Neste trabalho é apresentada uma ferramenta de otimização para análise de problemas de alocação de água em bacias hidrográficas utilizando técnicas de programação linear e linear por partes, integradas a um modelo de amortecimentos de ondas em canais. A otimização é feita de forma global, com uso de softwares de programação linear baseados nos métodos de pontos interiores. A metodologia de uso do sistema consiste em se obter uma solução ?ótima? para situações de disponibilidade de água insuficiente a todos os usos conflitantes na bacia. A ferramenta está sendo acoplada e incorporada ao AcquaNet, um Sistema de Suporte a Decisões (SSD) para análise de sistemas de recursos hídricos, que utiliza um algoritmo de rede de fluxo afim de otimizar a alocação de água. A formulação utilizando programação linear permite a análise global do sistema e por isso, espera-se melhor aproveitamento da água disponível, seja no menor déficit de atendimento às demandas ou maior armazenamento nos reservatórios. A programação linear com utilização de métodos de pontos interiores é atualmente uma técnica bastante conhecida e bem desenvolvida. Existem vários pacotes computacionais gratuitos com implementações eficientes dos métodos de pontos interiores que motivaram sua utilização neste trabalho. / This work presents an optimization tool for analyzing the problems of water allocation in watersheds by utilizing techniques of linear and piecewise linear programming integrated to a pattern of stream flow routing. The optimization is done in a global way with the usage of linear programming packages based upon the Internal Point Methods. The methodology of the usage consists in the acquirement of an optimal solution for situation of insufficient water availability for all conflicting consumptions from the watershed. The tool is being attached and incorporated to AcquaNet, which is a decision support system (DSS) for analysis of water resources systems that utilizes a network flow algorithm, with the purpose of optimizing the water allocation. The formulation that uses the linear programming leads to the analysis of the system as a whole and for this reason it is expected a better usage of the available water with a lower deficit in the supply or a greater storage in the reservoirs. Linear Programming with Internal Point Methods is nowadays a well known and very well developed technique. There are several computational packages with efficient implementations of the Internal Points Methods freely available, and that, has brought great motivation in its usage in the present work.

Este trabalho tem por objetivo estudar e analisar a influência do parâmetro de barreira e de seu fator de correção no processo de convergência dos métodos de pontos interiores primal-dual, primal-dual barreira modificada e primal-dual barreira modificada com as técnicas preditor-corretor e Newton composto. A grande motivação para o desenvolvimento desta pesquisa está relacionada com a busca de métodos eficientes para resolver problemas de otimização de programação não-linear, existentes na área de engenharia elétrica mais especificamente na operação de sistemas elétricos de potência. Esses métodos foram aplicados a um problema de programação não-linear e aos sistemas elétricos de três e de trinta barras para analisar a sensibilidade em relação ao parâmetro de barreira e ao seu fator de correção. / This work has for objective to study and to analyze the influence of the barrier parameter and its correction factor in the convergence process of the methods primal-dual interior point, primal-dual modified barrier and primal-dual barrier modified with the techniques predictor-corrector and composed Newton. The great motivation for the development of this research is related with the search of efficient methods to solve nonlinear programming optimization problems, existent in the area of electric engineering more specifically in the operation of power systems. Those methods were applied to a nonlinear programming problem and the electric systems of three and thirty buses to analyze the sensibility in relation to the barrier parameter and its correction factor.

Este trabalho tem por objetivo estudar e analisar a influência do parâmetro de barreira e de seu fator de correção no processo de convergência dos métodos de pontos interiores primal-dual, primal-dual barreira modificada e primal-dual barreira modificada com as técnicas preditor-corretor e Newton composto. A grande motivação para o desenvolvimento desta pesquisa está relacionada com a busca de métodos eficientes para resolver problemas de otimização de programação não-linear, existentes na área de engenharia elétrica mais especificamente na operação de sistemas elétricos de potência. Esses métodos foram aplicados a um problema de programação não-linear e aos sistemas elétricos de três e de trinta barras para analisar a sensibilidade em relação ao parâmetro de barreira e ao seu fator de correção. / This work has for objective to study and to analyze the influence of the barrier parameter and its correction factor in the convergence process of the methods primal-dual interior point, primal-dual modified barrier and primal-dual barrier modified with the techniques predictor-corrector and composed Newton. The great motivation for the development of this research is related with the search of efficient methods to solve nonlinear programming optimization problems, existent in the area of electric engineering more specifically in the operation of power systems. Those methods were applied to a nonlinear programming problem and the electric systems of three and thirty buses to analyze the sensibility in relation to the barrier parameter and its correction factor.

