High Efficiency Envelope Amplifier based on a Ripple Cancellation Buck Converter. Design, Optimization and Integration in an EER RFPA

Durante los últimos años la tendencia en el sector de las telecomunicaciones ha sido un aumento y diversificación en la transmisión de voz, video y fundamentalmente de datos. Para conseguir alcanzar las tasas de transmisión requeridas, los nuevos estándares de comunicaciones requieren un mayor ancho de banda y tienen un mayor factor de pico, lo cual influye en el bajo rendimiento del amplificador de radiofrecuencia (RFPA). Otro factor que ha influido en el bajo rendimiento es el diseño del amplificador de radiofrecuencia. Tradicionalmente se han utilizado amplificadores lineales por su buen funcionamiento. Sin embargo, debido al elevado factor de pico de las señales transmitidas, el rendimiento de este tipo de amplificadores es bajo. El bajo rendimiento del sistema conlleva desventajas adicionales como el aumento del coste y del tamaño del sistema de refrigeración, como en el caso de una estación base, o como la reducción del tiempo de uso y un mayor calentamiento del equipo para sistemas portátiles alimentados con baterías. Debido a estos factores, se han desarrollado durante las últimas décadas varias soluciones para aumentar el rendimiento del RFPA como la técnica de Outphasing, combinadores de potencia o la técnica de Doherty. Estas soluciones mejoran las prestaciones del RFPA y en algún caso han sido ampliamente utilizados comercialmente como la técnica de Doherty, que alcanza rendimientos hasta del 50% para el sistema completo para anchos de banda de hasta 20MHz. Pese a las mejoras obtenidas con estas soluciones, los mayores rendimientos del sistema se obtienen para soluciones basadas en la modulación de la tensión de alimentación del amplificador de potencia como “Envelope Tracking” o “EER”. La técnica de seguimiento de envolvente o “Envelope Tracking” está basada en la modulación de la tensión de alimentación de un amplificador lineal de potencia para obtener una mejora en el rendimiento en el sistema comparado a una solución con una tensión de alimentación constante. Para la implementación de esta técnica se necesita una etapa adicional, el amplificador de envolvente, que añade complejidad al amplificador de radiofrecuencia. En un amplificador diseñado con esta técnica, se aumentan las pérdidas debido a la etapa adicional que supone el amplificador de envolvente pero a su vez disminuyen las pérdidas en el amplificador de potencia. Si el diseño se optimiza adecuadamente, puede conseguirse un aumento global en el rendimiento del sistema superior al conseguido con las técnicas mencionadas anteriormente. Esta técnica presenta ventajas en el diseño del amplificador de envolvente, ya que el ancho de banda requerido puede ser menor que el ancho de banda de la señal de envolvente si se optimiza adecuadamente el diseño. Adicionalmente, debido a que la sincronización entre la señal de envolvente y de fase no tiene que ser perfecta, el proceso de integración conlleva ciertas ventajas respecto a otras técnicas como EER. La técnica de eliminación y restauración de envolvente, llamada EER o técnica de Kahn está basada en modulación simultánea de la envolvente y la fase de la señal usando un amplificador de potencia conmutado, no lineal y que permite obtener un elevado rendimiento. Esta solución fue propuesta en el año 1952, pero no ha sido implementada con éxito durante muchos años debido a los exigentes requerimientos en cuanto a la sincronización entre fase y envolvente, a las técnicas de control y de corrección de los errores y no linealidades de cada una de las etapas así como de los equipos para poder implementar estas técnicas, que tienen unos requerimientos exigentes en capacidad de cálculo y procesamiento. Dentro del diseño de un RFPA, el amplificador de envolvente tiene una gran importancia debido a su influencia en el rendimiento y ancho de banda del sistema completo. Adicionalmente, la linealidad y la calidad de la señal de transmitida deben ser elevados para poder cumplir con los diferentes estándares de telecomunicaciones. Esta tesis se centra en el amplificador de envolvente y el objetivo principal es el desarrollo de soluciones que permitan el aumento del rendimiento total del sistema a la vez que satisfagan los requerimientos de ancho de banda, calidad de la señal transmitida y de linealidad. Debido al elevado rendimiento que potencialmente puede alcanzarse con la técnica de EER, esta técnica ha sido objeto de análisis y en el estado del arte pueden encontrarse numerosas referencias que analizan el diseño y proponen diversas implementaciones. En una clasificación de alto nivel, podemos agrupar las soluciones propuestas del amplificador de envolvente según estén compuestas de una o múltiples etapas. Las soluciones para el amplificador de envolvente en una configuración multietapa se basan en la combinación de un convertidor conmutado, de elevado rendimiento con un regulador lineal, de alto ancho de banda, en una combinación serie o paralelo. Estas soluciones, debido a la combinación de las características de ambas etapas, proporcionan un buen compromiso entre rendimiento y buen funcionamiento del amplificador de RF. Por otro lado, la complejidad del sistema aumenta debido al mayor número de componentes y de señales de control necesarias y el aumento de rendimiento que se consigue con estas soluciones es limitado. Una configuración en una etapa tiene las ventajas de una mayor simplicidad, pero debido al elevado ancho de banda necesario, la frecuencia de conmutación debe aumentarse en gran medida. Esto implicará un bajo rendimiento y un peor funcionamiento del amplificador de envolvente. En el estado del arte pueden encontrarse diversas soluciones para un amplificador de envolvente en una etapa, como aumentar la frecuencia de conmutación y realizar la implementación en un circuito integrado, que tendrá mejor funcionamiento a altas frecuencias o utilizar técnicas topológicas y/o filtros de orden elevado, que permiten una reducción de la frecuencia de conmutación. En esta tesis se propone de manera original el uso de la técnica de