Types Of Preconditioners Research Articles

Analysis of efficient preconditioner for solving Poisson equation with Dirichlet boundary condition in irregular three-dimensional domains

This paper analyzes modified ILU (MILU)-type preconditioners for efficiently solving the Poisson equation with Dirichlet boundary conditions on irregular domains. In [1], the second-order accuracy of a finite difference scheme developed by Gibou et al. [2] and the effect of the MILU preconditioner were presented for two-dimensional problems. However, the analyses do not directly extend to three-dimensional problems. In this paper, we first demonstrate that the Gibou method attains second-order convergence for three-dimensional irregular domains, yet the discretized Laplacian exhibits a condition number of O(h−2) for a grid size h. We show that the MILU preconditioner reduces the order of the condition number to O(h−1) in three dimensions. Furthermore, we propose a novel sectored-MILU preconditioner, defined by a sectorized lexicographic ordering along each axis of the domain. We demonstrate that this preconditioner reaches a condition number of order O(h−1) as well. Sectored-MILU not only achieves a similar or better condition number than conventional MILU but also improves parallel computing efficiency, enabling very efficient calculation when the dimensionality or problem size increases significantly. Our findings extend the feasibility of solving large-scale problems across a range of scientific and engineering disciplines.

A recycling preconditioning method with auxiliary tip subspace for elastic crack propagation simulation using XFEM

In this paper, we propose a recycling preconditioning method with auxiliary tip subspace for solving a sequence of highly ill-conditioned linear systems of equations of different sizes arising from elastic crack propagation problems discretized by the extended finite element method. To construct a Schwarz type preconditioner, the finite element mesh is decomposed into crack tip subdomains, which contain all the degrees of freedom (DOFs) of the branch enrichment functions, and regular subdomains, which contain the standard DOFs and the DOFs of the Heaviside and the Junction enrichment functions. As cracks propagate these subdomains are modified accordingly, and the subdomain matrices are constructed as the restriction of the global matrix to the subdomains. In the overlapping Schwarz preconditioners, the crack tip subproblems are solved exactly and the regular subproblems are solved by some inexact solvers, such as ILU. We consider problems with and without crack intersections and develop a simple scheme to update, instead of re-computing, the subdomain problems as cracks propagate, in which only crack tip subdomains are updated around the new crack tips and all the regular subdomains remain unchanged. Therefore, no extra search is required, and the sizes of crack tip subproblems do not increase as cracks propagate, which greatly saves the computational cost. Moreover, starting from the second system, the Krylov subspace method uses a nontrivial initial guess constructed using the solution of the previous system with a modification around the new crack tips. The strategy accelerates the convergence remarkably. Numerical experiments demonstrate the efficiency of the proposed algorithms applied to problems with several types of cracks.

ODE‐based double‐preconditioning for solving linear systems Aαx=b and f(A)x=b

AbstractThis article is devoted to the computation of the solution to fractional linear algebraic systems using a differential‐based strategy to evaluate matrix–vector products , with . More specifically, we propose ODE‐based preconditioners for efficiently solving fractional linear systems in combination with traditional sparse linear system preconditioners. Different types of preconditioners are derived (Jacobi, incomplete LU, Padé) and numerically compared. The extension to systems is finally considered.

A preconditioned fast collocation method for a linear bond-based peridynamic model

We develop a fast collocation method for a static bond-based peridynamic model. Based on the analysis of the structure of the stiffness matrix, a fast matrix-vector multiplication technique was found, which can be used in the Krylov subspace iteration method. In this paper, we also present an effective preconditioner to accelerate the convergence of the Krylov subspace iteration method. Using the block-Toeplitz–Toeplitz-block (BTTB)-type structure of the stiffness matrix, we give a block-circulant-circulant-block (BCCB)-type preconditioner. The numerical experiments show the utility of the preconditioned fast collocation method.

Performance comparisons of geometric multigrid solvers and balancing domain decomposition solvers

Multigrid and domain-decomposition methods are now widely used in large-scale simulation studies. Meanwhile, many types of preconditioners are available for speeding the convergence. Users of analysis models are interested in the method or preconditioner that minimizes the computing time. In this paper, we analyze a thermal problem and a solid problem using two free, open-source software programs—the UG4 framework and the ADVENTURE system—based on geometric multigrid (GM) solvers and balancing domain-decomposition (BDD) solvers. We examine and report the computing times and iteration numbers to convergence of the two programs, and generate a common software interface between the UG4 framework and ADVENTURE system. Through this software interface, we can compare the solvers of the two software systems using the same mesh data. The results were presented on the CX400 system at the Information Technology Center of Nagoya University, Japan. The convergence rate of GM solver improved after suitable smoothing and scaling to finer grids. The BDD solver is suitable for large-scale analyses of structures with detailed and complex geometries.

