This work develops theoretical and applied results for variational convex analysis. First we present the basic tools of analysis necessary to develop the core theory and applications. New results concerning duality principles for systems originally modeled by non-linear differential equations are shown in chapters 9 to 17. A key aspect of this work is that although the original problems are non-linear with corresponding non-convex variational formulations, the dual formulations obtained are almost always concave and amenable to numerical computations. When the primal problem has no solution in the classical sense, the solution of dual problem is a weak limit of minimizing sequences, and the evaluation of such average behavior is important in many practical applications. Among the results we highlight the dual formulations for micro-magnetism, phase transition models, composites in elasticity and conductivity and others. To summarize, in the present work we introduce convex analysis as an interesting alternative approach for the understanding and computation of some important problems in the modern calculus of variations. / Ph. D.

The need to design a controller that self-schedules itself during the flight of an aircraft has been an active area of research. New methods have been developed beyond the traditional gain-scheduling approach. One such design method leads to a linear parameter varying (LPV) controller that changes based on the real-time variation of system dynamics. Before such a controller can be designed, the system has to also be represented as an LPV system. The current effort proposes a LPV modeling technique that is inspired by an affine LPV modeling techniques found in recent research. The properties of the proposed modeling method are investigated and compared to the affine modeling technique. It is shown that the proposed modeling technique represents the actual system behavior more closely than the existing affine modeling technique. To study the effect of the two LPV modeling techniques on controller design, a linear quadratic regulator (LQR) controller using linear matrix inequality (LMI) formulation is designed. This control design method provides a measure of conservatism that is used to compare the controllers based on the different modeling techniques. An F-16 short-period model is used to implement the modeling techniques and design the controllers. It was found that the controller based on the proposed LPV modeling method is less conservative than the controller based on the existing LPV method. Interesting features of LMI formulation for multiple plant models were also discovered during the exercise. A stability robustness analysis was also conducted as an additional comparison of the performance of the controllers designed using the two modeling methods. A scalar measure, called the probability of instability, is used as a measure of robustness. It was found that the controller based on the proposed modeling technique has the necessary robustness properties even though it is less conservative than the controller designed based on the existing modeling approach. / Master of Science

This research is designed to develop and investigate newly defined problems: the Maximum Convex Sum (MCS), and its generalisation, the K-Maximum Convex Sum (K-MCS), in a two-dimensional (2D) array based on dynamic programming. The study centres on the concept of finding the most useful informative array portion as defined by different parameters involved in data, which is generically expressed in this thesis as the Maximum Sum Problem (MSP). This concept originates in the Maximum Sub-Array (MSA) problem, which relies on rectangular regions to find the informative array portion. From the above it follows that MSA and MCS belong to MSP. This research takes a new stand in using an alternative shape in the MSP context, which is the convex shape. Since 1977, there has been substantial research in the development of the Maximum Sub-Array (MSA) problem to find informative sub-array portions, running in the best possible time complexity. Conventionally the research norm has been to use the rectangular shape in the MSA framework without any investigation into an alternative shape for the MSP. Theoretically there are shapes that can improve the MSP outcome and their utility in applications; research has rarely discussed this. To advocate the use of a different shape in the MSP context requires rigorous investigation and also the creation of a platform to launch a new exploratory research area. This can then be developed further by considering the implications and practicality of the new approach. This thesis strives to open up a new research frontier based on using the convex shape in the MSP context. This research defines the new MCS problem in 2D; develops and evaluates algorithms that serve the MCS problem running in the best possible time complexity; incorporates techniques to advance the MCS algorithms; generalises the MCS problem to cover the K-Disjoint Maximum Convex Sums (K-DMCS) problem and the K-Overlapping Maximum Convex Sums (K-OMCS) problem; and eventually implements the MCS algorithmic framework using real data in an ecology application. Thus, this thesis provides a theoretical and practical framework that scientifically contributes to addressing some of the research gaps in the MSP and the new research path: the MCS problem. The MCS and K-MCS algorithmic models depart from using the rectangular shape as in MSA, and retain a time complexity that is within the best known time complexities of the MSA algorithms. Future in-depth studies on the Maximum Convex Sum (MCS) problem can advance the algorithms developed in this thesis and their time complexity.