cancelación de rizado, aplicado al convertidor reductor síncrono, para reducir la frecuencia de conmutación comparado con diseño equivalente del convertidor reductor convencional. Adicionalmente se han desarrollado dos variantes topológicas basadas en esta solución para aumentar la robustez y las prestaciones de la misma. Otro punto de interés en el diseño de un RFPA es la dificultad de poder estimar la influencia de los parámetros de diseño del amplificador de envolvente en el amplificador final integrado. En esta tesis se ha abordado este problema y se ha desarrollado una herramienta de diseño que permite obtener las principales figuras de mérito del amplificador integrado para la técnica de EER a partir del diseño del amplificador de envolvente. Mediante el uso de esta herramienta pueden validarse el efecto del ancho de banda, el rizado de tensión de salida o las no linealidades del diseño del amplificador de envolvente para varias modulaciones digitales. Las principales contribuciones originales de esta tesis son las siguientes: La aplicación de la técnica de cancelación de rizado a un convertidor reductor síncrono para un amplificador de envolvente de alto rendimiento para un RFPA linealizado mediante la técnica de EER. Una reducción del 66% en la frecuencia de conmutación, comparado con el reductor convencional equivalente. Esta reducción se ha validado experimentalmente obteniéndose una mejora en el rendimiento de entre el 12.4% y el 16% para las especificaciones de este trabajo. La topología y el diseño del convertidor reductor con dos redes de cancelación de rizado en cascada para mejorar el funcionamiento y robustez de la solución con una red de cancelación. La combinación de un convertidor redactor multifase con la técnica de cancelación de rizado para obtener una topología que proporciona una reducción del cociente entre frecuencia de conmutación y ancho de banda de la señal. El proceso de optimización del control del amplificador de envolvente en lazo cerrado para mejorar el funcionamiento respecto a la solución en lazo abierto del convertidor reductor con red de cancelación de rizado. Una herramienta de simulación para optimizar el proceso de diseño del amplificador de envolvente mediante la estimación de las figuras de mérito del RFPA, implementado mediante EER, basada en el diseño del amplificador de envolvente. La integración y caracterización del amplificador de envolvente basado en un convertidor reductor con red de cancelación de rizado en el transmisor de radiofrecuencia completo consiguiendo un elevado rendimiento, entre 57% y 70.6% para potencias de salida de 14.4W y 40.7W respectivamente. Esta tesis se divide en seis capítulos. El primer capítulo aborda la introducción enfocada en la aplicación, los amplificadores de potencia de radiofrecuencia, así como los principales problemas, retos y soluciones existentes. En el capítulo dos se desarrolla el estado del arte de amplificadores de potencia de RF, describiéndose las principales técnicas de diseño, las causas de no linealidad y las técnicas de optimización. El capítulo tres está centrado en las soluciones propuestas para el amplificador de envolvente. El modo de control se ha abordado en este capítulo y se ha presentado una optimización del diseño en lazo cerrado para el convertidor reductor convencional y para el convertidor reductor con red de cancelación de rizado. El capítulo cuatro se centra en el proceso de diseño del amplificador de envolvente. Se ha desarrollado una herramienta de diseño para evaluar la influencia del amplificador de envolvente en las figuras de mérito del RFPA. En el capítulo cinco se presenta el proceso de integración realizado y las pruebas realizadas para las diversas modulaciones, así como la completa caracterización y análisis del amplificador de RF. El capítulo seis describe las principales conclusiones de la tesis y las líneas futuras. ABSTRACT The trend in the telecommunications sector during the last years follow a high increase in the transmission rate of voice, video and mainly in data. To achieve the required levels of data rates, the new modulation standards demand higher bandwidths and have a higher peak to average power ratio (PAPR). These specifications have a direct impact in the low efficiency of the RFPA. An additional factor for the low efficiency of the RFPA is in the power amplifier design. Traditionally, linear classes have been used for the implementation of the power amplifier as they comply with the technical requirements. However, they have a low efficiency, especially in the operating range of signals with a high PAPR. The low efficiency of the transmitter has additional disadvantages as an increase in the cost and size as the cooling system needs to be increased for a base station and a temperature increase and a lower use time for portable devices. Several solutions have been proposed in the state of the art to improve the efficiency of the transmitter as Outphasing, power combiners or Doherty technique. However, the highest potential of efficiency improvement can be obtained using a modulated power supply for the power amplifier, as in the Envelope Tracking and EER techniques. The Envelope Tracking technique is based on the modulation of the power supply of a linear power amplifier to improve the overall efficiency compared to a fixed voltage supply. In the implementation of this technique an additional stage is needed, the envelope amplifier, that will increase the complexity of the RFPA. However, the efficiency of the linear power amplifier will increase and, if designed properly, the RFPA efficiency will be improved. The advantages of this technique are that the envelope amplifier design does not require such a high bandwidth as the envelope signal and that in the integration process a perfect synchronization between envelope and phase is not required. The Envelope Elimination and Restoration (EER) technique, known also as Kahn’s technique, is based on the simultaneous modulation of envelope and phase using a high efficiency switched power amplifier. This solution has the highest potential in terms of the efficiency improvement but also has the most