective optimization problems. In doing like this, first we scalarize this problem and study the scalarized one by using the conjugacy approach developed before. The optimality conditions which we obtain in this case allow us to construct a multiobjective dual problem to the primal one. Additionally the weak and strong duality are proved. In conclusion, some special cases of the composed multiobjective optimization problem are considered. Once the general problem has been treated, particularizing the results, we construct a multiobjective dual for each of them and verify the weak and strong dualitiesIn dieser Arbeit wird, anhand der sogenannten konjugierten Dualitätstheorie, ein allgemeines Dualitätsverfahren für die Untersuchung verschiedener Optimierungsaufgaben dargestellt. Um dieses Ziel zu erreichen wird zuerst eine allgemeine Optimierungsaufgabe betrachtet, wobei sowohl die Zielfunktion als auch die Nebenbedingungen zusammengesetzte Funktionen sind. Mit Hilfe der konjugierten Dualitätstheorie, die auf der sogenannten Störungstheorie basiert, werden für die primale Aufgabe drei verschiedene duale Aufgaben konstruiert und weiterhin die Beziehungen zwischen deren optimalen Zielfunktionswerten untersucht. Unter geeigneten Konvexitäts- und Monotonievoraussetzungen wird die Gleichheit dieser optimalen Zielfunktionswerte und zusätzlich die Existenz der starken Dualität zwischen der primalen und den entsprechenden dualen Aufgaben bewiesen. In Zusammenhang mit der starken Dualität werden Optimalitätsbedingungen hergeleitet. Die Ergebnisse werden abgerundet durch die Betrachtung zweier Spezialfälle, nämlich die klassische restringierte bzw. unrestringierte Optimierungsaufgabe, für welche sich die aus der Literatur bekannten Dualitätsergebnisse ergeben. Der zweite Teil der Arbeit ist der Dualität bei Standortproblemen gewidmet. Dazu wird ein sehr allgemeines Standortproblem mit konvexer zusammengesetzter Zielfunktion in Form eines Gauges formuliert, für das die entsprechenden Dualitätsaussagen abgeleitet werden. Als Spezialfälle werden Optimierungsaufgaben mit monotonen Normen betrachtet. Insbesondere lassen sich Dualitätsaussagen und Optimalitätsbedingungen für das klassische Weber und Minmax Standortproblem mit Gauges als Zielfunktion herleiten. Das letzte Kapitel verallgemeinert die Dualitätsaussagen, die im zweiten Kapitel erhalten wurden, auf multikriterielle Optimierungsprobleme. Mit Hilfe geeigneter Skalarisierungen betrachten wir zuerst ein zu der multikriteriellen Optimierungsaufgabe zugeordnetes skalares Problem. Anhand der in diesem Fall erhaltenen Optimalitätsbedingungen formulieren wir das multikriterielle Dualproblem. Weiterhin beweisen wir die schwache und, unter bestimmten Annahmen, die starke Dualität. Durch Spezialisierung der Zielfunktionen bzw. Nebenbedingungen resultieren die klassischen konvexen Mehrzielprobleme mit Ungleichungs- und Mengenrestriktionen. Als weitere Anwendungen werden vektorielle Standortprobleme betrachtet, zu denen wir entsprechende duale Aufgaben formulieren
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Tünde, Vargyas Emese. "Duality for convex composed programming problems." [S.l. : s.n.], 2004. http://archiv.tu-chemnitz.de/pub/2004/0179.
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Jakee, Khan Md Kamall. "Computational convex analysis using parametric quadratic programming." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45182.
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The class of piecewise linear-quadratic (PLQ) functions is a very important
class of functions in convex analysis since the result of most convex
operators applied to a PLQ function is a PLQ function. Although there exists
a wide range of algorithms for univariate PLQ functions, recent work has
focused on extending these algorithms to PLQ functions with more than one
variable. First, we recall a proof in [Convexity, Convergence and Feedback
in Optimal Control, Phd thesis, R. Goebel, 2000] that PLQ functions are
closed under partial conjugate computation. Then we use recent results on
parametric quadratic programming (pQP) to compute the inf-projection of
any multivariate convex PLQ function. We implemented the algorithm for
bivariate PLQ functions, and modi ed it to compute conjugates. We provide
a complete space and time worst-case complexity analysis and show that for
bivariate functions, the algorithm matches the performance of [Computing
the Conjugate of Convex Piecewise Linear-Quadratic Bivariate Functions,
Bryan Gardiner and Yves Lucet, Mathematical Programming Series B, 2011]
while being easier to extend to higher dimensions.
