Preconditioned Krylov Subspace Methods Research Articles

Comparison results of the preconditioned AOR methods for L-matrices

In this paper, we first provide comparison results of several types of the preconditioned AOR (PAOR) methods for solving a linear system whose coefficient matrix is an L-matrix satisfying some weaker conditions than those used in the recent literature. Next, we propose an application of PAOR method to a preconditioner of Krylov subspace method. Lastly, numerical results are provided to show that Krylov subspace method with the PAOR preconditioner performs quite well as compared with the ILU (0) preconditioner.

Reordering and incomplete preconditioning in serial and parallel adaptive mesh refinement and coarsening flow solutions

SUMMARYThe effects of reordering the unknowns on the convergence of incomplete factorization preconditioned Krylov subspace methods are investigated. Of particular interest is the resulting preconditioned iterative solver behavior when adaptive mesh refinement and coarsening (AMR/C) are utilized for serial or distributed parallel simulations. As representative schemes, we consider the familiar reverse Cuthill–McKee and quotient minimum degree algorithms applied with incomplete factorization preconditioners to CG and GMRES solvers. In the parallel distributed case, reordering is applied to local subdomains for block ILU preconditioning, and subdomains are repartitioned dynamically as mesh adaptation proceeds. Numerical studies for representative applications are conducted using the object‐oriented AMR/C software system libMesh linked to the PETSc solver library. Serial tests demonstrate that global unknown reordering and incomplete factorization preconditioning can reduce the number of iterations and improve serial CPU time in AMR/C computations. Parallel experiments indicate that local reordering for subdomain block preconditioning associated with dynamic repartitioning because of AMR/C leads to an overall reduction in processing time. Copyright © 2011 John Wiley & Sons, Ltd.

A multigrid-based preconditioned Krylov subspace method for the Helmholtz equation with PML

In this paper, we generalize the complex shifted Laplacian preconditioner to the complex shifted Laplacian-PML preconditioner for the Helmholtz equation with perfectly matched layer (Helmholtz-PML equation). The Helmholtz-PML equation is discretized by an optimal 9-point difference scheme, and the preconditioned linear system is solved by the Krylov subspace method, especially by the biconjugate gradient stabilized method (Bi-CGSTAB). The spectral analysis of the linear system is given, and a new matrix-based interpolation operator is proposed for the multigrid method, which is used to approximately invert the preconditioner. The numerical experiments are presented to illustrate the efficiency of the preconditioned Bi-CGSTAB method with the multigrid based on the new interpolation operator, also, numerical results are given for comparing the performance of the new interpolation operator with that of classic bilinear interpolation operator and the one suggested in Erlangga et al. (2006) [10].

Improving the efficiency of variational methods for solving strongly nonsymmetric linear algebraic equation system received in convection-diffusion problems

An efficient algorithm for implementing the mathematical model of convection-diffusion transport with a dominant convection is proposed. Preconditioned Krylov subspace methods are used to solve strongly nonsymmetric systems of linear algebraic equations. Theoretical convergence analysis of product triangular skew-symmetric preconditioners is carried out. A series of numerical experiments have confirmed the technique’s efficiency.

Nonstandard Norms and Robust Estimates for Saddle Point Problems

In this paper we discuss how to find norms for parameter-dependent saddle point problems which lead to robust (i.e., parameter-independent) estimates of the solution in terms of the data. In a first step a characterization of such norms is given for a general class of symmetric saddle point problems. Then, for special cases, explicit formulas for these norms are derived. Finally, we will apply these results to distributed optimal control problems for elliptic equations and for the Stokes equations. The norms which lead to robust estimates turn out to differ from the standard norms typically used for these problems. This will lead to block diagonal preconditioners for the corresponding discretized problems with mesh-independent and robust convergence rates if used in preconditioned Krylov subspace methods.

A robust numerical method for the potential vorticity based control variable transform in variational data assimilation

AbstractThe potential vorticity based control variable transformation for variational data assimilation, proposed in Cullen (2003), is a promising alternative to the currently more common vorticity based transformation. It leads to a better decorrelation of the control variables, but it involves solving a highly ill‐conditioned elliptic partial differential equation (PDE), with a constraint. This PDE has so far been impossible to solve to any reasonable accuracy for realistic grid resolutions in finite difference formulations. Following on from the work in Buckeridge and Scheichl (2010) we propose a numerical method for it based on a Krylov subspace method with a multigrid preconditioner. The problem of interest includes a constraint in the form of two‐dimensional elliptic solves embedded within the main three‐dimensional problem. Thus the discretised problem cannot be formulated as a simple linear equation system with a sparse system matrix (as usual in elliptic PDEs). Therefore, in order to precondition the system we apply the multigrid method in Buckeridge and Scheichl (2010) to a simplified form of the three‐dimensional operator (without the embedded two‐dimensional problems) leading to an asymptotically optimal convergence of the preconditioned Krylov subspace method. The solvers used at the Met Office typically take over 100 iterations to converge to a residual tolerance of 0.1 and fail to converge to a tolerance of 10−2. The method proposed in this paper, in contrast, can converge to a tolerance of 10−2 within 15 iterations on all typical grid resolutions used at the Met Office, and is convergent to a tolerance of 10−6. In addition, the method demonstrates almost optimal parallel scalability. Copyright © 2011 Royal Meteorological Society and British Crown Copyright, the Met Office