challenging specifications. This solution, proposed in 1952, has not been successfully implemented until the last two decades due to the high demanding requirements for each of the stages as well as for the highly demanding processing and computation capabilities needed. At the system level, a very precise synchronization is required between the envelope and phase paths to avoid a linearity decrease of the system. Several techniques are used to compensate the non-linear effects in amplitude and phase and to improve the rejection of the out of band noise as predistortion, feedback and feed-forward. In order to obtain a high bandwidth and efficient RFPA using either ET or EER, the envelope amplifier stage will have a critical importance. The requirements for this stage are very demanding in terms of bandwidth, linearity and quality of the transmitted signal. Additionally the efficiency should be as high as possible, as the envelope amplifier has a direct impact in the efficiency of the overall system. This thesis is focused on the envelope amplifier stage and the main objective will be the development of high efficiency envelope amplifier solutions that comply with the requirements of the RFPA application. The design and optimization of an envelope amplifier for a RFPA application is a highly referenced research topic, and many solutions that address the envelope amplifier and the RFPA design and optimization can be found in the state of the art. From a high level classification, multiple and single stage envelope amplifiers can be identified. Envelope amplifiers for EER based on multiple stage architecture combine a linear assisted stage and a switched-mode stage, either in a series or parallel configuration, to achieve a very high performance RFPA. However, the complexity of the system increases and the efficiency improvement is limited. A single-stage envelope amplifier has the advantage of a lower complexity but in order to achieve the required bandwidth the switching frequency has to be highly increased, and therefore the performance and the efficiency are degraded. Several techniques are used to overcome this limitation, as the design of integrated circuits that are capable of switching at very high rates or the use of topological solutions, high order filters or a combination of both to reduce the switching frequency requirements. In this thesis it is originally proposed the use of the ripple cancellation technique, applied to a synchronous buck converter, to reduce the switching frequency requirements compared to a conventional buck converter for an envelope amplifier application. Three original proposals for the envelope amplifier stage, based on the ripple cancellation technique, are presented and one of the solutions has been experimentally validated and integrated in the complete amplifier, showing a high total efficiency increase compared to other solutions of the state of the art. Additionally, the proposed envelope amplifier has been integrated in the complete RFPA achieving a high total efficiency. The design process optimization has also been analyzed in this thesis. Due to the different figures of merit between the envelope amplifier and the complete RFPA it is very difficult to obtain an optimized design for the envelope amplifier. To reduce the design uncertainties, a design tool has been developed to provide an estimation of the RFPA figures of merit based on the design of the envelope amplifier. The main contributions of this thesis are: The application of the ripple cancellation technique to a synchronous buck converter for an envelope amplifier application to achieve a high efficiency and high bandwidth EER RFPA. A 66% reduction of the switching frequency, validated experimentally, compared to the equivalent conventional buck converter. This reduction has been reflected in an improvement in the efficiency between 12.4% and 16%, validated for the specifications of this work. The synchronous buck converter with two cascaded ripple cancellation networks (RCNs) topology and design to improve the robustness and the performance of the envelope amplifier. The combination of a phase-shifted multi-phase buck converter with the ripple cancellation technique to improve the envelope amplifier switching frequency to signal bandwidth ratio. The optimization of the control loop of an envelope amplifier to improve the performance of the open loop design for the conventional and ripple cancellation buck converter. A simulation tool to optimize the envelope amplifier design process. Using the envelope amplifier design as the input data, the main figures of merit of the complete RFPA for an EER application are obtained for several digital modulations. The successful integration of the envelope amplifier based on a RCN buck converter in the complete RFPA obtaining a high efficiency integrated amplifier. The efficiency obtained is between 57% and 70.6% for an output power of 14.4W and 40.7W respectively. The main figures of merit for the different modulations have been characterized and analyzed. This thesis is organized in six chapters. In Chapter 1 is provided an introduction of the RFPA application, where the main problems, challenges and solutions are described. In Chapter 2 the technical background for radiofrequency power amplifiers (RF) is presented. The main techniques to implement an RFPA are described and analyzed. The state of the art techniques to improve performance of the RFPA are identified as well as the main sources of no-linearities for the RFPA. Chapter 3 is focused on the envelope amplifier stage. The three different solutions proposed originally in this thesis for the envelope amplifier are presented and analyzed. The control stage design is analyzed and an optimization is proposed both for the conventional and the RCN buck converter. Chapter 4 is focused in the design and optimization process of the envelope amplifier and a design tool to evaluate the envelope amplifier design impact in the RFPA is presented. Chapter 5 shows the integration process of the complete amplifier. Chapter 6 addresses the main conclusions of the thesis and the future work.

