Indiana University-Purdue University Indianapolis (IUPUI) / The main objective of this work is to better quantify lunar surface minerals (agglutinate, clinopyroxene, orthopyroxene, plagioclase, olivine, ilmenite, and volcanic glass), particle sizes and the abundance of SMFe from the lunar soil characterization consortium (LSCC) dataset with our improved model based on Hapke's radiative transfer theory. The model is implemented for both forward and inverse modeling. Hapke's radiative transfer theory is implemented in the inverse model means Newton's method and least squares are jointly used to solve nonlinear questions rather than commonly used look-up Table (LUT). Although the effects of temperature and surface topography are incorporated into the implementation to improve the model performance for application of lunar spacecraft data, these effects cannot be extensively addressed in the current work because of the use of lab measured reflectance data. Our forward radiative transfer model (RTM) results show that the correlation coefficients between modeled and measured spectra are over 0.99. For the inverse model, the distribution of the calculated particle sizes is all within their measured range. The range of modeled SMFe for highland samples is 0.01% - 0.5 % and for mare samples is 0.03% - 1 %. The linear trend between SMFe and ferromagnetic resonance (Is) for all the LSCC samples is consistent with laboratory measurements. For quantifying lunar mineral abundances, the results show that the R-squared for the training samples (Is/FeO <= 65) are over 0.65 with plagioclase having highest correlation (0.94) and pyroxene the lowest (0.68). In the future work, the model needs to be improved for handling more mature lunar soil samples.

In this age, there are many applications of inverse problems to lots of areas ranging from astronomy, geoscience and so on. For example, image reconstruction and deblurring require the use of methods to solve inverse problems. Since the problems are subject to many factors and noise, we can't simply apply general inversion methods. Furthermore in the problems of interest, the number of unknown variables is huge, and some may depend nonlinearly on the data, such that we must solve nonlinear problems. It is quite different and significantly more challenging to solve nonlinear problems than linear inverse problems, and we need to use more sophisticated methods to solve these kinds of problems. / Master of Science / In various research areas, there are many required measurements which can't be observed due to physical and economical reasons. Instead, these unknown measurements can be recovered by known measurements. This phenomenon can be modeled and be solved by mathematics.

Semi-infinite programming problems is a class of optimization problems in finite dimensional variables which are subject to infinitely many inequality constraints. If the infinite index of inequality constraints depends on the decision variable, then the problem is called generalized semi-infinite programming problem (GSIP). If the infinite index set is fixed, then the problem is called standard semi-infinite programming problem (SIP). In this thesis, convergence of a semismooth Newton method for generalized semi-infinite programming problems with convex lower level problems is investigated. In this method, using nonlinear complementarity problem functions the upper and lower level Karush-Kuhn-Tucker conditions of the optimization problem are reformulated as a semismooth system of equations. A possible violation of strict complementary slackness causes nonsmoothness. In this study, we show that the standard regularity condition for convergence of the semismooth Newton method is satisfied under natural assumptions for semi-infinite programs. In fact, under the Reduction Ansatz in the lower level problem and strong stability in the reduced upper level problem this regularity condition is satisfied. In particular, we do not have to assume strict complementary slackness in the upper level. Furthermore, in this thesis we neither assume strict complementary slackness in the upper nor in the lower level. In the case of violation of strict complementary slackness in the lower level, the auxiliary functions of the locally reduced problem are not necessarily twice continuously differentiable. But still, we can show that a standard regularity condition for quadratic convergence of the semismooth Newton method holds under a natural assumption for semi-infinite programs. Numerical examples from, among others, design centering and robust optimization illustrate the performance of the method.

