Algebraic multilevel preconditioning in spectral fractional diffusion

IF 0.7 4区 数学 Q4 MATHEMATICS, APPLIED
Svetozar Margenov
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引用次数: 0

Abstract

The numerical solution of linear systems obtained as a result of discretization of a spectral fractional diffusion problem is studied. The finite element method is applied to the considered boundary value problem. The system matrix is a fractional power of the product of the inverse of the mass matrix and the stiffness matrix. The matrix thus defined is symmetric and positive definite (SPD) with respect to the inner product associated with the mass matrix, but is dense, which is consistent with the nonlocal nature of fractional diffusion. The presented results are in the spirit of the BURA (Best Uniform Rational Approximation) method. BURA reduces numerical solution of the dense linear system to the solution of k systems with sparse SPD diffusion-reaction matrices, where k is the degree of rational approximation. We prove the existence of algebraic multilevel iteration (AMLI) methods for preconditioning such type of emergent matrices that satisfy the conditions for optimal computational complexity. Both multiplicative and additive AMLI preconditioners have been developed, determining the minimum possible degree θ of the hierarchical θ-refinement of the mesh.

谱分数扩散中的代数多层预处理
研究了谱分数扩散问题离散化后线性系统的数值解。将有限元方法应用于所考虑的边值问题。系统矩阵是质量矩阵的逆和刚度矩阵的乘积的分数次方。由此定义的矩阵相对于与质量矩阵相关的内积是对称和正定的(SPD),但是是密集的,这与分数阶扩散的非局域性质是一致的。所提出的结果是在BURA(最佳均匀有理逼近)方法的精神。BURA将密集线性系统的数值解简化为具有稀疏SPD扩散反应矩阵的k个系统的解,其中k为有理逼近度。我们证明了对满足最优计算复杂度条件的这类紧急矩阵进行预处理的代数多层迭代方法的存在性。开发了乘式和加式AMLI预调节器,确定了网格分层θ-细化的最小可能度θ。
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来源期刊
Applications of Mathematics
Applications of Mathematics 数学-应用数学
CiteScore
1.50
自引率
0.00%
发文量
0
审稿时长
3.0 months
期刊介绍: Applications of Mathematics publishes original high quality research papers that are directed towards applications of mathematical methods in various branches of science and engineering. The main topics covered include: - Mechanics of Solids; - Fluid Mechanics; - Electrical Engineering; - Solutions of Differential and Integral Equations; - Mathematical Physics; - Optimization; - Probability Mathematical Statistics. The journal is of interest to a wide audience of mathematicians, scientists and engineers concerned with the development of scientific computing, mathematical statistics and applicable mathematics in general.
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