Résumé Nous présentons en premier lieu dans ce travail les principales notions de la théorie de l'Analyse Limite (AL) — ou théorie des charges limites — en mécanique. Puis nous proposons une méthode de point intérieur destinée à résoudre des problèmes de programmation convexe posés par la méthode statique de l'AL, en vue d'obtenir des bornes inférieures de la charge limite (ou de ruine) d'un système mécanique. Les principales caractéristiques de cette méthode de point intérieur sont exposées en détail, et particulièrement son itération type. En second lieu, nous exposons l'application de cet algorithme sur un problème concret d'analyse limite, sur une large gamme de tailles numériques, et nous comparons pour validation les résultats obtenus avec ceux déjà existants ainsi qu'avec ceux calculés à partir de versions linéarisées du problème statique. Nous analysons également les résultats obtenus pour des problèmes classiques avec matériaux de Gurson, pour lesquels la linéarisation ou la programmation conique ne s'applique pas. La deuxième partie de cet ouvrage a trait à la méthode cinématique de l'analyse limite, qui, elle, s'occupe de fournir des bornes supérieures des charges limites. En premier lieu, nous traitons de l'équivalence entre la méthode cinématique classique et la méthode cinématique mixe, en partant d'une l'approche variationnelle fournie précédemment par Radenkovic et Nguyen. Ensuite, prenant en compte les exigences particulières aux formulations numériques, nous présentons une méthode mixte originale, parfaitement cinématique, utilisant aussi bien des champs de vitesses linéaires que quadratiques, continus ou discontinus. Son modus operandi pratique est tiré de l'analyse des conditions d'optimalité de Karush, Kuhn et Tucker, fournissant par là un exemple significatif d'interaction fructueuse entre la mécanique et la programmation mathématique. La méthode est testée sur des problèmes classiques avec les critères de plasticité de von Mises/Tresca et Gurson. Ces test démontrent l'efficacité remarquable de cette méthode mixte — qui par ailleurs n'utilise que le critère de plasticité comme information sur le matériau — et sa robustesse, laquelle s'avère même supérieure à celle de codes commerciaux récents de programmation conique. Enfin, nous présentons une approche de décomposition, elle aussi originale, des problèmes de bornes supérieures en analyse limite. Cette approche est basée à la fois sur la méthode cinématique mixte et l'algorithme de point intérieur précédents, et elle est conçue pour utiliser jusqu'à des champs de vitesse quadratiques discontinus. Détaillée dans le cas de la déformation plane, cette approche apparaît très rapidement convergente, ainsi que nous le vérifions sur le problème du barreau comprimé de von Mises/Tresca dans le cas de champs de vitesse linéaires continus. Puis elle est appliquée, dans le cas de champs quadratiques discontinus, au problème classique de la stabilité du talus vertical de Tresca, avec des résultats particulièrement remarquables puisqu'ils améliorent nettement les solutions cinématiques connues jusqu'à présent dans la littérature sur le sujet. Cette caractéristique de forte convergence qualifie particulièrement cette méthode de décomposition comme algorithme de base pour une parallélisation directe— ou récursive — de l'approche par éléments finis de l'analyse limite. Abstract Firstly, the main notions of the theory of Limit analysis (LA) in Mechanics —or collapse load theory – is presented. Then is proposed an Interior Point method to solve convex programming problems raised by the static method of LA, in order to obtain lower bounds to the collapse (or limit) load of a mechanical system. We explain the main features of this Interior Point method, describing in particular its typical iteration. Secondly, we show and analyze the results of its application to a practical Limit Analysis problem, for a wide range of sizes, and we compare them for validation with existing results and with those of linearized versions of the static problem. Classical problems are also analyzed for Gurson materials to which linearization or conic programming does not apply. The second part of this work focuses on the kinematical method of Limit Analysis, aiming this time to provide upper bounds on collapse loads. In a first step, we detail the equivalence between the classical an general mixed approaches, starting from an earlier variational approach of Radenkovic and Nguyen. In a second step, keeping in mind numerical formulation requirements, an original purely kinematical mixed method—using linear or quadratic, continuous or discontinuous velocity fields as virtual variables—is proposed. Its practical modus operandi is deduced from the Karush-Kuhn-Tucker optimality conditions, providing an example of crossfertilization between mechanics and mathematical programming. The method is tested on classical problems for von Mises/tresca and Gurson plasticity criteria. Using only the yield criterion as material data, it appears very efficient and robust, even more reliable than recent conic commercial codes. Furthermore, both static and kinematic present approaches give rise to the first solutions of problem for homogeneous Gurson materials. Finally, an original decomposition approach of the upper bound method of limit analysis is proposed. It is based on both previous kinematical approach and interior point solver, using up to discontinuous quadratic velocity. Detailed in plane strain, this method appears very rapidly convergent, as verified in the von Mises/Tresca compressed bar problem in the linear continuous velocity case. Then the method is applied, using discontinuous quadratic velocity fields, to the classical problem of the stability of a Tresca vertical cut, with very significant results as they notably improved the kinematical solutions of the literature. Moreover its strong convergence qualifies this decomposition scheme as a suitable algorithm for a direct—or recursive—parallelization of the LA finite element approach.