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Ben, Daya Mohamed. "Barrier function algorithms for linear and convex quadratic programming." Diss., Georgia Institute of Technology, 1988. http://hdl.handle.net/1853/25502.
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Yung, Simon Yun Pui. "Definitive programming : a paradigm for exploratory programming." Thesis, University of Warwick, 1992. http://wrap.warwick.ac.uk/78859/.
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Exploratory software development is a method that applies to the development of programs whose requirement is initially unclear. In such a context, it is only through prototyping and experimenting on the prototypes that the requirement can be fully developed. A good exploratory software development method must have a short development cycle. This thesis describes our attempt to fulfil this demand. We address this issue in the programming language level. A novel programming paradigm - definitive (definition-based) programming - is developed. In definitive programming, a state is represented by a set of definitions (a definitive script) and a state transition is represented by a redefinition. By means of a definition, a variable is defined either by an explicit value or by a formula in terms of other variables. Unless this variable is redefined, the relationship between the variables within the definition persists. To apply this state representation principle, we have developed some definitive notations in which the underlying algebras used in formulating definitions are domain specific. We have also developed an agent-oriented specification language by which we can model state transitions over definitive scripts. The modelling principles of definitive programming rest on a solid foundation in observation and experiment that is essential for exploratory software development. This thesis describes how we may combine definitive notations and the agent oriented programming concept to produce software tools that are useful in exploratory software development. In this way, definitive programming can be considered as a paradigm for exploratory programming.
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Wang, Guanglei. "Relaxations in mixed-integer quadratically constrained programming and robust programming." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0026/document.
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De nombreux problèmes de la vie réelle sont exprimés sous la forme de décisions à prendre à l’aide de l’information accessible dans le but d’atteindre certains objectifs. La programmation numérique a prouvé être un outil efficace pour modéliser et résoudre une grande variété de problèmes de ce type. Cependant, de nombreux problèmes en apparence faciles sont encore durs à résoudre. Et même des problèmes faciles de programmation linéaire deviennent durs avec l’incertitude de l’information disponible. Motivés par un problème de télécommunication où l’on doit associer des machines virtuelles à des serveurs tout en minimisant les coûts, nous avons employé plusieurs outils de programmation mathématique dans le but de résoudre efficacement le problème, et développé de nouveaux outils pour des problèmes plus généraux. Dans l’ensemble, résumons les principaux résultats de cette thèse comme suit. Une formulation exacte et plusieurs reformulations pour le problème d’affectation de machines virtuelles dans le cloud sont données. Nous utilisons plusieurs inégalités valides pour renforcer la formulation exacte, accélérant ainsi l’algorithme de résolution de manière significative. Nous donnons en outre un résultat géométrique sur la qualité de la borne lagrangienne montrant qu’elle est généralement beaucoup plus forte que la borne de la relaxation continue. Une hiérarchie de relaxation est également proposée en considérant une séquence de couverture de l’ensemble de la demande. Ensuite, nous introduisons une nouvelle formulation induite par les symétries du problème. Cette formulation permet de réduire considérablement le nombre de termes bilinéaires dans le modèle, et comme prévu, semble plus efficace que les modèles précédents. Deux approches sont développées pour la construction d’enveloppes convexes et concaves pour l’optimisation bilinéaire sur un hypercube. Nous établissons plusieurs connexions théoriques entre différentes techniques et nous discutons d’autres extensions possibles. Nous montrons que deux variantes de formulations pour approcher l’enveloppe convexe des fonctions bilinéaires sont équivalentes. Nous introduisons un nouveau paradigme sur les problèmes linéaires généraux avec des paramètres incertains. Nous proposons une hiérarchie convergente de problèmes d’optimisation robuste – approche robuste multipolaire, qui généralise les notions de robustesse statique, de robustesse d’affinement ajustable, et de robustesse entièrement ajustable. En outre, nous montrons que l’approche multipolaire peut générer une séquence de bornes supérieures et une séquence de bornes inférieures en même temps et les deux séquences convergent vers la valeur robuste des FARC sous certaines hypothèses modéréesMany real life problems are characterized by making decisions with current information to achieve certain objectives. Mathematical programming has been developed as a successful tool to model and solve a wide range of such problems. However, many seemingly easy problems remain challenging. And some easy problems such as linear programs can be difficult in the face of uncertainty. Motivated by a telecommunication problem where assignment decisions have to be made such that the cloud