Search

The anisotropic space-fractional diffusion equations in two dimensions are discretized by the Crank-Nicolson difference scheme with the weighted and shifted Grünwald formula, which is unconditionally stable and second-order convergence. The coefficient matrix of the discretized linear system possesses a two-level Toeplitz-like structure. Due to the anisotropy, the standard multigrid method converges slowly. By utilizing the GMRES method with a newly proposed tridiagonal preconditioner as a smoother, the convergence rate of the multigrid method can be accelerated significantly. The proposed tridiagonal preconditioner is shown to be invertible and a numerical experiment is given to demonstrate the efficiency of the proposed multigrid method with preconditioned smoother. [ABSTRACT FROM AUTHOR]

Exponential stability of system with time-varying delay is studied in this paper. By taking intermediate polynomials and slack variables, we propose novel matrix-refined weighted functions (MRWFs). Then, based on the MRWFs, we construct a suitable Lyapunov-Krasovskii functional (LKF) and propose an exponential stability condition. Finally, numerical examples verify that the stability condition proposed in this paper is more effective, which is less conservative than some past criteria. [ABSTRACT FROM AUTHOR]

High dimensional conservative spatial distributed-order fractional diffusion equation is discretized by midpoint quadrature rule, Crank–Nicolson method, and a finite volume approximation, with alternating direction implicit scheme. The resulting scheme is shown to be consistent and unconditionally stable, hence convergent with order 3 − α , where α is the maximum of the involving fractional orders. Moreover, if the initial condition and source term possess Tensor-Train format (TT-format) with low TT-ranks, the scheme can be solved in TT-format, such that higher dimensional cases can be considered. Perturbation analysis ensures that the accumulated errors due to data recompression do not affect the overall convergence order. Numerical examples with low TT-ranks initial conditions and source terms, and with dimensions up to 20 are tested. [ABSTRACT FROM AUTHOR]

In this paper, high dimensional two-sided space fractional diffusion equations, derived from the fractional Fick’s law, and with monotonic variable diffusion coefficients, are solved by alternating direction implicit method. Each linear system corresponding to each spatial direction thus resulted is solved by Krylov subspace method. The method is accelerated by applying an approximate inverse preconditioner, where under certain conditions we showed that the normalized preconditioned matrix is equal to a sum of identity matrix, a matrix with small norm, and a matrix with low rank, such that the preconditioned Krylov subspace method converges superlinearly. We also briefly present some fast algorithms whose computational cost for solving the linear systems is O ( n log n ) , where n is the matrix size. The results are illustrated by some numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, a τ -preconditioner for a novel fourth-order finite difference scheme of two-dimensional Riesz space-fractional diffusion equations (2D RSFDEs) is considered, in which a fourth-order fractional centered difference operator is adopted for the discretizations of spatial Riesz fractional derivatives, while the Crank-Nicolson method is adopted to discretize the temporal derivative. The scheme is proven to be unconditionally stable and has a convergence rate of O (Δ t 2 + Δ x 4 + Δ y 4) in the discrete L 2 -norm, where Δ t , Δ x and Δ y are the temporal and spatial step sizes, respectively. In addition, the preconditioned conjugate gradient (PCG) method with τ -preconditioner is applied to solve the discretized symmetric positive definite linear systems arising from 2D RSFDEs. Theoretically, we show that the τ -preconditioner is invertible by a new technique, and analyze the spectrum of the corresponding preconditioned matrix. Moreover, since the τ -preconditioner can be diagonalized by the discrete sine transform matrix, the total operation cost of the PCG method is O (N x N y log ⁡ N x N y) , where N x and N y are the number of spatial unknowns in x - and y -directions. Finally, numerical experiments are performed to verify the convergence orders, and show that the PCG method with the τ -preconditioner for solving the discretized linear system has a convergence rate independent of discretization stepsizes. [ABSTRACT FROM AUTHOR]

We consider the solution of differential equations with multidelays by using boundary value methods (BVMs). These methods require the solution of some nonsymmetric, large and sparse linear systems. The GMRES method with the Strang-type block-circulant preconditioner is proposed to solve these linear systems. If an Ak1,k2-stable BVM is used, we show that our preconditioner is invertible and the spectrum of the preconditioned matrix is clustered. It follows that when the GMRES method is applied to solving the preconditioned systems, the method would converge fast. Numerical results are given to show the effectiveness of our methods. [Copyright &y& Elsevier]