Optimization is a scientific discipline that lies at the boundary between pure and applied mathematics. Indeed, while on the one hand some of its developments involve rather theoretical concepts, its most successful algorithms are on the other hand heavily used by numerous companies to solve scheduling and design problems on a daily basis. Our research started with the study of the conic formulation for convex optimization problems. This approach was already studied in the seventies but has recently gained a lot of interest due to development of a new class of algorithms called interior-point methods. This setting is able to exploit the two most important characteristics of convexity: - a very rich duality theory (existence of a dual problem that is strongly related to the primal problem, with a very symmetric formulation), - the ability to solve these problems efficiently, both from the theoretical (polynomial algorithmic complexity) and practical (implementations allowing the resolution of large-scale problems) point of views. Most of the research in this area involved so-called self-dual cones, where the dual problem has exactly the same structure as the primal: the most famous classes of convex optimization problems (linear optimization, convex quadratic optimization and semidefinite optimization) belong to this category. We brought some contributions in this field: - a survey of interior-point methods for linear optimization, with an emphasis on the fundamental principles that lie behind the design of these algorithms, - a computational study of a method of linear approximation of convex quadratic optimization (more precisely, the second-order cone that can be used in the formulation of quadratic problems is replaced by a polyhedral approximation whose accuracy that can be guaranteed a priori), - an application of semidefinite optimization to classification, whose principle consists in separating different classes of patterns using ellipsoids defined in the feature space (this approach was successfully applied to the prediction of student grades). However, our research focussed on a much less studied category of convex problems which does not rely on self-dual cones, i.e. structured problems whose dual is formulated very differently from the primal. We studied in particular - geometric optimization, developed in the late sixties, which possesses numerous application in the field of engineering (entropy optimization, used in information theory, also belongs to this class of problems) - l_p-norm optimization, a generalization of linear and convex quadratic optimization, which allows the formulation of constraints built around expressions of the form |ax+b|^p (where p is a fixed exponent strictly greater than 1). For each of these classes of problems, we introduced a new type of convex cone that made their formulation as standard conic problems possible. This allowed us to derive very simplified proofs of the classical duality results pertaining to these problems, notably weak duality (a mere consequence of convexity) and the absence of a duality gap (strong duality property without any constraint qualification, which does not hold in the general convex case). We also uncovered a very surprising result that stipulates that geometric optimization can be viewed as a limit case of l_p-norm optimization. Encouraged by the similarities we observed, we developed a general framework that encompasses these two classes of problems and unifies all the previously obtained conic formulations. We also brought our attention to the design of interior-point methods to solve these problems. The theory of polynomial algorithms for convex optimization developed by Nesterov and Nemirovsky asserts that the main ingredient for these methods is a computable self-concordant barrier function for the corresponding cones. We were able to define such a barrier function in the case of l_p-norm optimization (whose parameter, which is the main determining factor in the algorithmic complexity of the method, is proportional to the number of variables in the formulation and independent from p) as well as in the case of the general framework mentioned above. Finally, we contributed a survey of the self-concordancy property, improving some useful results about the value of the complexity parameter for certain categories of barrier functions and providing some insight on the reason why the most commonly adopted definition for self-concordant functions is the best possible.