virtual machines are assigned to servers in a minimum-cost way, we employ several mathematical programming tools to solve the problem efficiently and develop new tools for general theoretical problems. In brief, our work can be summarized as follows. We provide an exact formulation and several reformulations on the cloud virtual machine assignment problem. Then several valid inequalities are used to strengthen the exact formulation, thereby accelerating the solution procedure significantly. In addition, an effective Lagrangian decomposition is proposed. We show that, the bounds providedby the proposed Lagrangian decomposition is strong, both theoretically and numerically. Finally, a symmetry-induced model is proposed which may reduce a large number of bilinear terms in some special cases. Motivated by the virtual machine assignment problem, we also investigate a couple of general methods on the approximation of convex and concave envelopes for bilinear optimization over a hypercube. We establish several theoretical connections between different techniques and prove the equivalence of two seeming different relaxed formulations. An interesting research direction is also discussed. To address issues of uncertainty, a novel paradigm on general linear problems with uncertain parameters are proposed. This paradigm, termed as multipolar robust optimization, generalizes notions of static robustness, affinely adjustable robustness, fully adjustable robustness and fills the gaps in-between. As consequences of this new paradigms, several known results are implied. Further, we prove that the multipolar approach can generate a sequence of upper bounds and a sequence of lower bounds at the same time and both sequences converge to the robust value of fully adjustable robust counterpart under some mild assumptions
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Zetina, Villamor Carlos Armando. "Gamma Active Constraints in Convex Semi-Infinite Programming." Thesis, Universidad de las Am��ricas Puebla, 2011. http://catarina.udlap.mx/u_dl_a/tales/documentos/mosl/zetina_v_ca/.
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This thesis studies the problem of extending the concept of γ-active constraints to Convex Semi-Inαnite Programming. To achieve this goal, extensive knowledge of topology, convex analysis, real analysis and optimization is needed. We base ourselves on the deαnition and results shown in previous publications and present two approaches to extend this deαnition to the case of Convex Semi-Inαnite Programming. We also provide a comparison of the two approaches, where we state their limitations and advantages.
Key Words: Semi-Inαnite Programming, Extended Active Constraints, Convex Program-ming, Optimality Conditions.
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Ha, Hoang Kha Electrical Engineering & Telecommunications Faculty of Engineering UNSW. "Linear phase filter bank design by convex programming." Publisher:University of New South Wales. Electrical Engineering & Telecommunications, 2008. http://handle.unsw.edu.au/1959.4/43268.
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Digital filter banks have found in a wide variety of applications in data compression, digital communications, and adaptive signal processing. The common objectives of the filter bank design consist of frequency selectivity of the individual filters and perfect reconstruction of the filter banks. The design problems of filter banks are intrinsically challenging because their natural formulations are nonconvex constrained optimization problems. Therefore, there is a strong motivation to cast the design problems into convex optimization problems whose globally optimal solutions can be efficiently obtained. The main contributions of this dissertation are to exploit the convex optimization algorithms to design several classes of the filter banks. First, the two-channel orthogonal symmetric complex-valued filter banks are investigated. A key contribution is to derive the necessary and sufficient condition for the existence of complex-valued symmetric spectral factors. Moreover, this condition can be expressed as linear matrix inequalities (LMIs), and hence semi-definite programming (SDP) is applicable. Secondly, for two-channel symmetric real-valued filter banks, a more general and efficient method for designing the optimal triplet halfband filter banks with regularity is developed. By exploiting the LMI characterization of nonnegative cosine polynomials, the semi-infinite constraints can be efficiently handled. Consequently, the filter bank design is cast as an SDP problem. Furthermore, it is demonstrated that the resulting filter banks are applied to image coding with improved performance. It is not straightforward to extend the proposed design methods for two-channel filter banks to M-channel filter banks. However, it is investigated that the design problem of M-channel cosine-modulated filter banks is a nonconvex optimization problem with the low degree of nonconvexity. Therefore, the efficient semidefinite relaxation technique is proposed to design optimal prototype filters. Additionally, a cheap iterative algorithm is developed to further improve the performance of the filter banks. Finally, the application of filter banks to multicarrier systems is considered. The condition on the transmit filter bank and channel for the existence of zero-forcing filter bank equalizers is obtained. A closed-form expression of the optimal equalizer is then derived. The proposed filter bank transceivers are shown to outperform the orthogonal frequency-division multiplexing (OFDM) systems.