Shot-record extended model domain preconditioners for least-squares migration

We have studied the preconditioned conjugate gradient (CG) algorithm in the context of shot-record extended model domain least-squares migration. The CG algorithm is a powerful iterative technique that can solve the least-squares migration problem efficiently; however, to see the merits of least-squares migration, one needs to apply the algorithm for several iterations. Generally speaking, the convergence rate of the CG algorithm depends on the condition number of the operator. Preconditioners are a family of operators that are easy to build and invert. Proper preconditioners can cluster the eigenvalues of the original operator; hence, they reduce the condition number of the operator that one wishes to invert. Accordingly, preconditioning the operator can, in theory, improve the convergence rate of the algorithm. In least-squares migration, the diagonal scaling of the Hessian and the approximated inverse of the Hessian are proven to work well as a preconditioner. We develop and apply two types of preconditioners for the shot-record extended model domain least-squares migration problem. The first preconditioner belongs to the diagonal scaling category, and a second preconditioner is a filter-based approach, which approximates the partial Hessian operators by local convolutional filters. The goal is to increase the convergence rate of the shot-record extended model domain least-squares migration using the reformulated cost function with a preconditioned operator. Experiments with a synthetic Sigsbee model and a real data example from the Gulf of Mexico, Mississippi Canyon data set, indicate that preconditioning the linear system of the equations improves the convergence rate of the algorithm.

Robust and parallel scalable iterative solutions for large-scale finite cell analyses

The finite cell method is a flexible discretization technique for numerical analysis on domains with complex geometries. By using a non-boundary conforming computational domain that can be easily meshed, automatized computations on a wide range of geometrical models can be performed. The application of the finite cell method, and other immersed methods, to large real-life and industrial problems is often limited due to the conditioning problems associated with these methods. These conditioning problems have caused researchers to resort to direct solution methods. This significantly limits the maximum size of solvable systems. Iterative solvers are better suited for large-scale computations than their direct counterparts due to their lower memory requirements and suitability for parallel computing. These benefits can, however, only be exploited when systems are properly conditioned. In this contribution we present an Additive-Schwarz type preconditioner that enables efficient and parallel scalable iterative solutions of large-scale multi-level hp-refined finite cell systems.

On Partial Cholesky Factorization and a Variant of Quasi-Newton Preconditioners for Symmetric Positive Definite Matrices

This work studies limited memory preconditioners for linear symmetric positive definite systems of equations. Connections are established between a partial Cholesky factorization from the literature and a variant of Quasi-Newton type preconditioners. Then, a strategy for enhancing the Quasi-Newton preconditioner via available information is proposed. Numerical experiments show the behaviour of the resulting preconditioner.

Semismooth Newton methods with domain decomposition for American options

In this paper, we develop a class of parallel semismooth Newton algorithms for the numerical solution of the American option under the Black–Scholes–Merton pricing framework. In the approach, a nonlinear function is used to transform the complementarity problem, which arises from the discretization of the pricing model, into a nonlinear system. Then, a generalized Newton method with a domain decomposition type preconditioner is applied to solve this nonlinear system. In addition, an adaptive time stepping technique, which adjusts the time step size according to the initial residual of Newton iterations, is applied to improve the performance of the proposed method. Numerical experiments show that the proposed semismooth method has a good accuracy and scalability.

A Study of Preconditioned Jacobian-Free Newton-Krylov Discontinuous Galerkin Method for Compressible Flows on 3D Hexahedral Grids

Storage requirement and computational efficiency have always been challenges for the efficient implementation of discontinuous Galerkin (DG)methods for real life applications. In this paper, a fully implicit Jacobian-Free Newton-Krylov (JFNK) method is developed in the context of DG discretizations for the three-dimensional compressible Euler and Navier-Stokes equations. Compared with the Jacobian-based methods, the Jacobian-Free approach saves the storage for the Jacobian matrix which can be of great importance for DG methods. Three types of preconditioners are investigated in which the block diagonal preconditioner requires the least storage, while the block LU-SGS and ILU0 preconditioners require more storage but are more computationally efficient. An implicit time-stepping strategy is adopted for the stability of the current solver,which is based upon a hexahedral spatialmesh and the nonlinear solver package Kinsol is used to improve the computational efficiency and robustness. Numerical results demonstrate that the preconditioned JFNK-DG solver can substantially reduce the storage requirement compared with the Jacobian based method without significantly compromising accuracy or efficiency. Furthermore, as a good compromise between efficiency and storage requirement, the ILU0 preconditioner shows the best choice of the preconditioners presented.