Automatic design of Ant Colony Optimization for permutation problems

In this study, we present a framework based on ant colony optimization (ACO) for tackling combinatorial problems. ACO algorithms have been applied to many diferent problems, focusing on algorithmic variants that obtain high-quality solutions. Usually, the implementations are re-done for various problem even if they maintain the same details of the ACO algorithm. However, our goal is to generate a sustainable framework for applications on permutation problems. We concentrate on understanding the behavior of pheromone trails and specific methods that can be combined. Eventually, we will propose an automatic offline configuration tool to build an efective algorithm. ---RESUMEN---En este trabajo vamos a presentar un framework basado en la familia de algoritmos ant colony optimization (ACO), los cuales están dise~nados para enfrentarse a problemas combinacionales. Los algoritmos ACO han sido aplicados a diversos problemas, centrándose los investigadores en diversas variantes que obtienen buenas soluciones. Normalmente, las implementaciones se tienen que rehacer, inclusos si se mantienen los mismos detalles para los algoritmos ACO. Sin embargo, nuestro objetivo es generar un framework sostenible para aplicaciones sobre problemas de permutaciones. Nos centraremos en comprender el comportamiento de la sendas de feromonas y ciertos métodos con los que pueden ser combinados. Finalmente, propondremos una herramienta para la configuraron automática offline para construir algoritmos eficientes.

