Extended Krylov Subspace Research Articles

A parallel power system linear model reduction method based on extended Krylov subspace

With the ever-increasing scale of power systems, stability analysis and control usually bear heavy storage and massive calculation burdens. In view of this, the model order reduction technique proves valuable by constructing a low-dimensional approximate model of the original system, which is crucial for efficiently handling large-scale systems. Balanced truncation (BT), a famous model reduction method, confronts practical limitations as it requires the systems to be stable and cannot deal with unstable models. Therefore, a parallel linear balanced truncation method for power systems based on extended Krylov subspace (EKS) is proposed in this work. Besides extending the BT method to unstable systems by α-shift, the key contribution also lies in strategies to enhance the convergence of the EKS method, whereupon the algorithm improvements include effective and efficient techniques for solving dual Lyapunov equations, and parallel acceleration of the singular value decomposition. The results of the simulation case verify that the proposed method can effectively improve the convergence of the EKS method by increasing α-shift, and the improvement work in this paper reduces the total time consumption of the BT method by about 26 %–33 % of the original, exhibiting better calculation efficiency. In addition, the case studies show that the simplified model still retains the time-domain and frequency-domain response characteristics of the original high-dimensional model.

A Flexible Extended Krylov Subspace Method for Approximating Markov Functions of Matrices

A flexible extended Krylov subspace method (F-EKSM) is considered for numerical approximation of the action of a matrix function f(A) to a vector b, where the function f is of Markov type. F-EKSM has the same framework as the extended Krylov subspace method (EKSM), replacing the zero pole in EKSM with a properly chosen fixed nonzero pole. For symmetric positive definite matrices, the optimal fixed pole is derived for F-EKSM to achieve the lowest possible upper bound on the asymptotic convergence factor, which is lower than that of EKSM. The analysis is based on properties of Faber polynomials of A and (I−A/s)−1. For large and sparse matrices that can be handled efficiently by LU factorizations, numerical experiments show that F-EKSM and a variant of RKSM based on a small number of fixed poles outperform EKSM in both storage and runtime, and usually have advantages over adaptive RKSM in runtime.

Fast electromigration stress analysis using Low-Rank Balanced Truncation for general interconnect and power grid structures

Electromigration (EM) has become one of the most significant challenges considering longterm reliability in integrated circuit design. The problem is caused by the large current density in circuit interconnections. However, in most cases, we are interested in the EM stress at specific points of the interconnect, such as vias, junctions and boundaries. As a result, Model Order Reduction (MOR) techniques can provide attractive methodologies to reduce the complexity of the original systems. System-theoretic techniques like Balanced Truncation (BT) offer very reliable bounds for the approximation error, compared to moment-matching methods. In this paper, we apply a computationally efficient Low-Rank BT procedure based on the extended Krylov subspace method, that can handle large-scale models and significantly accelerate the EM stress analysis for any general interconnect or power grid structure. For the experimental evaluation, we first consider a general structure interconnect tree to demonstrate the generalizability of the proposed method and then we employ our method on the industrial IBM power grid benchmarks where the its scalability is shown. For the latter, we demonstrate that our method can achieve a speedup up to 238× over a standard transient analysis method and a speedup up to 15× over COMSOL, while exhibiting negligible error.

On an integrated Krylov-ADI solver for large-scale Lyapunov equations

One of the most computationally expensive steps of the low-rank ADI method for large-scale Lyapunov equations is the solution of a shifted linear system at each iteration. We propose the use of the extended Krylov subspace method for this task. In particular, we illustrate how a single approximation space can be constructed to solve all the shifted linear systems needed to achieve a prescribed accuracy in terms of Lyapunov residual norm. Moreover, we show how to fully merge the two iterative procedures in order to obtain a novel, efficient implementation of the low-rank ADI method, for an important class of equations. Many state-of-the-art algorithms for the shift computation can be easily incorporated into our new scheme, as well. Several numerical results illustrate the potential of our novel procedure when compared to an implementation of the low-rank ADI method based on sparse direct solvers for the shifted linear systems.

