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Crandall and Lions [23] introduced the concept of viscosity solutions which provides a foundation for studying the Hamilton-Jacobi equations both theoretically and numerically. Ever since then, computing the viscosity solutions numerically has become very important in a variety of applications. A lot of numerical methods have been developed to compute the viscosity solutions. We study the convergence of classical monotone upwind schemes, for example the fast sweeping method, for static convex Hamilton-Jacobi equations by analyzing a contraction property of such schemes. Heuristic error estimate is discussed, and the convergence proof through the Hopf formula in control theory is also studied. Monotone upwind schemes are at most first order [51]. In order to improve the accuracy when there is source singularity, we introduce a new fast sweeping method for the factored Eikonal equation, which improves the accuracy of original fast sweeping method on the Eikonal equation by resolving the source singularity with an underlying correction function. This new factorization idea comes from problems in geosciences. And it provides a possible procedure for source singularity resolution in other problems. Furthermore, high order schemes are also important in many applications, for example the high frequency wave propagation. The ENO or WENO technique seems to be the popular one. But methods based on ENO or WENO are often slower to converge. They are based on direction by direction approximations with wide stencils to capture smoother approximations of second derivatives. We develop a compact upwind second order scheme for the Eikonal equations by observing a superconvergence phenomena of classical monotone upwind schemes: the numerical gradient of such first order schemes is also first order. The new second order scheme combines this phenomena with the Lagrangian structure of the equations. The stencil can be reduced, and it is upwind. As an application of the fast sweeping method, we apply the method in computer vision by introducing a distance-ordered-homotopic thinning algorithm for computing the skeleton of an object represented by point clouds. This algorithm uses the closest point information calculated efficiently by the fast sweeping method. Further possible ideas on developing fast sweeping methods for static non-convex Hamilton-Jacobi equations are also discussed in the conclusion.
The authors develop a family of fast methods approximating the solution to a wide class of static Hamilton-Jacobi partial differential equations. These partial differential equations are considered in the context of control-theoretic and front-propagation problems. In general, to produce a numerical solution to such a problem, one has to solve a large system of coupled non-linear discretized equations. The techniques use partial information about the characteristic directions to de-couple the system. Previously known fast methods, available for isotropic problems, are discussed in detail. They introduce a family of new Ordered Upwinding Methods (OUM) for general (anisotropic) problems and prove convergence to the viscosity solution of the corresponding Hamilton-Jacobi partial differential equation. The hybrid methods introduced here are based on the analysis of the role played by anisotropy in the context of front propagation and optimal trajectory problems. The performance of the methods is analyzed and compared to that of several other numerical approaches to these problems. Computational experiments are performed using test problems from control theory, computational geometry and seismology.
Handbook of Numerical Methods for Hyperbolic Problems explores the changes that have taken place in the past few decades regarding literature in the design, analysis and application of various numerical algorithms for solving hyperbolic equations. This volume provides concise summaries from experts in different types of algorithms, so that readers can find a variety of algorithms under different situations and readily understand their relative advantages and limitations. Provides detailed, cutting-edge background explanations of existing algorithms and their analysis Ideal for readers working on the theoretical aspects of algorithm development and its numerical analysis Presents a method of different algorithms for specific applications and the relative advantages and limitations of different algorithms for engineers or readers involved in applications Written by leading subject experts in each field who provide breadth and depth of content coverage
The solution of a static Hamilton-Jacobi Partial Differential Equation (HJ PDE) can be used to determine the change of shape in a surface for etching/deposition/lithography applications, to provide the first-arrival time of a wavefront emanating from a source for seismic applications, or to compute the minimal-time trajectory of a robot trying to reach a goal. HJ PDEs are nonlinear so theory and methods for solving linear PDEs do not directly apply. An efficient way to approximate the solution is to emulate the causal property of this class of HJ PDE: the solution at a particular point only depends on values backwards along the characteristic that passes through that point and solution values always increase along characteristics. In our discretization of the HJ PDE we enforce an analogous causal property, that the solution value at a grid node may only depend on the values of nodes in its numerical stencil which are smaller. This causal property is related but not the same thing as an upwinding property of schemes for time dependent problems. The solution to such a discretized system of equations can be efficiently computed using a Dijkstra-like method in a single pass through the grid nodes in order of nondecreasing value. We develop two Dijkstra-like methods for solving two subclasses of static HJ PDEs. The first method is an extension of the Fast Marching Method for isotropic Eikonal equations and it can be used to solve a class of axis-aligned anisotropic HJ PDEs on an orthogonal grid. The second method solves general convex static HJ PDEs on simplicial grids by computing stencils for a causal discretization in an initial pass through the grid nodes, and then solving the discretization in a second Dijkstra-like pass through the nodes. This method is suitable for computing solutions on highly nonuniform grids, which may be useful for extending it to an error-control method based on adaptive grid refinement.
This book is a collection of thoroughly refereed papers presented at the 26th IFIP TC 7 Conference on System Modeling and Optimization, held in Klagenfurt, Austria, in September 2013. The 34 revised papers were carefully selected from numerous submissions. They cover the latest progress in a wide range of topics such as optimal control of ordinary and partial differential equations, modeling and simulation, inverse problems, nonlinear, discrete, and stochastic optimization as well as industrial applications.
This largely self-contained book provides a unified framework of semi-Lagrangian strategy for the approximation of hyperbolic PDEs, with a special focus on Hamilton-Jacobi equations. The authors provide a rigorous discussion of the theory of viscosity solutions and the concepts underlying the construction and analysis of difference schemes; they then proceed to high-order semi-Lagrangian schemes and their applications to problems in fluid dynamics, front propagation, optimal control, and image processing. The developments covered in the text and the references come from a wide range of literature.
This book contains 71 original, scienti?c articles that address state-of-the-art researchrelatedto scale space and variationalmethods for image processing and computer vision. Topics covered in the book range from mathematical analysis of both established and new models, fast numerical methods, image analysis, segmentation, registration, surface and shape construction and processing, to real applications in medical imaging and computer vision. The ideas of scale spaceandvariationalmethodsrelatedtopartialdi?erentialequationsarecentral concepts. The papers re?ect the newest developments in these ?elds and also point to the latest literature. All the papers were submitted to the Second International Conference on Scale Space and Variational Methods in Computer Vision, which took place in Voss, Norway, during June 1–5, 2009. The papers underwent a peer review process similar to that of high-level journals in the ?eld. We thank the authors, the Scienti?c Committee, the Program Committee and the reviewers for their hard work and helpful collaboration. Their contribution has been crucial for the e?cient processing of this book, and for the success of the conference.