Multi-order Arnoldi-based model order reduction of second-order time-delay systems

In this paper, we discuss the Krylov subspace-based model order reduction methods of second-order systems with time delays, and present two structure-preserving methods for model order reduction of these second-order systems, which avoid to convert the second-order systems into first-order ones. One method is based on a Krylov subspace by using the Taylor series expansion, the other method is based on the Laguerre series expansion. These two methods are used in the multi-order Arnoldi algorithm to construct the projection matrices. The resulting reduced models can not only preserve the structure of the original systems, but also can match a certain number of approximate moments or Laguerre expansion coefficients. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

基于Krylov-Schur重启技术的Arnoldi模型降阶方法

Krylov子空间模型降阶方法是模型降阶中的典型方法之一,Arnoldi模型降阶方法是这类方法中的一类基本方法。运用重正交化的Arnoldi算法得到[r]步Arnoldi分解;执行Krylov-Schur重启过程,导出基于Krylov-Schur重启技术的Arnoldi模型降阶方法。运用此方法对大规模线性时不变系统进行降阶,得到具有较高近似精度的稳定的降阶系统,从而改善了Krylov子空间降阶方法不能保持降阶系统稳定性的不足。数值算例验证了此方法是行之有效的。     

Structure preserving model reduction of port-Hamiltonian systems by moment matching at infinity

Model reduction of port-Hamiltonian systems by means of the Krylov methods is considered, aiming at port-Hamiltonian structure preservation. It is shown how to employ the Arnoldi method for model reduction in a particular coordinate system in order to preserve not only a specific number of the Markov parameters but also the port-Hamiltonian structure for the reduced order model. Furthermore it is shown how the Lanczos method can be applied in a structure preserving manner to a subclass of port-Hamiltonian systems which is characterized by an algebraic condition. In fact, for the same subclass of port-Hamiltonian systems the Arnoldi method and the Lanczos method turn out to be equivalent in the sense of producing reduced order port-Hamiltonian models with the same transfer function.     

Model-order reduction of large-scale second-order MIMO dynamical systems via a block second-order Arnoldi method

In this paper, we present a structure-preserving model-order reduction method for solving large-scale second-order MIMO dynamical systems. It is a projection method based on a block second-order Krylov subspace. We use the block second-order Arnoldi (BSOAR) method to generate an orthonormal basis of the projection subspace. The reduced system preserves the second-order structure of the original system. Some theoretical results are given. Numerical experiments report the effectiveness of this method.     

The Influence of Interface Conditions on Convergence of Krylov-Schwarz Domain Decomposition for the Advection-Diffusion Equation

Several variants of Schwarz domain decomposition, which differ in the choice of interface conditions, are studied in a finite volume context. Krylov subspace acceleration, GMRES in this paper, is used to accelerate convergence. Using a detailed investigation of the systems involved, we can minimize the memory requirements of GMRES acceleration. It is shown how Krylov subspace acceleration can be easily built on top of an already implemented Schwarz domain decomposition iteration, which makes Krylov-Schwarz algorithms easy to use in practice. The convergence rate is investigated both theoretically and experimentally. It is observed that the Krylov subspace accelerated algorithm is quite insensitive to the type of interface conditions employed.     

Krylov子空间方法及其并行计算

Krylov子空间方法在提高大型科学和工程计算效率上起着重要作用。本文阐述了Krylov子空间方．法产生的背景、Krylov子空间方法的分类,在此基础上,研完了分布式并行计算环境下Krylov子空间方法的并行计算方法,给出了Krylov子空间方法的并行化策略。     

A parameterised model order reduction method for parametric systems based on Laguerre polynomials

This paper presents a Laguerre polynomials-based parametrised model order reduction method for the parametric system in time domain. The method allows that the parametric dependence in system matrices is nonaffine. The method is presented via reducing an approximate polynomial parametric system based on Taylor expansion and Laguerre polynomials, resulting in a parametric reduced system that can accurately approximate the time response of the original parametric system over a wide range of parameter. The reduced parametric system obtained by proposed method can be implemented by two algorithms. Algorithm 1 is a direct way that is suitable for single-input multi-output parametric systems. Algorithm 2 is presented based on a connection to the Krylov subspace, which is efficient and suitable for multi-input multi-output parametric systems. The effectiveness of the proposed method is illustrated with two benchmarks in practical applications.     