Communication in task‐parallel ILU‐preconditioned CG solvers using MPI + OmpSs

SummaryWe target the parallel solution of sparse linear systems via iterative Krylov subspace–based methods enhanced with incomplete LU (ILU)‐type preconditioners on clusters of multicore processors. In order to tackle large‐scale problems, we develop task‐parallel implementations of the classical iteration for the CG method, accelerated via ILUPACK and ILU(0) preconditioners, using MPI + OmpSs. In addition, we integrate several communication‐avoiding strategies into the codes, including the butterfly communication scheme and Eijkhout's formulation of the CG method. For all these implementations, we analyze the communication patterns and perform a comparative analysis of their performance and scalability on a cluster consisting of 16 nodes, with 16 cores each.

Efficient CPR-type preconditioner and its adaptive strategies for large-scale parallel reservoir simulations

With the rapid development of high performance parallel computers and the increasing demands of large-scale reservoir simulation, the development of effective and fast solvers and preconditioners for parallel reservoir simulation has become increasingly important. The incomplete LU factorization (ILU) preconditioners are the most commonly used preconditioners in reservoir simulations due to their low computational cost. They have been implemented in commercial simulators as the default preconditioners. However, for reservoirs with highly heterogeneous permeability, the CPR (constrained pressure residual)-type preconditioners are more effective than the ILU preconditioners, especially when parallel computing is employed. The CPR-type preconditioners consist of multi-stages of preconditioning processes, which lead to huge computational costs. Employing the algebraic multigrid (AMG) method to solve a pressure system and the ILU method to solve the whole system has two indispensable stages. For parallel computation, due to the serial nature of the ILU method, the ILU method is usually combined with the restricted additive Schwarz (RAS) method and used as a subdomain solver. Different settings of the AMG and RAS methods, such as a grid coarsening strategy, interpolation and smoothing in the AMG method, and a subdomain solver and an overlap level in the RAS method, as well as different CPR-type preconditioners, yield significantly different performance. This paper first gives an overview of the CPR-type preconditioners and the AMG and RAS methods. Then a detailed comparison between different CPR-type preconditioners with different settings of the AMG and RAS methods is presented. Based on the comparison, the efficient settings of the AMG and RAS methods and the most efficient CPR-type preconditioner are given. Furthermore, an adaptive preconditioning strategy is designed to save the computational time. The adaptive preconditioning strategy introduces an indicator to measure the difficulty level of solving a linear system. According to difficulties, a RAS–ILU preconditioner or a CPR-type preconditioner is automatically and dynamically selected to achieve both efficiency and effectiveness.

(I+βU)型前処理付Gauss-Seidel法の収束定理

(I+βU)型前処理付Gauss-Seidel法の収束定理

Alternating direction implicit type preconditioners for the steady state inhomogeneous Vlasov equation

The purpose of the current work is to find numerical solutions of the steady state inhomogeneous Vlasov equation. This problem has a wide range of applications in the kinetic simulation of non-thermal plasmas. However, the direct application of either time stepping schemes or iterative methods (such as Krylov-based methods such as the generalized minimal residual method (GMRES) or relaxation schemes) is computationally expensive. In the former case the slowest time scale in the system forces us to perform a long time integration while in the latter case a large number of iterations is required. In this paper we propose a preconditioner based on an alternating direction implicit type splitting method. This preconditioner is then combined with both GMRES and Richardson iteration. The resulting numerical schemes scale almost ideally (i.e. the computational effort is proportional to the number of grid points). Numerical simulations conducted show that this can result in a speed-up of close to two orders of magnitude (even for intermediate grid sizes) with respect to the unpreconditioned case. In addition, we discuss the characteristics of these numerical methods and show the results for a number of numerical simulations.

Multipreconditioned Gmres for Shifted Systems

An implementation of GMRES with multiple preconditioners is proposed for solving shifted linear systems with shift-and-invert preconditioners. With this type of preconditioner, the Krylov subspace can be built without requiring the matrix-vector product with the shifted matrix. Furthermore, the multipreconditioned search space is shown to grow only linearly with the number of preconditioners. This allows for a more efficient implementation of the algorithm. The proposed implementation is tested on shifted systems that arise in computational hydrology and the evaluation of different matrix functions. The numerical results indicate the effectiveness of the proposed approach.