Two-dimensional mesh optimization in the finite element method

The solution to the problem of finding the optimum mesh design in the finite element method with the restriction of a given number of degrees of freedom, is an interesting problem, particularly in the applications method. At present, the usual procedures introduce new degrees of freedom (remeshing) in a given mesh in order to obtain a more adequate one, from the point of view of the calculation results (errors uniformity). However, from the solution of the optimum mesh problem with a specific number of degrees of freedom some useful recommendations and criteria for the mesh construction may be drawn. For 1-D problems, namely for the simple truss and beam elements, analytical solutions have been found and they are given in this paper. For the more complex 2-D problems (plane stress and plane strain) numerical methods to obtain the optimum mesh, based on optimization procedures have to be used. The objective function, used in the minimization process, has been the total potential energy. Some examples are presented. Finally some conclusions and hints about the possible new developments of these techniques are also given.

Interval-based ranking in noisy evolutionary multiobjective optimization

As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

Structural efficiency variation with the problem size in some bending problems = Variación del rendimiento estructural con el tamaño del problema en algunos casos de flexión simple

Existe normalmente el propósito de obtener la mejor solución posible cuando se plantea un problema estructural, entendiendo como mejor la solución que cumpliendo los requisitos estructurales, de uso, etc., tiene un coste físico menor. En una primera aproximación se puede representar el coste físico por medio del peso propio de la estructura, lo que permite plantear la búsqueda de la mejor solución como la de menor peso. Desde un punto de vista práctico, la obtención de buenas soluciones—es decir, soluciones cuyo coste sea solo ligeramente mayor que el de la mejor solución— es una tarea tan importante como la obtención de óptimos absolutos, algo en general difícilmente abordable. Para disponer de una medida de la eficiencia que haga posible la comparación entre soluciones se propone la siguiente definición de rendimiento estructural: la razón entre la carga útil que hay que soportar y la carga total que hay que contabilizar (la suma de la carga útil y el peso propio). La forma estructural puede considerarse compuesta por cuatro conceptos, que junto con el material, definen una estructura: tamaño, esquema, proporción, y grueso.Galileo (1638) propuso la existencia de un tamaño insuperable para cada problema estructural— el tamaño para el que el peso propio agota una estructura para un esquema y proporción dados—. Dicho tamaño, o alcance estructural, será distinto para cada material utilizado; la única información necesaria del material para su determinación es la razón entre su resistencia y su peso especifico, una magnitud a la que denominamos alcance del material. En estructuras de tamaño muy pequeño en relación con su alcance estructural la anterior definición de rendimiento es inútil. En este caso —estructuras de “talla nula” en las que el peso propio es despreciable frente a la carga útil— se propone como medida del coste la magnitud adimensional que denominamos número de Michell, que se deriva de la “cantidad” introducida por A. G. M. Michell en su artículo seminal de 1904, desarrollado a partir de un lema de J. C. Maxwell de 1870. A finales del siglo pasado, R. Aroca combino las teorías de Galileo y de Maxwell y Michell, proponiendo una regla de diseño de fácil aplicación (regla GA), que permite la estimación del alcance y del rendimiento de una forma estructural. En el presente trabajo se estudia la eficiencia de estructuras trianguladas en problemas estructurales de flexión, teniendo en cuenta la influencia del tamaño. Por un lado, en el caso de estructuras de tamaño nulo se exploran esquemas cercanos al optimo mediante diversos métodos de minoración, con el objetivo de obtener formas cuyo coste (medido con su numero deMichell) sea muy próximo al del optimo absoluto pero obteniendo una reducción importante de su complejidad. Por otro lado, se presenta un método para determinar el alcance estructural de estructuras trianguladas (teniendo en cuenta el efecto local de las flexiones en los elementos de dichas estructuras), comparando su resultado con el obtenido al aplicar la regla GA, mostrando las condiciones en las que es de aplicación. Por último se identifican las líneas de investigación futura: la medida de la complejidad; la contabilidad del coste de las cimentaciones y la extensión de los métodos de minoración cuando se tiene en cuenta el peso propio. ABSTRACT When a structural problem is posed, the intention is usually to obtain the best solution, understanding this as the solution that fulfilling the different requirements: structural, use, etc., has the lowest physical cost. In a first approximation, the physical cost can be represented by the self-weight of the structure; this allows to consider the search of the best solution as the one with the lowest self-weight. But, from a practical point of view, obtaining good solutions—i.e. solutions with higher although comparable physical cost than the optimum— can be as important as finding the optimal ones, because this is, generally, a not affordable task. In order to have a measure of the efficiency that allows the comparison between different solutions, a definition of structural efficiency is proposed: the ratio between the useful load and the total load —i.e. the useful load plus the self-weight resulting of the structural sizing—. The structural form can be considered to be formed by four concepts, which together with its material, completely define a particular structure. These are: Size, Schema, Slenderness or Proportion, and Thickness. Galileo (1638) postulated the existence of an insurmountable size for structural problems—the size for which a structure with a given schema and a given slenderness, is only able to resist its self-weight—. Such size, or structural scope will be different for every different used material; the only needed information about the material to determine such size is the ratio between its allowable stress and its specific weight: a characteristic length that we name material structural scope. The definition of efficiency given above is not useful for structures that have a small size in comparison with the insurmountable size. In this case—structures with null size, inwhich the self-weight is negligible in comparisonwith the useful load—we use as measure of the cost the dimensionless magnitude that we call Michell’s number, an amount derived from the “quantity” introduced by A. G. M. Michell in his seminal article published in 1904, developed out of a result from J. C.Maxwell of 1870. R. Aroca joined the theories of Galileo and the theories of Maxwell and Michell, obtaining some design rules of direct application (that we denominate “GA rule”), that allow the estimation of the structural scope and the efficiency of a structural schema. In this work the efficiency of truss-like structures resolving bending problems is studied, taking into consideration the influence of the size. On the one hand, in the case of structures with null size, near-optimal layouts are explored using several minimization methods, in order to obtain forms with cost near to the absolute optimum but with a significant reduction of the complexity. On the other hand, a method for the determination of the insurmountable size for truss-like structures is shown, having into account local bending effects. The results are checked with the GA rule, showing the conditions in which it is applicable. Finally, some directions for future research are proposed: the measure of the complexity, the cost of foundations and the extension of optimization methods having into account the self-weight.