Krylov子空间方法解线性方程组的并行性能分析及应用

许多并行计算问题,在结合并行机的特有体系结构时,要对算法的并行性能及其可扩展性进行分析。它决定了该算法解决有关问题是否有效,并进一步判断所用的并行计算系统是否符合求解问题的要求。文章通过对Ｋｒｙｌｏｖ子空间中两种有效算法－ＰＣＧ算法和ＧＭＲＥＳ（ｍ）算法在一类并行系统中形成的并行算法的性能进行了分析,给出了其求解问题规模与处理机数与加速比的关系结果表明。ＧＭＲＥＳ（ｍ）算法比ＰＣＧ算法更适合于并行。     

Short-recurrence Krylov subspace methods for the overlap Dirac operator at nonzero chemical potential

The overlap operator in lattice QCD requires the computation of the sign function of a matrix, which is non-Hermitian in the presence of a quark chemical potential. In previous work we introduced an Arnoldi-based Krylov subspace approximation, which uses long recurrences. Even after the deflation of critical eigenvalues, the low efficiency of the method restricts its application to small lattices. Here we propose new short-recurrence methods which strongly enhance the efficiency of the computational method. Using rational approximations to the sign function we introduce two variants, based on the restarted Arnoldi process and on the two-sided Lanczos method, respectively, which become very efficient when combined with multishift solvers. Alternatively, in the variant based on the two-sided Lanczos method the sign function can be evaluated directly. We present numerical results which compare the efficiencies of a restarted Arnoldi-based method and the direct two-sided Lanczos approximation for various lattice sizes. We also show that our new methods gain substantially when combined with deflation.     

Model order reduction of MIMO bilinear systems by multi-order Arnoldi method

In this paper, we present a time domain model order reduction method for multi-input multi-output (MIMO) bilinear systems by general orthogonal polynomials. The proposed method is based on a multi-order Arnoldi algorithm applied to construct the projection matrix. The resulting reduced model can match a desired number of expansion coefficient terms of the original system. The approximate error estimate of the reduced model is given. And we also briefly discuss the stability preservation of the reduced model in some cases. Additionally, in combination with Krylov subspace methods, we propose a two-sided projection method to generate reduced models which capture properties of the original system in the time and frequency domain simultaneously. The effectiveness of the proposed methods is demonstrated by two numerical examples.     

Application of block Krylov subspace algorithms to the Wilson-Dirac equation with multiple right-hand sides in lattice QCD

It is well known that the block Krylov subspace solvers work efficiently for some cases of the solution of differential equations with multiple right-hand sides. In lattice QCD calculation of physical quantities on a given configuration demands us to solve the Dirac equation with multiple sources. We show that a new block Krylov subspace algorithm recently proposed by the authors reduces the computational cost significantly without losing numerical accuracy for the solution of the O(a)-improved Wilson-Dirac equation.     

基于Krylov子空间的测向算法

提出一种基于Krylov子空间的信号波达方向估计算法,搜索信号可能入射角度,通过测试所构造的Krylov子空间与信号子空间的等价性来判断信号的DOA,算法用多级维纳滤波实现子空间分解。仿真实验表明,算法在低信噪比条件下对相邻信号有良好的谱分辨率和估计性能。     

解大规模非对称矩阵特征问题的精化Arnoldi方法的一种变形

§1.引言 传统的投影类方法是计算大规模非对称矩阵特征问题Ax=λx部分特征对的主要方法,它们包括Arnoldi方法、块Arnoldi方法、同时迭代法、Davidson方法和Jacobi-Davidson方法,贾提出的精化投影类方法目前被公认为是另一类重要     

Parallel Performance of Block ILU Preconditioners for a Block-tridiagonal Matrix

The parallelizable block ILU (incomplete LU) factorization preconditioners for a block-tridiagonal matrix have been recently proposed by the author. In this paper, we describe a parallelization of Krylov subspace methods with the block ILU factorization preconditioners on distributed-memory computers such as the Cray T3E, and then parallel performance results of a preconditioned Krylov subspace method are provided to evaluate the effectiveness and efficiency of the block ILU preconditioners on the Cray T3E.     