Convergence analysis of inexact LU-type preconditioners for indefinite problems arising in incompressible continuum analysis

Developing efficient solution methods for indefinite problems arising in constraint problems is an important issue in incompressible or nearly incompressible continuum analysis. In this paper, we first compare the convergence properties of two classical iterative approaches, namely the inexact block triangular algorithm and the inexact block LU algorithm. It is shown that the latter can be applied under more relaxed conditions than the former. We further analyze properties of the latter algorithm when applied as a preconditioner for Krylov subspace methods. Similar to the analysis of the inexact block LU preconditioner, we also analyze the properties of a fill-controlled incomplete LU preconditioner. The theoretical convergence estimates are validated and the performance of the two LU-type preconditioners are compared through numerical experiments with large deformation problems of a hyper-elastic material.

Numerical solution of electromagnetic scattering from a large partly covered cavity

The paper focuses on the numerical study of electromagnetic scattering from two-dimensional (2D) large partly covered cavities, which is described by the Helmholtz equation with a nonlocal boundary condition on the aperture. The classical five-point finite difference method is applied for the discretization of the Helmholtz equation and a linear approximation is used for the nonlocal boundary condition. We prove the existence and uniqueness of the numerical solution when the medium in the cavity is y -direction layered or the number of the mesh points on the aperture is large enough. The fast algorithm proposed in Bao and Sun (2005) [2] for open cavity models is extended to solving the partly covered cavity problem with (vertically) layered media. A preconditioned Krylov subspace method is proposed to solve the partly covered cavity problem with a general medium, in which a layered medium model is used as a preconditioner of the general model. Numerical results for several types of partly covered cavities with different wave numbers are reported and compared with those by ILU-type preconditioning algorithms. Our numerical experiments show that the proposed preconditioning algorithm is more efficient for partly covered cavity problems, particularly with large wave numbers.

On the Behavior of Combination High-Order Compact Approximations with Preconditioned Methods in the Diffusion-Convection Equation

In this paper, a family of high-order compact finite difference methods in combination preconditioned methods are used for solution of the Diffusion-Convection equation. We developed numerical methods by replacing the time and space derivatives by compact finite-difference approximations. The system of resulting nonlinear finite difference equations are solved by preconditioned Krylov subspace methods. Numerical results are given to verify the behavior of high-order compact approximations in combination preconditioned methods for stability, convergence. Also, the accuracy and efficiency of the proposed scheme are considered.

High Performance Iterative Solver for Linear System using Multi GPU

The variable preconditioned (VP) Krylov subspace method on multi Graphics Processing Unit (GPU) is numerically investigated. Besides, the linear system obtained by finite element method with an edge element is adopted for the problem. The results of computations show that VP conjugate gradient method on multi GPU demonstrated significant achievement than that of CPU. Especially, VP conjugate gradient method on multi GPU is 4.35 times faster than that of CPU. However, transmission rate between the PC using Gigabit Ethernet is the bottleneck of the performance.

Parallel Newton–Krylov–Schwarz algorithms for the three-dimensional Poisson–Boltzmann equation in numerical simulation of colloidal particle interactions

We investigate fully parallel Newton–Krylov–Schwarz (NKS) algorithms for solving the large sparse nonlinear systems of equations arising from the finite element discretization of the three-dimensional Poisson–Boltzmann equation (PBE), which is often used to describe the colloidal phenomena of an electric double layer around charged objects in colloidal and interfacial science. The NKS algorithm employs an inexact Newton method with backtracking (INB) as the nonlinear solver in conjunction with a Krylov subspace method as the linear solver for the corresponding Jacobian system. An overlapping Schwarz method as a preconditioner to accelerate the convergence of the linear solver. Two test cases including two isolated charged particles and two colloidal particles in a cylindrical pore are used as benchmark problems to validate the correctness of our parallel NKS-based PBE solver. In addition, a truly three-dimensional case, which models the interaction between two charged spherical particles within a rough charged micro-capillary, is simulated to demonstrate the applicability of our PBE solver to handle a problem with complex geometry. Finally, based on the result obtained from a PC cluster of parallel machines, we show numerically that NKS is quite suitable for the numerical simulation of interaction between colloidal particles, since NKS is robust in the sense that INB is able to converge within a small number of iterations regardless of the geometry, the mesh size, the number of processors. With help of an additive preconditioned Krylov subspace method NKS achieves parallel efficiency of 71% or better on up to a hundred processors for a 3D problem with 5 million unknowns.