Šiame darbe buvo analizuojama Niutono fraktalų Žulija aibės. Dažniausiai Žulija ir užpildytų Žulija aibių vaizdai gaunami, panaudojant "pabėgimo laiko" algoritmą. Norėdami šį algoritmą naudoti kompleksinio daugianario šaknų vizualizacijai, turime nurodyti iteracijų skaiči��, algoritmo tikslumą, žingsnį bei kompleksiniu Niutono metodu rasti daugianario šaknis. Taikant Niutono metodą, buvo susidurta su pradinių taškų parinkimo problema. Tyrimo metu patvirtinta, kad pakanka Niutono iteracinę funkciją taikyti taškams z, kurių modulis 2. Darbe buvo pasiūlytas šaknų lokalizacijos srities nustatymo būdas. Naudojant PL-algoritmą, pasirinktu žingsniu pereiname visus taškus, kurie patenka į šią sritį. Taip gauname Niutono-Rafsono fraktalus ir lygiagrečiai analizuojame Žulija aibes bei užpildytas Žulija aibes. / Julia sets and filled Julia sets of Newton‘s fractals are analyzed in this work. The Escape Time Algorithm provides us with a means for "seeing" the filled Julia sets of Newton‘s fractals, but roots, (zeros) of the polynomial under investigation should be known. The Newton‘s method for finding roots of an algebraic equation is well known. Here in the paper the complex Newton method for finding roots of a complex polynomial is presented. The main difficulties, associated with implementation of this method in practice, are discussed, namely: construction of the set of initial points (first approximations of the roots), finding the basin of attraction for a particular root and so forth. Some experimental results are presented.

This thesis addresses optimal control problems with elasticity for large deformations. A hyperelastic model with a polyconvex energy density is employed to describe the elastic behavior of a body. The two approaches to derive the nonlinear partial differential equation, a balance of forces and an energy minimization, are compared. Besides the conventional volume and boundary loads, two novel internal loads are presented. Furthermore, curvilinear coordinates and a hierarchical plate model can be incorporated into the formulation of the elastic forward problem. The forward problem can be solved with Newton\\\'s method, though a globalization technique should be used to avoid divergence of Newton\\\'s method. The repeated solution of the Newton system is done by a CG or MinRes method with a multigrid V-cycle as a preconditioner. The optimal control problem consists of the displacement (as the state) and a load (as the control). Besides the standard tracking-type objective, alternative objective functionals are presented for problems where a reasonable desired state cannot be provided. Two methods are proposed to solve the optimal control problem: an all-at-once approach by a Lagrange-Newton method and a reduced formulation by a quasi-Newton method with an inverse limited-memory BFGS update. The algorithms for the solution of the forward problem and the optimal control problem are implemented in the finite-element software FEniCS, with the geometrical multigrid extension FMG. Numerical experiments are performed to demonstrate the mesh independence of the algorithms and both optimization methods.