A new numerical method for solution of boiling flow using combination of SIMPLE and Jacobian-free Newton-Krylov algorithms

In this paper, an efficient and stable numerical approach is developed in which a different combination of Jacobian-free Newton-Krylov (JFNK) and SIMPLE methods are introduced for solution of two-phase flow in both steady state and transient conditions. It is shown that, combination of Krylov subspace methods and SIMPLE type preconditioner give a fast convergence in the solution of the Drift Flux Model (DFM). It is demonstrated that the stability problem with SIMPLE approach, for two-phase flow problems, can be avoided by combining with JFNK. RELAP5 code and experimental data are considered for verification study. For all benchmarks, the proposed methods predictions are in good agreement with experiment and RELAP5 results. The accuracy of the proposed schemes were studied and compared with the classical SIMPLE method. It was found that the present approach to two-phase flow simulation predicts accurate results over a wide pressure range; whereas the classical SIMPLE algorithm underestimated the void fraction at low pressures. Finally, a typical power transient is considered to demonstrate how the problem fastest time scale can change during the course of a transient. The present assessment of the numerical method showed that the time step control is not based on a stability time step restriction, like the material Courant limit.

An improvement of H. Wang preconditioner for L-matrices

In this paper, we improve the preconditioner, that introduced by H. Wang et al [6]. The H. Wang preconditioner \(P\in R^{n\times n}\) has only one non-zero, non-diagonal element in \(P_{n1}\) or \(P_{1n}\) , when \(a_{1n}a_{n1}\ne 0\) . But the new preconditioner has only one non-zero, non-diagonal element in \(P_{ij}\) or \(P_{ji}\) if \(a_{ij}a_{ji}\ne 0\), so the H. Wang preconditioner is a spacial case of the new preconditioner for L-matrices. Also we present two models to construct a better \(I+S\) type preconditioner for the \(AOR\) iterative method. Convergence analysis are given, numerical results are presented which show the effectiveness of the new preconditioners.

A scalable fully implicit method with adaptive time stepping for unsteady compressible inviscid flows

The class of fully implicit methods is drawing more attention in the simulation of fluid dynamics for engineering community, due to the allowance of large time steps in extreme-scale simulations. In this paper, we introduce and study a scalable fully implicit method for the numerical simulations of unsteady compressible inviscid flows governed by the compressible Euler equations. In the method, a cell-centered finite volume scheme together with the local Lax-Friedrichs (LLF) formula is used for the spatial discretization, and a backward differentiation formula is applied to integrate the Euler equations in time. The resultant nonlinear system at each time step is then solved by a parallel Newton-Krylov method with a domain decomposition type preconditioner. To improve the performance of the proposed method, we introduce an adaptive time stepping method which adjusts the time step size according to the initial residual of Newton iterations. Therefore, the proposed fully implicit solver overcomes the often severe limits on the time steps associated with existing methods. Numerical experiments validate that the approach is effective and robust for the simulations of several compressible inviscid flows. We also show that the newly developed algorithm scales well with more than one thousand processor cores for the problem with tens of millions of unknowns.

A multi-continuum multi-phase parallel simulator for large-scale conventional and unconventional reservoirs

Reservoir simulation is considered as the core of data analysis during the development of an oilfield. With higher requirements from reservoir engineers, high-resolution geological model problems are commonly used in reservoir simulation. To simulate this kind of large-scale reservoir problems, based on black-oil models and compositional model, a parallel simulator is developed in this paper. Cartesian and corner point grids are supported by our simulator to describe complex geology, including faults and pinch-outs. The multi-continuum models (including a dual-porosity model, a dual-porosity dual-permeability model, and a multiple interacting continua model) are used to simulate fractured reservoirs. Geomechanics effects are successfully involved through a flexible and convenient interface provided by our simulator. Krylov subspace linear solvers, restricted additive Schwarz method, incomplete LU factorization (ILU) preconditioner, and a family of CPR (constrained pressure residual)-type preconditioners are implemented to provide flexibility of the linear system solution processes. Moreover, a new adaptive preconditioning strategy, which can automatically select and change preconditioner between a CPR-type preconditioner and an ILU preconditioner, is designed to accelerate the solution processes and is considered as the most efficient preconditioning technique to our knowledge. MPI-based parallel implementation is employed for each module of the simulator. With the simulator we developed, various conventional and unconventional reservoir simulation problems with large-scale geological models can be achieved on parallel computers within practical computational time. The computational efficiency and parallel scalability are achieved by our high-quality parallel implementation and efficient nonlinear and linear solution techniques.

A new family of (I+S)-type preconditioner with some applications

In this article, a new family of preconditioned modeling for systems of linear equations has been introduced. Moreover, we present some theoretical analysis to compare with the existing methods. Numerical experiments of convection–diffusion equations and boundary value problems are also given to illustrate our assertions. These results show that the convergence rates of proposed methods are superior to the other modified iterative methods.