Flexible GMRES with Deflated Restarting

In many situations, it has been observed that significant convergence improvements can be achieved in preconditioned Krylov subspace methods by enriching them with some spectral information. On the other hand, effective preconditioning strategies are often designed where the preconditioner varies from one step to the next so that a flexible Krylov solver is required. In this paper, we present a new numerical technique for nonsymmetric problems that combines these two features. We illustrate the numerical behavior of the new solver both on a set of small academic test examples as well as on large industrial simulation arising in wave propagation simulations.

On projected Newton–Krylov solvers for instationary laminar reacting gas flows

Numerical aspects of computational modeling of chemical vapor deposition are discussed. Large sparse strongly nonlinear algebraic systems are to be solved per time step. For this, inexact Newton methods and preconditioned Krylov subspace methods are suitable. To ensure positivity of concentrations, we propose a novel approach, namely a projected inexact Newton method. Unlike the commonly used method of clipping, this conserves mass. Efficiency of several preconditionings is compared. Our numerical tests culminate in an unusually large computation, namely a three-dimensional case with 17 species and 26 reactions.

HSL_MI20: An efficient AMG preconditioner for finite element problems in 3D

AbstractAlgebraic multigrid (AMG) is one of the most effective iterative methods for the solution of large, sparse linear systems obtained from the discretization of second‐order scalar elliptic self‐adjoint partial differential equations. It can also be used as a preconditioner for Krylov subspace methods. In this communication, we report on the design and development of a robust, effective and portable Fortran 95 implementation of the classical Ruge–Stüben AMG, which is available as package HSL_MI20 within the HSL mathematical software library. The routine can be used as a ‘black‐box’ preconditioner, but it also offers the user a range of options and parameters. Proper tuning of these parameters for a particular application can significantly enhance the performance of an AMG‐preconditioned Krylov solver. This is illustrated using a number of examples arising in the unstructured finite element discretization of the diffusion, the convection–diffusion, and the Stokes equations, as well as transient thermal convection problems associated with the Boussinesq approximation of the Navier–Stokes equations in 3D. Copyright © 2009 John Wiley & Sons, Ltd.

Application of the incomplete Cholesky factorization preconditioned Krylov subspace method to the vector finite element method for 3-D electromagnetic scattering problems

The incomplete Cholesky (IC) factorization preconditioning technique is applied to the Krylov subspace methods for solving large systems of linear equations resulted from the use of edge-based finite element method (FEM). The construction of the preconditioner is based on the fact that the coefficient matrix is represented in an upper triangular compressed sparse row (CSR) form. An efficient implementation of the IC factorization is described in detail for complex symmetric matrices. With some ordering schemes our IC algorithm can greatly reduce the memory requirement as well as the iteration numbers. Numerical tests on harmonic analysis for plane wave scattering from a metallic plate and a metallic sphere coated by a lossy dielectric layer show the efficiency of this method.

Multi‐level incomplete factorizations for the iterative solution of non‐linear finite element problems

AbstractSeveral engineering applications give rise quite naturally to linearized FE systems of equations possessing a multi‐level structure. An example is provided by geomechanical models of layered and faulted geological formations. For such problems the use of a multi‐level incomplete factorization (MIF) as a preconditioner for Krylov subspace methods can prove a robust and efficient solution accelerator, allowing for a fine tuning of the fill‐in degree with a significant improvement in both the solver performance and the memory consumption. The present paper develops two novel MIF variants for the solution of multi‐level symmetric positive definite systems. Two correction algorithms are proposed with the aim of preserving the positive definiteness of the preconditioner, thus avoiding possible breakdowns of the preconditioned conjugate gradient solver. The MIF variants are experimented with in the solution of both a single system and a long‐term quasi‐static simulation dealing with a multi‐level geomechanical application. The numerical results show that MIF typically outperforms by up to a factor 3 a more traditional algebraic preconditioner such as an incomplete Cholesky factorization with partial fill‐in. The advantage is emphasized in a long‐term simulation where the fine fill‐in tuning allowed for by MIF yields a significant improvement for the computer memory requirement as well. Copyright © 2009 John Wiley & Sons, Ltd.