Dynamic weighting in Monte Carlo and optimization

Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.

On stable uniqueness in linear semi-infinite optimization

This paper is intended to provide conditions for the stability of the strong uniqueness of the optimal solution of a given linear semi-infinite optimization (LSIO) problem, in the sense of maintaining the strong uniqueness property under sufficiently small perturbations of all the data. We consider LSIO problems such that the family of gradients of all the constraints is unbounded, extending earlier results of Nürnberger for continuous LSIO problems, and of Helbig and Todorov for LSIO problems with bounded set of gradients. To do this we characterize the absolutely (affinely) stable problems, i.e., those LSIO problems whose feasible set (its affine hull, respectively) remains constant under sufficiently small perturbations.

Primal Attainment in Convex Infinite Optimization Duality

This article provides results guarateeing that the optimal value of a given convex infinite optimization problem and its corresponding surrogate Lagrangian dual coincide and the primal optimal value is attainable. The conditions ensuring converse strong Lagrangian (in short, minsup) duality involve the weakly-inf-(locally) compactness of suitable functions and the linearity or relative closedness of some sets depending on the data. Applications are given to different areas of convex optimization, including an extension of the Clark-Duffin Theorem for ordinary convex programs.

Multiple criteria optimization of contemporary logistics distribution network problems

In the contemporary customer-driven supply chain, maximization of customer service plays an equally important role as minimization of costs for a company to retain and increase its competitiveness. This article develops a multiple-criteria optimization approach, combining the analytic hierarchy process (AHP) and an integer linear programming (ILP) model, to aid the design of an optimal logistics distribution network. The proposed approach outperforms traditional cost-based optimization techniques because it considers both quantitative and qualitative factors and also aims at maximizing the benefits of deliverer and customers. In the approach, the AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to some critical customer-oriented criteria. The results of AHP prioritization are utilized as the input of the ILP model, the objective of which is to select the best warehouses at the lowest possible cost. In this article, two commercial packages are used: including Expert Choice and LINDO.