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Wytock, Matt. "Optimizing Optimization: Scalable Convex Programming with Proximal Operators." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/785.
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Convex optimization has developed a wide variety of useful tools critical to many applications in machine learning. However, unlike linear and quadratic programming, general convex solvers have not yet reached sufficient maturity to fully decouple the convex programming model from the numerical algorithms required for implementation. Especially as datasets grow in size, there is a significant gap in speed and scalability between general solvers and specialized algorithms. This thesis addresses this gap with a new model for convex programming based on an intermediate representation of convex problems as a sum of functions with efficient proximal operators. This representation serves two purposes: 1) many problems can be expressed in terms of functions with simple proximal operators, and 2) the proximal operator form serves as a general interface to any specialized algorithm that can incorporate additional `2-regularization. On a single CPU core, numerical results demonstrate that the prox-affine form results in significantly faster algorithms than existing general solvers based on conic forms. In addition, splitting problems into separable sums is attractive from the perspective of distributing solver work amongst multiple cores and machines. We apply large-scale convex programming to several problems arising from building the next-generation, information-enabled electrical grid. In these problems (as is common in many domains) large, high-dimensional datasets present opportunities for novel data-driven solutions. We present approaches based on convex models for several problems: probabilistic forecasting of electricity generation and demand, preventing failures in microgrids and source separation for whole-home energy disaggregation.
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Sentieiro, Joao Jose Dos Santos. "Convex programming for optimal control : algorithms and convergence rates." Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38156.
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Lin, Chin-Yee. "Interior point methods for convex optimization." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/15044.
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Chen, Jieqiu. "Convex relaxations in nonconvex and applied optimization." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/654.
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Traditionally, linear programming (LP) has been used to construct convex relaxations in the context of branch and bound for determining global optimal solutions to nonconvex optimization problems. As second-order cone programming (SOCP) and semidefinite programming (SDP) become better understood by optimization researchers, they become alternative choices for obtaining convex relaxations and producing bounds on the optimal values. In this thesis, we study the use of these convex optimization tools in constructing strong relaxations for several nonconvex problems, including 0-1 integer programming, nonconvex box-constrained quadratic programming (BoxQP), and general quadratic programming (QP).
We first study a SOCP relaxation for 0-1 integer programs and a sequential relaxation technique based on this SOCP relaxation. We present desirable properties of this SOCP relaxation, for example, this relaxation cuts off all fractional extreme points of the regular LP relaxation. We further prove that the sequential relaxation technique generates the convex hull of 0-1 solutions asymptotically.
We next explore nonconvex quadratic programming. We propose a SDP relaxation for BoxQP based on relaxing the first- and second-order KKT conditions, where the difficulty and contribution lie in relaxing the second-order KKT condition. We show that, although the relaxation we obtain this way is equivalent to an existing SDP relaxation at the root node, it is significantly stronger on the children nodes in a branch-and-bound setting.
New advance in optimization theory allows one to express QP as optimizing a linear function over the convex cone of completely positive matrices subject to linear constraints, referred to as completely positive programming (CPP). CPP naturally admits strong semidefinite relaxations. We incorporate the first-order KKT conditions of QP into the constraints of QP, and then pose it in the form of CPP to obtain a strong relaxation. We employ the resulting SDP relaxation inside a finite branch-and-bound algorithm to solve the QP. Comparison of our algorithm with commercial global solvers shows potential as well as room for improvement.
