As groundwater models are being used increasingly in the area of resource allocation, there has been an increase in the level of complexity in an attempt to capture heterogeneity, complex geometries and detail in interaction between the model domain and the outside hydraulic influences. As models strive to represent the real world in ever increasing detail, there is a strong likelihood that the boundary conditions will become nonlinear. Nonlinearities exist in the groundwater flow equation even in simple models when watertable (unconfined) conditions are simulated. This thesis is concerned with how these nonlinearities are treated numerically, with particular focus on the MODFLOW groundwater flow software and the nonlinear nature of the unconfined condition simulation. One of the limitations of MODFLOW is that it employs a first order fixed point iterative scheme to linearise the nonlinear system that arises as a result of the finite difference discretisation process, which is well known to offer slow convergence rates for highly nonlinear problems. However, Newton's method can achieve quadratic convergence and is more effective at dealing with higher levels of nonlinearity. Consequently, the main objective of this research is to investigate the inclusion of Newton's method to the suite of computational tools in MODFLOW to enhance its flexibility in dealing with the increasing complexity of real world problems, as well as providing a more competitive and efficient solution methodology. Furthermore, the underpinning linear iterative solvers that MODFLOW currently utilises are targeted at symmetric systems and a consequence of using Newton's method would be the requirement to solve non-symmetric Jacobian systems. Therefore, another important aspect of this work is to investigate linear iterative solution techniques that handle such systems, including the newer Krylov style solvers GMRES and BiCGSTAB. To achieve these objectives a number of simple benchmark problems involving nonlinearities through the simulation of unconfined conditions were established to compare the computational performance of the existing MODFLOW solvers to the new solution strategies investigated here. One of the highlights of these comparisons was that Newton's method when combined with an appropriately preconditioned Krylov solver was on average greater than 40% more CPU time efficient than the Picard based solution techniques. Furthermore, a significant amount of this time saving came from the reduction in the number of nonlinear iterations due to the quadratic nature of Newton's method. It was also found that Newton's method benefited more from improved initial conditions than Picard's method. Of all the linear iterative solvers tested, GMRES required the least amount of computational effort. While the Newton method involves more complexity in its implementation, this should not be interpreted as prohibitive in its application. The results here show that the extra work does result in performance increase, and thus the effort is certainly worth it.

No / An algorithm called VPAStab is given for the acceleration of convergence of a sequence of vectors. It combines a method of vector-Padé approximation with a successful technique for stabilisation. More generally, this algorithm is designed to find the fixed point of the generating function of the given sequence of vectors, analogously to the way in which ordinary Padé approximants can accelerate the convergence of a given scalar sequence. VPAStab is justified in the context of its application to the solution of a large sparse system of linear equations. The possible breakdowns of the algorithm are listed. Numerical experiments indicate that these breakdowns can be classified either as pivot-type (type L) or as ghost-type (type D).

No / The key equations of BiCGStab are summarised to show its connections with Pade and vector-Pade approximation. These considerations lead naturally to stabilised vector-Pade approximation of a vector-valued function (VPAStab), and an algorithm for the acceleration of convergence of a linearly generated sequence of vectors. A generalisation of this algorithm for the acceleration of convergence of a nonlinearly generated system is proposed here, and comparative numerical results are given.

No / The effects of the three principal possible exact breakdowns which may occur using BiCGStab are discussed. BiCGStab is used to solve large sparse linear systems of equations, such as arise from the discretisation of PDEs. These PDEs often involve a parameter, say . We investigate here how the numerical error grows as breakdown is approached by letting tend to a critical value, say c, at which the breakdown is numerically exact. We found empirically in our examples that loss of numerical accuracy due stabilisation breakdown and Lanczos breakdown was discontinuous with respect to variation of around c. By contrast, the loss of numerical accuracy near a critical value c for pivot breakdown is roughly proportional to |¿c|¿1.

NITSOL: A Newton Iterative Solver for Nonlinear Systems describes an algorithm for solving nonlinear systems. Michael Pernice and Homer F. Walker, the authors of the paper NITSOL [3], implemented this algorithm in FORTRAN. The goal of the project has been to use the modern and robust language MATLAB to implement the NITSOL algorithm. In this paper, the main mathematical and algorithmic background for understanding NITSOL are described, and a user guide is included outlining how to use the MATLAB implementation of NITSOL. A nonlinear system example problem, the 2D Bratu problem, and the solution obtained by MATLAB NITSOL's are also included.