We study the effects of a deformation via the heat equation on closed, plane curves. We begin with an overview of the theory of curves in R3. In particular, we develop the Frenet-Serret equations for any curve parametrized by arc length. This chapter is followed by an examination of curves in R2, and the resultant adjustment of the Frenet-Serret equations. We then prove the rotation index for closed, plane curves is an integer and for simple, closed, plane curves is ±1. We show that a curve is convex if and only if the curvature does not change sign, and we prove the Isoperimetric Inequality, which gives a bound on the area of a closed curve with fixed length. Finally, we study the deformation of plane curves developed by M. Gage and R. S. Hamilton. We observe that convex curves under deformation remain convex, and simple curves remain simple.

This paper contains four main ideas. First, it shows global existence for the steepest descent in the uniformly convex setting. Secondly, it shows existence of critical points for convex functions defined on uniformly convex spaces. Thirdly, it shows an isomorphism between the dual space of H^{1,p}[0,1] and the space H^{1,q}[0,1] where p > 2 and {1/p} + {1/q} = 1. Fourthly, it shows how the Beurling-Denny theorem can be extended to find a useful function from H^{1,p}[0,1] to L_{p}[1,0] where p > 2 and addresses the problem of using that function to establish a relationship between the ordinary and the Sobolev gradients. The paper contains some numerical experiments and two computer codes.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2014-10-08T19:52:23Z No. of bitstreams: 2 Dissertação - Ronan Gomes de Amorim - 2013.pdf: 1551424 bytes, checksum: 2acf9af7fdc161d745d9a1fcf58ba4b0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-10-09T11:22:45Z (GMT) No. of bitstreams: 2 Dissertação - Ronan Gomes de Amorim - 2013.pdf: 1551424 bytes, checksum: 2acf9af7fdc161d745d9a1fcf58ba4b0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-10-09T11:22:45Z (GMT). No. of bitstreams: 2 Dissertação - Ronan Gomes de Amorim - 2013.pdf: 1551424 bytes, checksum: 2acf9af7fdc161d745d9a1fcf58ba4b0 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2013-03-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper presents the main ideas concerning convex sets and functions. Our aim is to deal, didactically, with the main topics concerning convexity, as well as the consequent exploitation of the envolved mathematical concepts. In this sense, we have made a bibliographic revision approaching important theorems, lemmas, corollaries and propositions designed both to first readers and to those who want to work with applications arising from convexity. We hope that this study may constitute an important research source either for students, teachers or researchers who wish to learn more about convex sets. / Neste trabalho, apresentamos as principais ideias concernentes aos conjuntos convexos e às funções convexas. Nosso principal foco é tratar, de forma didática, os principais tópicos envolvidos na convexidade, bem como a consequente exploração dos conceitos matemáticos envolvidos. Nesse sentido, realizamos uma revisão bibliográfica que contemplou teoremas, lemas, corolários e proposições relevantes a um primeiro leitor e a todos que pretendem trabalhar com as aplicações decorrentes da convexidade. Assim, esperamos que este material constitua uma importante fonte de pesquisa a estudantes, professores e pesquisadores que almejem estudar conteúdos relacionados aos conjuntos convexos.

In this thesis we study Mahler's conjecture in convex geometry, give a short summary about its history, gather and explain different approaches that have been used to attack the conjecture, deduce formulas to calculate the Mahler volume and perform numerical analysis on it. The conjecture states that the Mahler volume of any symmetric convex body, i.e. the product of the volume of the symmetric convex body and the volume of its dual body, is minimized by the (hyper-)cube. The conjecture was stated and solved in 1938 for the 2-dimensional case by Kurt Mahler. While the maximizer for this problem is known (it is the ball), the conjecture about the minimizer is still open for all dimensions greater than 2. A lot of effort has benn made to solve this conjecture, and many different ways to attack the conjecture, from simple geometric attempts to ones using sophisticated results from functional analysis, have all been tried unsuccesfully. We will present and discuss the most important approaches. Given the support function of the body, we will then introduce several formulas for the volume of the dual and the original body and hence for the Mahler volume. These formulas are tested for their effectiveness and used to perform numerical work on the conjecture. We examine the conjectured minimizers of the Mahler volume by approximating them in different ways. First the spherical harmonic expansion of their support functions is calculated and then the bodies are analyzed with respect to the length of that expansion. Afterwards the cube is further examined by approximating its principal radii of curvature functions, which involve Dirac delta functions.