The remainder is devoted to new techniques for solving a class of large-scale linear programming problems. First order methods, although not as fast as second-order methods, are extremely memory efficient. We develop a first-order method based on Nesterov's smoothing technique and demonstrate the effectiveness of our method on two machine learning problems.
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REY, PABLO ANDRES. "CONVEX ANALYSIS AND LIFT-AND-PROJECT METHODS FOR INTEGER PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2001. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=1794@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOAlgoritmos para a resolução de problemas de programação mista 0-1 gerais baseados em cortes derivados dos métodos lift-and-project, tem se mostrado bastante eficientes na prática. Estes cortes são gerados resolvendo um problema que depende de uma certa normalização. Desde um ponto de vista teórico, o bom comportamento destes algoritmos não foi completamente compreendido, especialmente no que diz respeito à normalização. Neste trabalho consideramos normalizações gerais definidas por um conjunto convexo fechado arbitrário, estendendo assim a análise teórica desenvolvida nos anos noventa. Apresentamos um marco teórico que abarca todas as normalizações previamente estudadas e introduzimos novas normalizações, analisando as propriedades dos cortes associados.Introduzimos também uma nova fórmula de atualização do parâmetro proximal para uma variante dos métodos de feixes. Estes métodos são bem conhecidos pela sua eficiência na resolução de problemas de otimização não diferenciável. Por último, propomos uma metodologia para eliminr soluções redundantes de programas inteiros combinatórios. Nossa proposta baseia-se na utilização da informação de simetria do problema, eliminam a simetria sem prejudicar a solução do problema inteiro.
Algorithms for general 0-1 mixed integer programs can be successfully developed by using lift-and-project methods to generate cuts. Cuts are generated by solving a cut- generation-program that depends on a certain normalization. From a theoretical point of view, the good numerical behavior of these cuts is not completely understood yet, specially, concerning to the normalization chosen. We consider a general normalization given by an arbitrary closed convex set, extending the theory developed in the 90's. We present a theoretical framework covering a wide group of already known normalizations. We also introduce new normalizations and analyze the properties of the associated cuts. In this work, we also propose a new updating rule for the prox parameter of a variant of the proximal bundle methods, making use of all the information available at each iteration. Proximal bundle methods are well known for their efficiency in nondifferentiable optimization. Finally, we introduce a way to eliminate redundant solutions ( due to geometrical symmetries ) of combinatorial integer program. This can be done by using the information about the problem symmetry in order to generate inequalities, which added to the formulation of the problem, eliminate this symmetry without affecting solution of the integer problem.
Los algoritmos para la resolución de problemas de programación mixta 0-1 generales que utilizan cortes derivados de los métodos lift-and-project, se han mostrado bastante eficientes en la práctica. Estos cortes se generan resolviendo un problema que depende de una cierta normalización. Desde el punto de vista teórico, el buen comportamiento de estos algoritmos no fue completamente comprendido, especialmente respecto a la normalización. En este trabajo consideramos normalizaciones generales definidas por un conjunto convexo cerrado arbitrario, extendiendo así el análisis teórico desarrollado en los años noventa. Presentamos un marco teórico que abarca todas las normalizaciones previamente estudiadas e introducimos nuevas normalizaciones, analizando las propiedades de los cortes asociados. Introducimos una nueva fórmula de actualización del parámetro de. Estoss métodos son bien conocidos por su eficiencia en la resolución de problemas de optimización no diferenciable. Por último, proponemos una metodología para eliminar soluciones redundantes de programas enteros combinatorios. Nuestra propuesta se basa en la utilización de la información de simetría del problema, eliminan la simetría sin perjudicar la solución del problema entero.
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Núñez, Araya Manuel A. (Manuel Adolfo) 1964. "Condition numbers and properties of central trajectories in convex programming." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10214.
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Wei, Hua. "Numerical Stability in Linear Programming and Semidefinite Programming." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2922.