Folded Preconditioner: A New Class of Preconditioners for Krylov Subspace Methods to Solve Redundancy-Reduced Linear Systems of Equations

The A-phi formulation, which is widely used in electromagnetic analysis, leads to a redundant linear system of equations that includes a substantial number of redundant degrees of freedom (DOF). We can derive a redundancy-reduced linear system of equations by eliminating the redundant DOF, thereby decreasing the computation costs per iteration for iterative solvers, such as the incomplete Cholesky conjugate gradient (ICCG) solver. This does not, however, result in a reduction in total computation time, due to significant convergence deterioration. In this paper, we present a solution to this problem in the form of folded preconditioners. First, the theorem presented reveals that, for any preconditioned Krylov subspace method for the original redundant linear systems, we can derive the equivalent Krylov subspace method for the redundancy-reduced linear systems by using the corresponding folded preconditioner. As an uncomplicated example, the standard ICCG solver for the original redundant systems has exactly the same convergence property as the CG solver for the redundancy-reduced systems using the folded variant of the IC preconditioner (the folded IC preconditioner). Furthermore, we discuss efficient computational procedures for the folded preconditioners and the design of Krylov subspace algorithms using the preconditioners. A sample full-wave analysis demonstrates the good performance of a newly developed solver, the conjugate orthogonal conjugate gradient (COCG) method with the folded IC preconditioner. The new solver not only lowers the computation costs per iteration by reducing the number of DOF, but also completely avoids the convergence deterioration.

A block inverse-free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems

The inverse-free preconditioned Krylov subspace method of Golub and Ye [G.H. Golub, Q. Ye, An inverse free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems, SIAM J. Sci. Comp. 24 (2002) 312–334] is an efficient algorithm for computing a few extreme eigenvalues of the symmetric generalized eigenvalue problem. In this paper, we first present an analysis of the preconditioning strategy based on incomplete factorizations. We then extend the method by developing a block generalization for computing multiple or severely clustered eigenvalues and develop a robust black-box implementation. Numerical examples are given to illustrate the analysis and the efficiency of the block algorithm.

A Reordering Heuristic for Accelerating the Convergence of the Solution of Some Large Sparse PDE Matrices on Structured Grids by the Krylov Subspace Methods with the ILUT Preconditioner

Given a sparse linear system, A x = b, we can solve the equivalent system P A PT y = P b, x = PT y, where P is a permutation matrix. It has been known that, for example, when P is the RCMK (Reverse Cuthill-Mckee) ordering permutation, the convergence rate of the Krylov subspace method combined with the ILU-type preconditioner is often enhanced, especially if the matrix A is highly nonsymmetric. In this paper we offer a reordering heuristic for accelerating the solution of large sparse linear systems by the Krylov subspace methods with the ILUT preconditioner. It is the LRB (Line Red/Black) ordering based on the well-known 2-point Red-Black ordering. We show that for some model-like PDE (partial differential equation)s the LRB ordered FDM (Finite Difference Method)/FEM (Finite Element Method) discretization matrices require much less fill-ins in the ILUT factorizations than those of the Natural ordering and the RCMK ordering and hence, produces a more accurate preconditioner, if a high level of fill-in is used. It implies that the LRB ordering could outperform the other two orderings combined with the ILUT preconditioned Krylov subspace method if the level of fill-in is high enough. We compare the performance of our heuristic with that of the RCMK (Reverse Cuthill-McKee) ordering. Our test matrices are obtained from various standard discretizations of two-dimensional and three-dimensional model-like PDEs on structured grids by the FDM or the FEM. We claim that for the resulting matrices the performance of our reordering strategy for the Krylov subspace method combined with the ILUT preconditioner is superior to that of RCMK ordering, when the proper number of fill-in was used for the ILUT. Also, while the RCMK ordering is known to have little advantage over the Natural ordering in the case of symmetric matrices, the LRB ordering still can improve the convergence rate, even if the matrices are symmetric.

SIMPLE‐type preconditioners for the Oseen problem

AbstractIn this paper, the steady incompressible Navier–Stokes equations are discretized by the finite element method. The resulting systems of equations are solved by preconditioned Krylov subspace methods. Some new preconditioning strategies, both algebraic and problem dependent are discussed. We emphasize on the approximation of the Schur complement as used in semi implicit method for pressure‐linked equations‐type preconditioners. In the usual formulation, the Schur complement matrix and updates use scaling with the diagonal of the convection–diffusion matrix. We propose a variant of the SIMPLER preconditioner. Instead of using the diagonal of the convection–diffusion matrix, we scale the Schur complement and updates with the diagonal of the velocity mass matrix. This variant is called modified SIMPLER (MSIMPLER). With the new approximation, we observe a drastic improvement in convergence for large problems. MSIMPLER shows better convergence than the well‐known least‐squares commutator preconditioner which is also based on the diagonal of the velocity mass matrix. Copyright © 2008 John Wiley & Sons, Ltd.