Partial differential equations are the chief means of providing mathematical models in science, engineering and other fields. Optimal control of partial differential equations (PDEs) has tremendous applications in engineering and science, such as shape optimization, image processing, fluid dynamics, and chemical processes. In this thesis, we develop and analyze several efficient numerical methods for the optimal control problems governed by elliptic PDE, parabolic PDE, and wave PDE, respectively. The thesis consists of six chapters. In Chapter 1, we briefly introduce a few motivating applications and summarize some theoretical and computational foundations of our following developed approaches. In Chapter 2, we establish a new multigrid algorithm to accelerate the semi-smooth Newton method that is applied to the first-order necessary optimality system arising from semi-linear control-constrained elliptic optimal control problems. Under suitable assumptions, the discretized Jacobian matrix is proved to have a uniformly bounded inverse with respect to mesh size. Different from current available approaches, a new strategy that leads to a robust multigrid solver is employed to define the coarse grid operator. Numerical simulations are provided to illustrate the efficiency of the proposed method, which shows to be computationally more efficient than the popular full approximation storage (FAS) multigrid method. In particular, our proposed approach achieves a mesh-independent convergence and its performance is highly robust with respect to the regularization parameter. In Chaper 3, we present a new second-order leapfrog finite difference scheme in time for solving the first-order necessary optimality system of the linear parabolic optimal control problems. The new leapfrog scheme is shown to be unconditionally stable and it provides a second-order accuracy, while the classical leapfrog scheme usually is well-known to be unstable. A mathematical proof for the convergence of the proposed scheme is provided under a suitable norm. Moreover, the proposed leapfrog scheme gives a favorable structure that leads to an effective implementation of a fast solver under the multigrid framework. Numerical examples show that the proposed scheme significantly outperforms the widely used second-order backward time differentiation approach, and the resultant fast solver demonstrates a mesh-independent convergence as well as a linear time complexity. In Chapter 4, we develop a new semi-smooth Newton multigrid algorithm for solving the discretized first-order necessary optimality system that characterizes the optimal solution of semi-linear parabolic PDE optimal control problems with control constraints. A new leapfrog discretization scheme in time associated with the standard five-point stencil in space is established to achieve a second-order accuracy. The convergence (or unconditional stability) of the proposed scheme is proved when time-periodic solutions are considered. Moreover, the derived well-structured discretized Jacobian matrices greatly facilitate the development of an effective smoother in our multigrid algorithm. Numerical simulations are provided to illustrate the effectiveness of the proposed method, which validates the second-order accuracy in solution approximations as well as the optimal linear complexity of computing time. In Chapter 5, we offer a new implicit finite difference scheme in time for solving the first-order necessary optimality system arising in optimal control of wave equations. With a five-point central finite difference scheme in space, the full discretization is proved to be unconditionally convergent with a second-order accuracy, which is not restricted by the classical Courant-Friedrichs-Lewy (CFL) stability condition on the spatial and temporal step sizes. Moreover, based on its advantageous developed structure, an efficient preconditioned Krylov subspace method is provided and analyzed for solving the discretized sparse linear system. Numerical examples are presented to confirm our theoretical conclusions and demonstrate the promising performance of proposed preconditioned iterative solver. Finally, brief summaries and future research perspectives are given in Chapter 6.

Time Difference Of Arrival (TDOA) based position tracking technique is one of the pinnacles of sports tracking technology. Using radio frequency com-munication, advanced filtering techniques and various computation methods, the position of a moving player in a virtually created sports arena can be iden-tified using MATLAB. It can also be related to player’s movement in real-time. For football in particular, this acts as a powerful tool for coaches to enhanceteam performance. Football clubs can use the player tracking data to boosttheir own team strengths and gain insight into their competing teams as well. This method helps to improve the success rate of Athletes and clubs by analyz-ing the results, which helps in crafting their tactical and strategic approach to game play. The algorithm can also be used to enhance the viewing experienceof audience in the stadium, as well as broadcast.In this thesis work, a typical football field scenario is assumed and an arrayof base stations (BS) are installed along perimeter of the field equidistantly.The player is attached with a radio transmitter which emits radio frequencythroughout the assigned game time. Using the concept of TDOA, the position estimates of the player are generated and the transmitter is tracked contin-uously by the BS. The position estimates are then fed to the Kalman filter, which filters and smoothens the position estimates of the player between the sample points considered. Different paths of the player as straight line, circu-lar, zig-zag paths in the field are animated and the positions of the player are tracked. Based on the error rate of the player’s estimated position, the perfor-mance of the Kalman filter is evaluated. The Kalman filter’s performance is analyzed by varying the number of sample points.

This paper studies Newton's method for solving the algebraic Riccati equation combined with an exact line search. Based on these considerations we present a Newton{like method for solving algebraic Riccati equations. This method can improve the sometimes erratic convergence behavior of Newton's method.