This thesis describes research into the problem of transmission congestion management. The causes, remedies, pricing methods, and other issues of transmission congestion are briefly reviewed. This research is to develop market-based approaches to cope with transmission congestion in real-time, short-run and long-run efficiently, economically and fairly. Extended OPF techniques have been playing key roles in many aspects of electricity markets. The Primal-Dual Interior Point Linear Programming and Quadratic Programming are applied to solve various optimization problems of congestion management proposed in the thesis. A coordinated real-time optimal dispatch method for unbundled electricity markets is proposed for system balancing and congestion management. With this method, almost all the possible resources in different electricity markets, including operating reserves and bilateral transactions, can be used to eliminate the real-time congestion according to their bids into the balancing market. Spot pricing theory is applied to real-time congestion pricing. Under the same framework, a Lagrangian Relaxation based region decomposition OPF algorithm is presented to deal with the problems of real-time active power congestion management across multiple regions. The inter/intra-regional congestion can be relieved without exchanging any information between regional ISOs but the Lagrangian Multipliers. In day-ahead spot market, a new optimal dispatch method is proposed for congestion and price risk management, particularly for bilateral transaction curtailment. Individual revenue adequacy constraints, which include payments from financial instruments, are involved in the original dispatch problem. An iterative procedure is applied to solve this special optimization problem with both primal and dual variables involved in its constraints. An optimal Financial Transmission Rights (FTR) auction model is presented as an approach to the long-term congestion management. Two types of series F ACTS devices are incorporated into this auction problem using the Power Injection Model to maximize the auction revenue. Some new treatment has been done on TCSC's operating limits to keep the auction problem linear.

Neste trabalho é apresentada uma ferramenta de otimização para análise de problemas de alocação de água em bacias hidrográficas utilizando técnicas de programação linear e linear por partes, integradas a um modelo de amortecimentos de ondas em canais. A otimização é feita de forma global, com uso de softwares de programação linear baseados nos métodos de pontos interiores. A metodologia de uso do sistema consiste em se obter uma solução ?ótima? para situações de disponibilidade de água insuficiente a todos os usos conflitantes na bacia. A ferramenta está sendo acoplada e incorporada ao AcquaNet, um Sistema de Suporte a Decisões (SSD) para análise de sistemas de recursos hídricos, que utiliza um algoritmo de rede de fluxo afim de otimizar a alocação de água. A formulação utilizando programação linear permite a análise global do sistema e por isso, espera-se melhor aproveitamento da água disponível, seja no menor déficit de atendimento às demandas ou maior armazenamento nos reservatórios. A programação linear com utilização de métodos de pontos interiores é atualmente uma técnica bastante conhecida e bem desenvolvida. Existem vários pacotes computacionais gratuitos com implementações eficientes dos métodos de pontos interiores que motivaram sua utilização neste trabalho. / This work presents an optimization tool for analyzing the problems of water allocation in watersheds by utilizing techniques of linear and piecewise linear programming integrated to a pattern of stream flow routing. The optimization is done in a global way with the usage of linear programming packages based upon the Internal Point Methods. The methodology of the usage consists in the acquirement of an optimal solution for situation of insufficient water availability for all conflicting consumptions from the watershed. The tool is being attached and incorporated to AcquaNet, which is a decision support system (DSS) for analysis of water resources systems that utilizes a network flow algorithm, with the purpose of optimizing the water allocation. The formulation that uses the linear programming leads to the analysis of the system as a whole and for this reason it is expected a better usage of the available water with a lower deficit in the supply or a greater storage in the reservoirs. Linear Programming with Internal Point Methods is nowadays a well known and very well developed technique. There are several computational packages with efficient implementations of the Internal Points Methods freely available, and that, has brought great motivation in its usage in the present work.