Efficient Control in Multistage Stochastic Optimization Problem

AMS subject classification: 90C31, 90A09, 49K15, 49L20.

Resource management and optimization in wireless mesh networks

A wireless mesh network is a mesh network implemented over a wireless network system such as wireless LANs. Wireless Mesh Networks(WMNs) are promising for numerous applications such as broadband home networking, enterprise networking, transportation systems, health and medical systems, security surveillance systems, etc. Therefore, it has received considerable attention from both industrial and academic researchers. This dissertation explores schemes for resource management and optimization in WMNs by means of network routing and network coding.^ In this dissertation, we propose three optimization schemes. (1) First, a triple-tier optimization scheme is proposed for load balancing objective. The first tier mechanism achieves long-term routing optimization, and the second tier mechanism, using the optimization results obtained from the first tier mechanism, performs the short-term adaptation to deal with the impact of dynamic channel conditions. A greedy sub-channel allocation algorithm is developed as the third tier optimization scheme to further reduce the congestion level in the network. We conduct thorough theoretical analysis to show the correctness of our design and give the properties of our scheme. (2) Then, a Relay-Aided Network Coding scheme called RANC is proposed to improve the performance gain of network coding by exploiting the physical layer multi-rate capability in WMNs. We conduct rigorous analysis to find the design principles and study the tradeoff in the performance gain of RANC. Based on the analytical results, we provide a practical solution by decomposing the original design problem into two sub-problems, flow partition problem and scheduling problem. (3) Lastly, a joint optimization scheme of the routing in the network layer and network coding-aware scheduling in the MAC layer is introduced. We formulate the network optimization problem and exploit the structure of the problem via dual decomposition. We find that the original problem is composed of two problems, routing problem in the network layer and scheduling problem in the MAC layer. These two sub-problems are coupled through the link capacities. We solve the routing problem by two different adaptive routing algorithms. We then provide a distributed coding-aware scheduling algorithm. According to corresponding experiment results, the proposed schemes can significantly improve network performance.^

A diagnostic framework for demand amplification problems in supply chains

This dissertation delivers a framework to diagnose the Bull-Whip Effect (BWE) in supply chains and then identify methods to minimize it. Such a framework is needed because in spite of the significant amount of literature discussing the bull-whip effect, many companies continue to experience the wide variations in demand that are indicative of the bull-whip effect. While the theory and knowledge of the bull-whip effect is well established, there still is the lack of an engineering framework and method to systematically identify the problem, diagnose its causes, and identify remedies. ^ The present work seeks to fill this gap by providing a holistic, systems perspective to bull-whip identification and diagnosis. The framework employs the SCOR reference model to examine the supply chain processes with a baseline measure of demand amplification. Then, research of the supply chain structural and behavioral features is conducted by means of the system dynamics modeling method. ^ The contribution of the diagnostic framework, is called Demand Amplification Protocol (DAMP), relies not only on the improvement of existent methods but also contributes with original developments introduced to accomplish successful diagnosis. DAMP contributes a comprehensive methodology that captures the dynamic complexities of supply chain processes. The method also contributes a BWE measurement method that is suitable for actual supply chains because of its low data requirements, and introduces a BWE scorecard for relating established causes to a central BWE metric. In addition, the dissertation makes a methodological contribution to the analysis of system dynamic models with a technique for statistical screening called SS-Opt, which determines the inputs with the greatest impact on the bull-whip effect by means of perturbation analysis and subsequent multivariate optimization. The dissertation describes the implementation of the DAMP framework in an actual case study that exposes the approach, analysis, results and conclusions. The case study suggests a balanced solution between costs and demand amplification can better serve both firms and supply chain interests. Insights pinpoint to supplier network redesign, postponement in manufacturing operations and collaborative forecasting agreements with main distributors.^

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Optimization models and methods for real-time transportation planning in forestry