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We study numerical stability for interior-point methods applied to Linear Programming, LP, and Semidefinite Programming, SDP. We analyze the difficulties inherent in current methods and present robust algorithms. We start with the error bound analysis of the search directions for the normal equation approach for LP. Our error analysis explains the surprising fact that the ill-conditioning is not a significant problem for the normal equation system. We also explain why most of the popular LP solvers have a default stop tolerance of only 10-8 when the machine precision on a 32-bit computer is approximately 10-16.
We then propose a simple alternative approach for the normal equation based interior-point method. This approach has better numerical stability than the normal equation based method. Although, our approach is not competitive in terms of CPU time for the NETLIB problem set, we do obtain higher accuracy. In addition, we obtain significantly smaller CPU times compared to the normal equation based direct solver, when we solve well-conditioned, huge, and sparse problems by using our iterative based linear solver. Additional techniques discussed are: crossover; purification step; and no backtracking.
Finally, we present an algorithm to construct SDP problem instances with prescribed strict complementarity gaps. We then introduce two measures of strict complementarity gaps. We empirically show that: (i) these measures can be evaluated accurately; (ii) the size of the strict complementarity gaps correlate well with the number of iteration for the SDPT3 solver, as well as with the local asymptotic convergence rate; and (iii) large strict complementarity gaps, coupled with the failure of Slater's condition, correlate well with loss of accuracy in the solutions. In addition, the numerical tests show that there is no correlation between the strict complementarity gaps and the geometrical measure used in [31], or with Renegar's condition number.
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Davidescu, Diana Maria. "Convexifiable smooth programming and applications." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82216.
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This thesis is an introduction to the study of convexification problems involving smooth functions in the area of continuous mathematical programming. The results are applied to a real life problem in oil production. An improved model is formulated for the company which yields environmentally friendlier optimal solutions at the same profit level.
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Sharifi, Mokhtarian Faranak. "Mathematical programming with LFS functions." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56762.
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Differentiable functions with a locally flat surface (LFS) have been recently introduced and studied in convex optimization. Here we extend this motion in two directions: to non-smooth convex and smooth generalized convex functions. An important feature of these functions is that the Karush-Kuhn-Tucker condition is both necessary and sufficient for optimality. Then we use the properties of linear LFS functions and basic point-to-set topology to study the "inverse" programming problem. In this problem, a feasible, but nonoptimal, point is made optimal by stable perturbations of the parameters. The results are applied to a case study in optimal production planning.
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Rolandsson, Jakob. "Programming as Mathematics – A Curriculum Perspective." Thesis, Uppsala universitet, Matematiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451806.
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Ellison, E. F. D. "Solution methods and applications of convex quadratic programming and its extensions." Thesis, Brunel University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436501.
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Asif, Muhammad Salman. "Primal dual pursuit a homotopy based algorithm for the Dantzig selector /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24693.
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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.Committee Chair: Romberg, Justin; Committee Member: McClellan, James; Committee Member: Mersereau, Russell
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Lu, Zhaosong. "Algorithm Design and Analysis for Large-Scale Semidefinite Programming and Nonlinear Programming." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7151.
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The limiting behavior of weighted paths associated with the semidefinite program (SDP) map $X^{1/2}SX^{1/2}$ was studied and some applications to error bound analysis and superlinear convergence of a class of
primal-dual interior-point methods were provided. A new approach for solving large-scale well-structured sparse SDPs via a saddle point mirror-prox algorithm with ${cal O}(epsilon^{-1})$ efficiency was developed based on exploiting sparsity structure and reformulating SDPs into smooth convex-concave saddle point problems. An iterative solver-based
long-step primal-dual infeasible path-following algorithm for convex quadratic programming (CQP) was developed. The search directions of
this algorithm were computed by means of a preconditioned iterative linear solver. A uniform bound, depending only on the CQP data, on
the number of iterations performed by a preconditioned iterative linear solver was established. A polynomial bound on the number of
iterations of this algorithm was also obtained. One efficient ``nearly exact' type of method for solving large-scale ``low-rank' trust region
subproblems was proposed by completely avoiding the computations of Cholesky or partial Cholesky factorizations. A computational study of this method was also provided by applying it to solve some large-scale nonlinear programming problems.
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Moor, Oege de. "Categories, relations and dynamic programming." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305600.
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