一种孔隙介质中地下水流并行计算方法

针对孔隙介质中地下水流动问题提出了一种并行数值计算方法,并基于此设计了一套专用于求解大规模三维地下水流动方程的并行计算模块。计算模块基于区域分解的方法实现对模型区域的并行求解,采用了分布式内存和压缩矩阵技术解决大规模稀疏矩阵的存储及其计算,整合多种并行Krylov子空间方法和预条件子技术迭代求解大规模线性方程组。在Linux集群系统上进行了数值模拟实验,性能测试结果表明,程序具有良好的加速比和可扩展性。     

Arnoldi-based model reduction for fractional order linear systems

In this paper, the Arnoldi-based model reduction methods are employed to fractional order linear time-invariant systems. The resulting model has a smaller dimension, while its fractional order is the same as that of the original system. The error and stability of the reduced model are discussed. And to overcome the local convergence of Padé approximation, the multi-point Arnoldi algorithm, which can recursively generate a reduced-order orthonormal basis from the corresponding Krylov subspace, is used. Numerical examples are given to illustrate the accuracy and efficiency of the proposed methods.     

A real‐time algorithm for nonlinear receding horizon control using multiple shooting and continuation/Krylov method

In this paper, we propose a real‐time algorithm for nonlinear receding horizon control using multiple shooting and the continuation/GMRES method. Multiple shooting is expected to improve numerical accuracy in calculations for solving boundary value problems. The continuation method is combined with a Krylov subspace method, GMRES, to update unknown quantities by solving a linear equation. At the same time, we apply condensing, which reduces the size of the linear equation, to speed up numerical calculations. A numerical example shows that both numerical accuracy and computational speed improve using the proposed algorithm by combining multiple shooting with condensing. Copyright © 2008 John Wiley & Sons, Ltd.     

A nested Krylov subspace method to compute the sign function of large complex matrices

We present an acceleration of the well-established Krylov–Ritz methods to compute the sign function of large complex matrices, as needed in lattice QCD simulations involving the overlap Dirac operator at both zero and nonzero baryon density. Krylov–Ritz methods approximate the sign function using a projection on a Krylov subspace. To achieve a high accuracy this subspace must be taken quite large, which makes the method too costly. The new idea is to make a further projection on an even smaller, nested Krylov subspace. If additionally an intermediate preconditioning step is applied, this projection can be performed without affecting the accuracy of the approximation, and a substantial gain in efficiency is achieved for both Hermitian and non-Hermitian matrices. The numerical efficiency of the method is demonstrated on lattice configurations of sizes ranging from 4⁴ to 10⁴, and the new results are compared with those obtained with rational approximation methods.     

The communication-hiding pipelined BiCGstab method for the parallel solution of large unsymmetric linear systems

A High Performance Computing alternative to traditional Krylov subspace methods, pipelined Krylov subspace solvers offer better scalability in the strong scaling limit compared to standard Krylov subspace methods for large and sparse linear systems. The typical synchronization bottleneck is mitigated by overlapping time-consuming global communication phases with local computations in the algorithm. This paper describes a general framework for deriving the pipelined variant of any Krylov subspace algorithm. The proposed framework was implicitly used to derive the pipelined Conjugate Gradient (p-CG) method in Hiding global synchronization latency in the preconditioned Conjugate Gradient algorithm by P. Ghysels and W. Vanroose, Parallel Computing, 40(7):224–238, 2014. The pipelining framework is subsequently illustrated by formulating a pipelined version of the BiCGStab method for the solution of large unsymmetric linear systems on parallel hardware. A residual replacement strategy is proposed to account for the possible loss of attainable accuracy and robustness by the pipelined BiCGStab method. It is shown that the pipelined algorithm improves scalability on distributed memory machines, leading to significant speedups compared to standard preconditioned BiCGStab.