Our work applies techniques for blending and tightening solid shapes represented by sets. We require that the output contain one set and exclude a second set, and then we optimize the boundary separating the two sets. Working within that framework, we present mason, tightening, tight hulls, tight blends, and the medial cover, with details for implementation. Mason uses opening and closing techniques from mathematical morphology to smooth small features. By contrast, tightening uses mean curvature flow to minimize the measure of the boundary separating the opening of the interior of the closed input set from the opening of its complement, guaranteeing a mean curvature bound. The tight hull offers a significant generalization of the convex hull subject to volumetric constraints, introducing developable boundary patches connecting the constraints. Tight blends then use opening to replicate some of the behaviors from tightenings by applying tight hulls. The medial cover provides a means for adjusting the topology of a tight hull or tight blend, and it provides an implementation technique for two-dimensional polygonal inputs. Collectively, we offer applications for boundary estimation, three-dimensional solid design, blending, normal field simplification, and polygonal repair. We consequently establish the value of blending and tightening as tools for solid modeling.

In this thesis we investigate the design and complexity analysis of the algorithms to solve convex programming problems under inexact first-order information. In the first part of this thesis we focus on the general non-smooth convex minimization under a stochastic oracle. We start by introducing an important algorithmic advancement in this area, namely, the development of the mirror descent stochastic approximation algorithm. The main contribution is to develop a validation procedure for this algorithm applied to stochastic programming. In the second part of this thesis we consider the Stochastic Composite Optimizaiton (SCO) which covers smooth, non-smooth and stochastic convex optimization as certain special cases. Note that the optimization algorithms that can achieve this lower bound had never been developed. Our contribution in this topic mainly consists of the following aspects. Firstly, with a novel analysis, it is demonstrated that the simple RM-SA algorithm applied to the aforementioned problems exhibits the best known so far rate of convergence. Moreover, by adapting Nesterov's optimal method, we propose an accelerated SA, which can achieve, uniformly in dimension, the theoretically optimal rate of convergence for solving this class of problems. Finally, the significant advantages of the accelerated SA over the existing algorithms are illustrated in the context of solving a class of stochastic programming problems. In the last part of this work, we extend our attention to certain deterministic optimization techniques which operate on approximate first-order information for the dual problem. In particular, we establish, for the first time in the literature, the iteration-complexity for the inexact augmented Lagrangian (I-AL) methods applied to a special class of convex programming problems.

abstract: This research mainly focuses on improving the utilization of photovoltaic (PV) re-sources in distribution systems by reducing their variability and uncertainty through the integration of distributed energy storage (DES) devices, like batteries, and smart PV in-verters. The adopted theoretical tools include statistical analysis and convex optimization. Operational issues have been widely reported in distribution systems as the penetration of PV resources has increased. Decision-making processes for determining the optimal allo-cation and scheduling of DES, and the optimal placement of smart PV inverters are con-sidered. The alternating current (AC) power flow constraints are used in these optimiza-tion models. The first two optimization problems are formulated as quadratically-constrained quadratic programming (QCQP) problems while the third problem is formu-lated as a mixed-integer QCQP (MIQCQP) problem. In order to obtain a globally opti-mum solution to these non-convex optimization problems, convex relaxation techniques are introduced. Considering that the costs of the DES are still very high, a procedure for DES sizing based on OpenDSS is proposed in this research to avoid over-sizing. Some existing convex relaxations, e.g. the second order cone programming (SOCP) relaxation and semidefinite programming (SDP) relaxation, which have been well studied for the optimal power flow (OPF) problem work unsatisfactorily for the DES and smart inverter optimization problems. Several convex constraints that can approximate the rank-1 constraint X = xxT are introduced to construct a tighter SDP relaxation which is referred to as the enhanced SDP (ESDP) relaxation using a non-iterative computing framework. Obtaining the convex hull of the AC power flow equations is beneficial for mitigating the non-convexity of the decision-making processes in power systems, since the AC power flow constraints exist in many of these problems. The quasi-convex hull of the quadratic equalities in the AC power bus injection model (BIM) and the exact convex hull of the quadratic equality in the AC power branch flow model (BFM) are proposed respectively in this thesis. Based on the convex hull of BFM, a novel convex relaxation of the DES optimizations is proposed. The proposed approaches are tested on a real world feeder in Arizona and several benchmark IEEE radial feeders. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016