Orientador: Ariovaldo Verandio Garcia, Ruben Augusto Romero Lazaro / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T06:56:43Z (GMT). No. of bitstreams: 1 RiderFlores_MarcosJulio_D.pdf: 1021887 bytes, checksum: 6000961c2f5457b410ac691912476270 (MD5) Previous issue date: 2006 / Resumo: Neste trabalho são propostos modelos matemáticos e técnicas de solução para resolver o problema de planejamento da expansão de sistemas de transmissão através de três enfoques. a) Usando o modelo de corrente alternada do sistema de transmissão e um algoritmo heurístico construtivo especializado para resolver o problema de planejamento, e, ainda, realiza-se uma primeira tentativa de alocação de fontes de potência reativas; b) Usando o modelo de corrente contínua e técnicas de programação não-linear especializadas. Nesse caso emprega-se uma versão relaxada do problema de planejamento da expansão de sistemas de transmissão usando o modelo de corrente contínua, onde a integralidade das variáveis de investimento é desprezada. Resolve-se o problema de programação não-linear, modelado de forma matricial com um algoritmo de otimização especializado e, além disso, um algoritmo heurístico construtivo especializado é utilizado para resolver o problema de planejamento. c) Usando o modelo de corrente contínua e um algoritmo Branch and Bound (B&B) sem empregar técnicas de decomposição. Para isso foram redefinidos os chamados testes de sondagem no algoritmo B&B e em cada nó da árvore de B&B tem-se um problema de programação não-linear que são resolvidos usando a metodologia desenvolvida no item (b). Os ítens (a), (b) e (c) requerem a solução de problemas de programação não-linear diferenciados. Uma revisão das características principais da resolução iterativa dos métodos de pontos interiores é apresentada. Foi desenvolvida uma técnica baseada em uma combinação de métodos de pontos interiores de alta ordem (MPI-AO) para resolver os problemas de programação não-linear de forma rápida, eficiente e robusta. Essa combinação dos MPI-AO tem como objetivo colocar num único método as características particulares de cada um dos MPI-AO e melhorar o desempenho computacional comparado com os MPI-AO de forma individual / Abstract: In this work mathematical models and solution techniques are proposed to solve the power system transmission expansion planning problem through three approaches: a) Using the nonlinear model ofthe transmission system (AC model) and a specialized constructive heuristic algorithm to solve the problem and, yet, a first attempt to allocate reactive power sources is also considered; b) Using the direct-current (DC) model and specialized techniques of nonlinear programming. In this case a version of the power system transmission expansion planning problem using the DC model where the integrality of the investment variables is relaxed is used. The nonlinear programming problem is solved with a specialized optimization algorithm and, moreover, a constructive heuristic algorithm is employed to solve the planning problem. c) Using the DC model and Branch and Bound (B&B) algorithm without the use of decomposition techniques. The so called fathoming tests of the B&B were redefined and at each node of the tree a nonlinear programming problem is solved using the method developed in b). Items a), b) and c) require the solution of distinct problems of nonlinear programming. A revision of the main characteristics of the iterative solution of the interior points methods is presented. An optimization technique based on a combination of the higher order interior point methods (HO-IPM) had been developed to solve the nonlinear programming problems in a fast, efficient and robust way. This combination of the HO-IPM has as objective to explore the particular characteristics of each method in a single one and to improve the comparative computational performance with the HO-IPM of individual form / Doutorado / Energia Eletrica / Doutor em Engenharia Elétrica

The goal of this thesis is two-fold. On the one hand, it pursues to provide a contribution to the conjugate duality by proposing a new duality concept, which can be understood as an umbrella for different meaningful perturbation methods. On the other hand, this thesis aims to investigate minimax location problems by means of the duality concept introduced in the first part of this work, followed by a numerical approach using epigraphical splitting methods. After summarizing some elements of the convex analysis as well as introducing important results needed later, we consider an optimization problem with geometric and cone constraints, whose objective function is a composition of n+1 functions. For this problem we propose a conjugate dual problem, where the functions involved in the objective function of the primal problem are decomposed. Furthermore, we formulate generalized interior point regularity conditions for strong duality and give necessary and sufficient optimality conditions. As applications of this approach we determine the formulae of the conjugate as well as the biconjugate of the objective function of the primal problem and analyze an optimization problem having as objective function the sum of reciprocals of concave functions. In the second part of this thesis we discuss in the sense of the introduced duality concept three classes of minimax location problems. The first one consists of nonlinear and linear single minimax location problems with geometric constraints, where the maximum of nonlinear or linear functions composed with gauges between pairs of a new and existing points will be minimized. The version of the nonlinear location problem is additionally considered with set-up costs. The second class of minimax location problems deals with multifacility location problems as suggested by Drezner (1991), where for each given point the sum of weighted distances to all facilities plus set-up costs is determined and the maximal value of these sums is to be minimized. As the last and third class the classical multifacility location problem with geometrical constraints is considered in a generalized form where the maximum of gauges between pairs of new facilities and the maximum of gauges between pairs of new and existing facilities will be minimized. To each of these location problems associated dual problems will be formulated as well as corresponding duality statements and necessary and sufficient optimality conditions. To illustrate the results of the duality approach and to give a more detailed characterization of the relations between the location problems and their corresponding duals, we consider examples in the Euclidean space. This thesis ends with a numerical approach for solving minimax location problems by epigraphical splitting methods. In this framework, we give formulae for the projections onto the epigraphs of several sums of powers of weighted norms as well as formulae for the projection onto the epigraphs of gauges. Numerical experiments document the usefulness of our approach for the discussed location problems.

