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We restrict our attention to finite difference discretizations of the differential operator 1. The requirement of monotonicity drastically affects the properties and construction of finite difference schemes. Theorem 4 in [27] proves that local monotone discretizations have at most first order for first-order equations and second order for second-order equations. What is more, standard fixed stencil methods are monotone only under restrictions on the diffusion matrix, such as diagonal dominance [9,12].

Results from [6,21] further illustrate the limitations of such methods for the monotone approximation of second order derivatives. This implies that generally approximations have to be non-local on the discrete level, i. Such schemes are referred to as wide stencils. For general diffusion matrices, first order accurate wide stencils of the type considered here have been proposed in [5,9], and a mixed fixed- and wide-stencil scheme in [19].

In this article, we analyse two issues arising in practice when numerically solving 1. This approximation combines wide stencils in the directions determined by the columns of the diffusion matrix aa and the drift ba, together with linear interpolation. Therefore, 0 is replaced. It is shown in [9] that the leading order terms of the local truncation error are proportional to k2 and Ar-, where the last quantity corresponds to the linear interpolation error in the finite difference formulae 1.

Following the notation in [9], the LISL finite difference approximations for the differential operator in 1. In particular, [9] discusses the following three schemes:. The authors show that this family of discretizations of 1. Definition 1. Hence, when applied. As discussed in [9], the overstepping may pose a problem depending on the equation and the type of boundary conditions imposed.

We consider Dirichlet boundary conditions here. Our first goal is to present and analyse a modification of the LISL scheme to deal with overstepping for problems on bounded domains with Dirichlet boundary conditions, and general drift and diffusion coefficients. We describe how to truncate the LISL stencil so that the truncation remains consistent and monotone. We prove that the resulting stencil for Scheme 2 above is of positive type as per Definition 1. This is not the case for Schemes 1 and 3. We also observe that the truncation has both local and global impacts on the properties of the scheme.

Locally, the modification of the scheme leads to a loss of accuracy of half an order in the consistency error, i. We compare the accuracy of the truncation with extrapolations of the boundary conditions by way of numerical tests for benchmark problems. The second goal is therefore the use of implicit schemes and the efficient solution of the discrete system 1. Comparing with 1. The maximisation over a in 1. Therefore, following results in [4], we can use policy iteration to.

We find in contrast to [19] that this last step is the computationally most costly part of the overall algorithm if direct linear solvers or standard iterative solvers are used. In the literature on multigrid for HJB equations, two main approaches are observed: on the one hand, multigrid is applied directly to the non-linear problem, as in [3,13,14]; and on the other hand, multigrid is applied to a linearised problem, as in [1].

Book Semi Lagrangian Approximation Schemes For Linear And Hamilton Jacobi Equations

In particular, [3,14] provide the first multigrid algorithms for HJB equations and prove convergence, while [13] presents a novel smoother for HJB equations based on damped value iteration [17]. These articles have in common the use of standard fixed stencil finite difference approximations and the use of a geometric structure when building the hierarchy of multigrid subspaces.

The novelty of this article is to study the application of multigrid preconditioning to a wide stencil discretization. We will demonstrate, both by Fourier analysis of a model problem and by numerical tests in a more complex application, that standard geometric multigrid does not give mesh-size independent convergence. We then investigate algebraic multigrid methods. The basis for the specific algorithm we use was introduced in [24] for linear elliptic PDEs.

It empirically showed that "aggregation based methods could yield robust2 and convergent schemes if used as preconditioners of a Krylov method, and were part of an enhanced multigrid cycle, not simple V- or W-cycles" as considered in [31]. By enhanced multigrid cycles, the authors refer to recursive schemes in which at each coarse level the solution to the residual equation is computed using a number of Krylov subspace iterations as in [26] or with a semi-iterative method based on Chebyshev polynomials called the AMLI cycle, see Section 5. The aggregates were formed using heuristic criteria following coupling in the strongest direction.

In [22] the authors introduced an aggregation-based multigrid method with guaranteed convergence rate for symmetric M-matrices with non-negative row sum. A LISL discretization matrix is only symmetric in very specific cases with limited practical interest.

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For non-symmetric matrices, in [25] convergence of a simplified two-grid scheme using aggregation is proved for non-singular M-matrices with non-negative row and column sums. We will derive conditions on the coefficients of the HJB equation such that this theory applies, and show empirically that aggregation-based multigrid gives roughly mesh-size independent convergence.

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The rest of the article is organised as follows. Section 2 discusses the truncation of the LISL scheme for points whose stencil exceeds the domain and compares its performance to naive extrapolations of the boundary conditions. Section 3 considers the application of three different multigrid methods to linear systems where the coefficient matrix arises from LISL discretizations. Section 4 contains the final remarks.

A dynamic domain decomposition for the eikonal-diffusion equation

The optimisation step is typically fast if the control is taken from a finite set, if the local control problem is analytically solvable e. It will be more costly, if the optimal control has to be approximated by exhaustive search over a discretised control set, especially if the dimension of the control space is higher than the spatial dimension of the PDE. In the examples considered in this paper, the control is scalar and we optimise by linear search over a one-dimensional control mesh. In this section, we analyse adaptations of Schemes for initial-boundary value problems on bounded domains.

In the following, we therefore discuss the truncation of 1. We first outline how the method can be defined on a general domain with curved boundary, but later especially in the numerical tests focus for simplicity on rectangular domains. See Fig. We say the stencil "oversteps". The modified stencil samples the domain boundary.

If it does, for any t we define. In the remainder of this section we restrict our attention to the truncation of the scheme on rectangular domains, in which case the elements of the Cartesian mesh cover exactly the domain and case B does not occur. Moreover, this means that interior mesh points cannot be arbitrarily close to the boundary, but are always at least Ax away. In the truncated scheme 2.