The central theme of this thesis is the design of optimal balanced black-box stopping criteria in iterative solvers of symmetric positive-definite, symmetric indefinite, and nonsymmetric linear systems arising from finite element approximation of stochastic (parametric) partial differential equations. For a given stochastic and spatial approximation, it is known that iteratively solving the corresponding linear(ized) system(s) of equations to too tight algebraic error tolerance results in a wastage of computational resources without decreasing the usually unknown approximation error. In order to stop optimally-by avoiding unnecessary computations and premature stopping-algebraic error and a posteriori approximation error estimate must be balanced at the optimal stopping iteration. Efficient and reliable a posteriori error estimators do exist for close estimation of the approximation error in a finite element setting. But the algebraic error is generally unknown since the exact algebraic solution is not usually available. Obtaining tractable upper and lower bounds on the algebraic error in terms of a readily computable and monotonically decreasing quantity (if any) of the chosen iterative solver is the distinctive feature of the designed optimal balanced stopping strategy. Moreover, this work states the exact constants, that is, there are no user-defined parameters in the optimal balanced stopping tests. Hence, an iterative solver incorporating the optimal balanced stopping methodology that is presented here will be a black-box iterative solver. Typically, employing such a stopping methodology would lead to huge computational savings and in any case would definitely rule out premature stopping. The constants in the devised optimal balanced black-box stopping tests in MINRES solver for solving symmetric positive-definite and symmetric indefinite linear systems can be estimated cheaply on-the- fly. The contribution of this thesis goes one step further for the nonsymmetric case in the sense that it not only provides an optimal balanced black-box stopping test in a memory-expensive Krylov solver like GMRES but it also presents an optimal balanced black-box stopping test in memory-inexpensive Krylov solvers such as BICGSTAB(L), TFQMR etc. Currently, little convergence theory exists for the memory-inexpensive Krylov solvers and hence devising stopping criteria for them is an active field of research. Also, an optimal balanced black-box stopping criterion is proposed for nonlinear (Picard or Newton) iterative method that is used for solving the finite dimensional Navier-Stokes equations. The optimal balanced black-box stopping methodology presented in this thesis can be generalized for any iterative solver of a linear(ized) system arising from numerical approximation of a partial differential equation. The only prerequisites for this purpose are the existence of a cheap and tight a posteriori error estimator for the approximation error along with cheap and tractable bounds on the algebraic error.

Le modèle de Blacket Scholes reste le modèle de référence sur les marchés des dérivés. Sa parcimonie et sa maniabilité sont certes attractives. Il ne faut cependant pas perdre de vue les hypothèses restrictives, voire simplistes, qui lui servent de base et qui limitent sa capacité à reproduire la dynamique du marché. Afin de refléter un peu mieux cette dynamique, nous introduisons un modèle d’évaluation des options à changement de régime avec sauts. Sous ce modèle, l’hypothèse de complétude des marchés n’est plus valable. Les sources d’incertitude sont plus nombreuses que les instruments disponibles à la couverture. On ne parle plus de réplication/couverture parfaite mais plutôt de réplication optimale dans un sens à définir. Dans cette thèse, on suppose que le marché peut être décrit par plusieurs «régimes» (ou encore par des «modes») re?étant l’état de l’économie, le comportement général des investisseurs et leurs tendances. Pour chacun de ces régimes, le sous-jacent est caractérisé par un niveau de volatilité et de rendement donné. Avec en plus, et a priori des discontinuités du prix du sous-jacent à chaque fois qu’une transition d’un régime à un autre a lieu. La thèse comprend trois parties: 1.Modélisation du problème et application de la théorie du contrôle stochastique. Par l’utilisation du principe de programmation dynamique et la considération des différents régimes de marché, on aboutit à un système de M (le nombre de régimes) équations de Hamilton Jacobi Bellman «HJB» couplées. 2.La résolution numérique de l’équation HJB pour l’évolution d’options, par différences finies généralisées. 3.L’estimation des paramètres du modèle par un filtre récursif, qui produit une estimation récursive d’un état inconnu au vu d’observation bruitée supposée continue, dans le cas où l’état inconnu serait modélisé par une chaîne de Markov à temps discret et espace d’état fini. / Abstract