Dans cette thèse, nous nous étudions des algorithmes d’optimisation non linéaire. D’une part nous proposons des techniques de détection rapide de la non-réalisabilité d’un problème à résoudre. D’autre part, nous analysons le comportement local des algorithmes pour la résolution de problèmes singuliers. Dans la première partie, nous présentons une modification d’un algorithme de lagrangien augmenté pour l’optimisation avec contraintes d’égalité. La convergence quadratique du nouvel algorithme dans le cas non-réalisable est démontrée théoriquement et numériquement. La seconde partie est dédiée à l’extension du résultat précédent aux problèmes d’optimisation non linéaire généraux avec contraintes d’égalité et d’inégalité. Nous proposons une modification d’un algorithme de pénalisation mixte basé sur un lagrangien augmenté et une barrière logarithmique. Les résultats théoriques de l’analyse de convergence et quelques tests numériques montrent l’avantage du nouvel algorithme dans la détection de la non-réalisabilité. La troisième partie est consacrée à étudier le comportement local d’un algorithme primal-dual de points intérieurs pour l’optimisation sous contraintes de borne. L’analyse locale est effectuée sans l’hypothèse classique des conditions suffisantes d’optimalité de second ordre. Celle-ci est remplacée par une hypothèse plus faible basée sur la notion de borne d’erreur locale. Nous proposons une technique de régularisation de la jacobienne du système d’optimalité à résoudre. Nous démontrons ensuite des propriétés de bornitude de l’inverse de ces matrices régularisées, ce qui nous permet de montrer la convergence superlinéaire de l’algorithme. La dernière partie est consacrée à l’analyse de convergence locale de l’algorithme primal-dual qui est utilisé dans les deux premières parties de la thèse. En pratique, il a été observé que cet algorithme converge rapidement même dans le cas où les contraintes ne vérifient l’hypothèse de qualification de Mangasarian-Fromovitz. Nous démontrons la convergence superlinéaire et quadratique de cet algorithme, sans hypothèse de qualification des contraintes. / This thesis is devoted to the study of numerical algorithms for nonlinear optimization. On the one hand, we propose new strategies for the rapid infeasibility detection. On the other hand, we analyze the local behavior of primal-dual algorithms for the solution of singular problems. In the first part, we present a modification of an augmented Lagrangian algorithm for equality constrained optimization. The quadratic convergence of the new algorithm in the infeasible case is theoretically and numerically demonstrated. The second part is dedicated to extending the previous result to the solution of general nonlinear optimization problems with equality and inequality constraints. We propose a modification of a mixed logarithmic barrier-augmented Lagrangian algorithm. The theoretical convergence results and the numerical experiments show the advantage of the new algorithm for the infeasibility detection. In the third part, we study the local behavior of a primal-dual interior point algorithm for bound constrained optimization. The local analysis is done without the standard assumption of the second-order sufficient optimality conditions. These conditions are replaced by a weaker assumption based on a local error bound condition. We propose a regularization technique of the Jacobian matrix of the optimality system. We then demonstrate some boundedness properties of the inverse of these regularized matrices, which allow us to prove the superlinear convergence of our algorithm. The last part is devoted to the local convergence analysis of the primal-dual algorithm used in the first two parts of this thesis. In practice, it has been observed that this algorithm converges rapidly even in the case where the constraints do not satisfy the Mangasarian-Fromovitz constraint qualification. We demonstrate the superlinear and quadratic convergence of this algorithm without any assumption of constraint qualification.