In this paper, we study nonsymmetric and highly nondiagonally dominant linear systems that arise from discretizations of constant-coefficient first-order partial differential equations (PDEs). We apply the generalized minimal residual method [Y. Saad, Iterative Methods for Sparse Linear Systems, PWS Publishing Company, Boston] for solving the system with a preconditioner based on the fast sine transform. An analytic formula for the eigenvalues of the preconditioned matrix is derived and it is shown that the eigenvalues are clustered around 1 except some outliers. The outlier eigenvalues are bounded and well separated from the origin when the size of system increases. In numerical experiments, we compare our preconditioner with the semi-Toeplitz preconditioner proposed in [SIAM J. Sci. Comput. 17 (1996) 47]. We refer to [J. Numer. Linear Algebra Appl. 1 (1992) 77, Numer. Math. J. Chinese Univ. 2 (1993) 116, BIT 32 (1992) 650, Linear Algebra Appl. 293 (1999) 85] for the early works on preconditioning techniques for PDEs. [Copyright &y& Elsevier]

In recent years, fractional partial differential equation (FPDE) has been widely applied in options pricing problems, which better explains many important empirical facts of financial markets. However, the vast majority of the literatures focus on pricing the single asset option under the FPDE framework. In this paper, a two-dimensional FPDE governing the valuation of rainbow options is established, where two underlying assets are assumed to follow independent exponential Lévy processes, and its boundary conditions are determined by solving one-dimensional FPDEs. A second-order accurate finite difference scheme is proposed to discretize the two-dimensional FPDE. Given the block Toeplitz with Toeplitz block structure of the coefficient matrix, a fast preconditioned Krylov subspace method is developed for solving the resulting linear system with O (N log N) computational complexity per iteration, where N is the matrix size. The proposed preconditioner accelerates the convergence of the iterative method with theoretical analysis. Numerical examples are given to demonstrate the accuracy and efficiency of our proposed numerical methods. [ABSTRACT FROM AUTHOR]

• Two-dimensional distributed-order time-space fractional diffusion problem is considered and its finite difference discretization is studied. • The stability and convergence of the scheme are investigated. • The spatial second-order convergence and the temporal optimal convergence are obtained. • A fast and memory saving algorithm for solving DO time-space fractional diffusion equation is developed through Gauss quadrature formula, ESA method and PCG method. • Numerical experiments show strong effectiveness and efficiency of the method. In this paper, a fast algorithm is proposed for solving distributed-order time-space fractional diffusion equations. Integral terms in time and space directions are discretized by the Gauss-Legendre quadrature formula. The Caputo fractional derivatives are approximated by the exponential-sum-approximation method, and the finite difference method is applied for spatial approximation. The coefficient matrix of the discretized linear system is symmetric positive definite and possesses block-Toeplitz-Toeplitz-block structure. The preconditioned conjugate gradient method with a block-circulant-circulant-block preconditioner is employed to solve the linear system. Theoretically, the stability and convergence of the proposed scheme are discussed. Numerical experiments are carried out to demonstrate the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

A fast preconditioned policy iteration method is proposed for the Hamilton–Jacobi–Bellman (HJB) equation involving tempered fractional order partial derivatives, governing the valuation of American options whose underlying asset follows exponential Lévy processes. An unconditionally stable upwind finite difference scheme with shifted Grünwald approximation is first developed to discretize the established HJB equation under the tempered fractional diffusion models. Next, the policy iteration method as an outer iterative method is utilized to solve the discretized HJB equation and proven to be convergent in finite steps to its numerical solution. Given the Toeplitz-like structure of the coefficient matrix in each policy iteration, the resulting linear system can be fast solved by the Krylov subspace method as an inner iterative method via fast Fourier transform (FFT). Furthermore, a novel preconditioner is proposed to speed up the convergence rate of the inner Krylov subspace iteration with theoretical analysis to ensure the linear system can be solved in O ( N log N ) operations under some mild conditions, where N is the number of spatial node points. Numerical examples are given to demonstrate the accuracy and efficiency of the proposed fast preconditioned policy method. [ABSTRACT FROM AUTHOR]