Lors du transport du bois de la forêt vers les usines, de nombreux événements imprévus peuvent se produire, événements qui perturbent les trajets prévus (par exemple, en raison des conditions météo, des feux de forêt, de la présence de nouveaux chargements, etc.). Lorsque de tels événements ne sont connus que durant un trajet, le camion qui accomplit ce trajet doit être détourné vers un chemin alternatif. En l’absence d’informations sur un tel chemin, le chauffeur du camion est susceptible de choisir un chemin alternatif inutilement long ou pire, qui est lui-même "fermé" suite à un événement imprévu. Il est donc essentiel de fournir aux chauffeurs des informations en temps réel, en particulier des suggestions de chemins alternatifs lorsqu’une route prévue s’avère impraticable. Les possibilités de recours en cas d’imprévus dépendent des caractéristiques de la chaîne logistique étudiée comme la présence de camions auto-chargeurs et la politique de gestion du transport. Nous présentons trois articles traitant de contextes d’application différents ainsi que des modèles et des méthodes de résolution adaptés à chacun des contextes. Dans le premier article, les chauffeurs de camion disposent de l’ensemble du plan hebdomadaire de la semaine en cours. Dans ce contexte, tous les efforts doivent être faits pour minimiser les changements apportés au plan initial. Bien que la flotte de camions soit homogène, il y a un ordre de priorité des chauffeurs. Les plus prioritaires obtiennent les volumes de travail les plus importants. Minimiser les changements dans leurs plans est également une priorité. Étant donné que les conséquences des événements imprévus sur le plan de transport sont essentiellement des annulations et/ou des retards de certains voyages, l’approche proposée traite d’abord l’annulation et le retard d’un seul voyage, puis elle est généralisée pour traiter des événements plus complexes. Dans cette ap- proche, nous essayons de re-planifier les voyages impactés durant la même semaine de telle sorte qu’une chargeuse soit libre au moment de l’arrivée du camion à la fois au site forestier et à l’usine. De cette façon, les voyages des autres camions ne seront pas mo- difiés. Cette approche fournit aux répartiteurs des plans alternatifs en quelques secondes. De meilleures solutions pourraient être obtenues si le répartiteur était autorisé à apporter plus de modifications au plan initial. Dans le second article, nous considérons un contexte où un seul voyage à la fois est communiqué aux chauffeurs. Le répartiteur attend jusqu’à ce que le chauffeur termine son voyage avant de lui révéler le prochain voyage. Ce contexte est plus souple et offre plus de possibilités de recours en cas d’imprévus. En plus, le problème hebdomadaire peut être divisé en des problèmes quotidiens, puisque la demande est quotidienne et les usines sont ouvertes pendant des périodes limitées durant la journée. Nous utilisons un modèle de programmation mathématique basé sur un réseau espace-temps pour réagir aux perturbations. Bien que ces dernières puissent avoir des effets différents sur le plan de transport initial, une caractéristique clé du modèle proposé est qu’il reste valable pour traiter tous les imprévus, quelle que soit leur nature. En effet, l’impact de ces événements est capturé dans le réseau espace-temps et dans les paramètres d’entrée plutôt que dans le modèle lui-même. Le modèle est résolu pour la journée en cours chaque fois qu’un événement imprévu est révélé. Dans le dernier article, la flotte de camions est hétérogène, comprenant des camions avec des chargeuses à bord. La configuration des routes de ces camions est différente de celle des camions réguliers, car ils ne doivent pas être synchronisés avec les chargeuses. Nous utilisons un modèle mathématique où les colonnes peuvent être facilement et naturellement interprétées comme des itinéraires de camions. Nous résolvons ce modèle en utilisant la génération de colonnes. Dans un premier temps, nous relaxons l’intégralité des variables de décision et nous considérons seulement un sous-ensemble des itinéraires réalisables. Les itinéraires avec un potentiel d’amélioration de la solution courante sont ajoutés au modèle de manière itérative. Un réseau espace-temps est utilisé à la fois pour représenter les impacts des événements imprévus et pour générer ces itinéraires. La solution obtenue est généralement fractionnaire et un algorithme de branch-and-price est utilisé pour trouver des solutions entières. Plusieurs scénarios de perturbation ont été développés pour tester l’approche proposée sur des études de cas provenant de l’industrie forestière canadienne et les résultats numériques sont présentés pour les trois contextes.

Problems in Algebraic Vision

Thesis (Ph.D.)--University of Washington, 2016-08