As we will see below, this is only possible for Scheme 2. Proof By Taylor expansion of a smooth test function we find that the consistency conditions for Scheme 1 are. This overdetermined system has a solution only if there is linear dependence between the equations. Except for special cases, e. Hence, in general the truncated Scheme 1 is not consistent. We conclude that for points whose stencil oversteps the boundary, the approximations of the first and second derivative should be considered separately, as done in Scheme 2.

Ignoring the interpolation error for the time being, the coefficients are obtained from the consistency conditions up to a term o Ax ,.

By construction of the truncated stencil 2. The contribution to the consistency error of 2. Proof The claim in a follows from 2. The limits in b follow from 2. To prove c we use Taylor expansions for each p and conclude using the limits in b. Remark 2. The scheme is consistent at points with two-sided overstepping if the truncated scheme is not interpolated at the boundary but uses the exact boundary values.

In that case, the consistency error for those points is O Ax.


The changes in the finite difference weights of scheme 2. We start by writing the scheme on a discrete time-space grid with mesh parameters At and Ax as. The first equality follows from 2. Writing the overall scheme in the form 1. This has the advantage that interpolation error is avoided.

Moreover, as this value then contributes to the right-hand-side f of Eq. This is advantageous for the iterative solution, see Sect. The next proposition contains the positivity conditions for the coefficients B defined above. Proposition 2. Corollary 2. Proof From Corollary 2. To test the truncation of the stencil, we consider Problems A and B in Section 9. Both problems follow the formulation in 1. Problem A see Section 9. Problem B see Section 9.

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We discretize the spatial domain using Cartesian grids with NX x NX equispaced nodes and for the control set A we take Na equally spaced points. Here, Iax is the usual bilinear interpolator on rectangles. For illustration of the stencil and its non-locality, the top row of Fig. Colour coded lines link the stencil points with the node where the numerical solution is computed, the different colours correspond to the different ya''. The bottom row of Fig. The distance is measured as multiples of Ax and given by J, where the grid is of size x and 10 points. Problems A and B were obviously chosen in [9] for their periodic solutions, to be able to analyse the convergence of the scheme without the complication of boundary conditions.

Here, we do not make use of the periodicity but only use the values at the boundary and not outside the domain. We report the w-norm of the errors over two regions: the first one comprising the whole domain, and the second one comprising part of the interior of the domain. Journal of Geometric Mechanics , , 4 4 : Claudio Marchi. On the convergence of singular perturbations of Hamilton-Jacobi equations. Isabeau Birindelli , J. Homogenization of Hamilton-Jacobi equations in the Heisenberg group. Holger Heumann , Ralf Hiptmair. Eulerian and semi-Lagrangian methods for convection-diffusion for differential forms.

Daniel Guo , John Drake. A global semi-Lagrangian spectral model for the reformulated shallow water equations. Conference Publications , , Special : Nonlocal spatially inhomogeneous Hamilton-Jacobi equation with unusual free boundary. Yuxiang Li. Stabilization towards the steady state for a viscous Hamilton-Jacobi equation.

Semi-Lagrangian Approximation Schemes for Linear and Hamilton—Jacobi Equations

Analysis of the attainment of boundary conditions for a nonlocal diffusive Hamilton-Jacobi equation. Limit of the infinite horizon discounted Hamilton-Jacobi equation. Nicolas Forcadel , Mamdouh Zaydan. A comparison principle for Hamilton-Jacobi equation with moving in time boundary.

49: April Hamilton Jacobi theory - Part 1

Preface: Recent developments related to conservation laws and Hamilton-Jacobi equations. Systems of convex Hamilton-Jacobi equations with implicit obstacles and the obstacle problem. Yasuhiro Fujita , Katsushi Ohmori. Inequalities and the Aubry-Mather theory of Hamilton-Jacobi equations. Olga Bernardi , Franco Cardin.

American Institute of Mathematical Sciences. Previous Article Hyperbolic equations of Von Karman type. Discrete models for fluid-structure interactions: The finite element Immersed Boundary Method. We propose a parallel algorithm for the numerical solution of the eikonal-diffusion equation, by means of a dynamic domain decomposition technique.

An Adaptive Sparse Grid Semi-Lagrangian Scheme for First Order Hamilton-Jacobi Bellman Equations

The new method is an extension of the patchy domain decomposition method presented in [5] for first order Hamilton-Jacobi-Bellman equations. Using the connection with stochastic optimal control theory, the semi-Lagrangian scheme underlying the original method is modified in order to deal with possibly degenerate diffusion. We show that under suitable relations between the discretization parameters and the diffusion coefficient, the parallel computation on the proposed dynamic decomposition can be faster than that on a static decomposition.

Some numerical tests in dimension two are presented, in order to show the features of the proposed method. Keywords: Hamilton-Jacobi equations , eikonal-diffusion equation , semi-Lagrangian schemes , Domain decomposition , stochastic optimal control. Citation: Simone Cacace, Maurizio Falcone.

A dynamic domain decomposition for the eikonal-diffusion equation. This largely self-contained book provides a framework for the semi-Lagrangian strategy for 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 cover high-order semi-Lagrangian schemes and their applications to problems in fluid dynamics, front propagation, optimal control, and image processing. The text brings together developments from a wide range of sources to provide a unified treatment of the subject.

This book is written for graduate and advanced undergraduate courses on numerical methods, and for researchers and practitioners whose work involves numerical analysis of hyperbolic PDEs. Ihr Name:. Folgen Sie uns beck-shop.