Orientadores: Paulo Sollero, Eder Lima de Albuquerque / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-28T12:08:28Z (GMT). No. of bitstreams: 1 Shaterzadeh-Yazdi_MohammadHossein_M.pdf: 5411191 bytes, checksum: 83da697ff892a31af99059f3e88bd338 (MD5) Previous issue date: 2015 / Resumo: Em problemas de contato mecânico entre dois corpos elásticos, o cálculo de tensões e deformações dos componentes é de grande importância. Em casos particulares os corpos estão sujeitos a cargas normal e tangencial na presença de atrito, o qual aumenta a complexidade do problema. O estudo do fenômeno e a modelagem do problema, empregando o método dos elementos de contorno (MEC), é apresentado neste trabalho. Devido à presença de atrito e restrições de contato, esse problema torna-se um caso não linear. A não linearidade do problema foi contornada com a aplicação incremental de carga e o uso de um método de resolução de sistemas não lineares. A zona de contato é uma das variáveis do problema e pode conter estados de adesão e escorregamento, simultaneamente. Esses estados dependem dos esforços normais e tangenciais no componente e podem variar durante o processo de aplicação de carga. Dessa forma, cada incremento de carga pode perturbar em relação ao estado anterior. Portanto, o cálculo de variáveis e a atualização do sistema de equações em cada iteração é indispensável. Por este motivo, um algoritmo robusto para definição dos estados de contato é proposto. Como o sistema de equações obtido é não linear, o uso de um método numérico adequado é exigido. Para a solução deste sistema, o método de Newton foi aplicado, o qual permite a verificação do estado de contato em cada incremento. A análise é feita com o uso de elementos quadráticos contínuos, apresentando resultados contínuos e sem oscilação. A comparação dos resultados com as soluções analíticas de Hertz e Mindlin-Cattaneo mostram boa concordância / Abstract: The computation of stresses and strains on the components is of great importance, when the contact mechanics problems between two elastic bodies are analyzed. In particular cases, bodies are subjected to normal and shear loading in the presence of friction, which increases the complexity of the problem. The study of the phenomenon and modeling of the problem, using the boundary element method (BEM), are presented in this work. Due to the presence of friction and natural restrictions, this problem becomes non-linear. The non-linearity of the problem was solved with an incremental applied load and with the use of solvers to non linear systems. The contact zone can contain stick and slip states, simultaneously. These states are dependent on the normal and shear forces on the component and can vary during the application load process. Thus, each load increment can violate the previous state and therefore, the evaluation of variables and the updating of the system of equations after each iteration is indispensable For this reason, a robust algorithm for contact state definition is suggested. Since a non linear system of equations is obtained, an appropriate numerical method is required. To solve this system, Newton¿s method is applied, which allows the verification of the state of contact at each increment. The analysis is done with the use of quadratic continuous elements and provides continuous and non-oscillatory results. Comparisons of the results with the analytical solutions of Hertz and Mindlin-Cattaneo show good agreement / Mestrado / Mecanica dos Sólidos e Projeto Mecanico / Mestre em Engenharia Mecânica

Neste trabalho propomos uma abordagem que utiliza o método de barreira modificada/penalidade para a resolução de problemas restritos gerais de otimização. Para isso, foram obtidos dados teóricos, a partir de um levantamento bibliográfico, que explicitaram os métodos primal-dual barreira logarítmica e método de barreira modificada. Nesta abordagem, as restrições de desigualdade canalizadas são tratadas pela função barreira de Frisch modificada, ou por uma extrapolação quadrática e as restrições de igualdade do problema através da função Lagrangiana. A implementação consiste num duplo estágio de aproximação: um ciclo externo, onde o problema restrito é convertido em um problema irrestrito, usando a função Lagrangiana barreira modificada/penalidade; e um ciclo interno, onde o método de Newton é utilizado para a atualização das variáveis primais e duais. É apresentada também uma função barreira clássica extrapolada para a inicialização dos multiplicadores de Lagrange. A eficiência do método foi verificada utilizando um problema teste e em problemas de fluxo de potência ótimo (FPO). / In this paper, we propose an approach that utilizes the penalty/modified barrier method to solve the general constrained problems. On this purpose, theoretical data were obtained, from a bibliographical review, which enlightened the logarithmic barrier primal-dual method and modified barrier method. In this approach, the bound constraints are handled by the modified log-barrier function, or by quadratic extrapolation and the equality constraints of the problem through Lagrangian function. The method, as implemented, consists of a two-stage approach: an outer cycle, where the constrained problem is transformed into unconstrained problem, using penalty/modified barrier Lagrangian function; and an inner cycle, where the Newton\'s method is used for update the primal and dual variables. Also, it is presented a classical barrier extrapolated function for initialization of Lagrange multipliers. The effectiveness of the proposed approach has been examined by solving a test problem and optimal power flow problems (OPF).