Solving symmetric and positive definite second-order cone linear complementarity problem by a rational Krylov subspace method

The second-order cone linear complementarity problem (SOCLCP) is a generalization of the classical linear complementarity problem. It has been known that SOCLCP, with the globally uniquely solvable property, is essentially equivalent to a zero-finding problem in which the associated function bears much similarity to the transfer function in model reduction [38]. In this paper, we propose a new rational Krylov subspace method to solve the zero-finding problem for the symmetric and positive definite SOCLCP. The algorithm consists of two stages: first, it relies on an extended Krylov subspace to obtain an approximation of the zero root, and then applies multiple-pole rational Krylov subspace projections iteratively to acquire an accurate solution. Numerical evaluations on various types of SOCLCP examples demonstrate its efficiency and robustness.

An efficient extended block Arnoldi algorithm for feedback stabilization of incompressible Navier-Stokes flow problems

Navier-Stokes equations are well known in modeling of an incompressible Newtonian fluid, such as air or water. This system of equations is very complex due to the non-linearity term that characterizes it. After the linearization and the discretization parts, we get a descriptor system of index-2 described by a set of differential algebraic equations (DAEs). The two main parts we develop through this paper are focused firstly on constructing an efficient algorithm based on a projection technique onto an extended block Krylov subspace, that appropriately allows us to construct a reduced system of the original DAE system. Secondly, we solve a Linear Quadratic Regulator (LQR) problem based on a Riccati feedback approach. This approach uses numerical solutions of large-scale algebraic Riccati equations. To this end, we use the extended Krylov subspace method that allows us to project the initial large matrix problem onto a low order one that is solved by some direct methods. These numerical solutions are used to obtain a feedback matrix that will be used to stabilize the original system. We conclude by providing some numerical results to confirm the performance of our proposed method compared to other known methods.

Extended Krylov subspace methods for solving Sylvester and Stein tensor equations

<p style='text-indent:20px;'>This paper deals with Sylvester and Stein tensor equations with low rank right hand sides. It proposes extended Krylov-like methods for solving Sylvester and Stein tensor equations. The expressions of residual norms are presented. To show the performance of the proposed approaches, some numerical experiments are given.</p>

Frequency-Limited Reduction of Regular and Singular Circuit Models Via Extended Krylov Subspace Method

During the past decade, model order reduction (MOR) has become key enabler for the efficient simulation of large circuit models. MOR techniques based on balanced truncation (BT) offer very good error estimates and can provide compact models with any desired accuracy over the whole range of frequencies (from dc to infinity). However, in most applications the circuit is only intended to operate at specific frequency windows, which means that the reduced-order model can become unnecessarily large to achieve approximation over all frequencies. In this article, we present a frequency-limited approach which, combined with an efficient low-rank sparse implementation of the extended Krylov subspace (EKS) method, can handle large input models and provably leads to reduced-order models that are either smaller or exhibit better accuracy than full-frequency BT.

Extended nonsymmetric global Lanczos method for matrix function approximation

Extended Krylov subspace methods are attractive methods for computing approximations of matrix functions and other problems producing large-scale matrices. In this work, we propose the extended nonsymmetric global Lanczos method for solving some matrix approximation problems. The derived algorithm uses short recursive relations to generate bi-orthonormal bases, with respect to the Frobenius inner product, of the corresponding extended Krylov subspaces ${K^{e}_{m}}(A,V)$ and ${K^{e}_{m}}(A^{T},W)$ . Here, A is a large nonsymmetric matrix; V and $W\in \mathbb {R}^{n\times s}$ are two blocks. New algebraic properties of the proposed method are developed and applications to approximation of both WTf(A)V and trace(WTf(A)V ) are given. Numerical examples are presented to show the performance of the extended nonsymmetric global Lanczos for these problems.

Global extended Krylov subspace methods for large-scale differential Sylvester matrix equations

In this paper, we present a new numerical methods for solving large-scale differential Sylvester matrix equations with low rank right hand sides. These differential matrix equations appear in many applications such as robust control problems, model reduction problems and others. We present two approaches based on extended global Arnoldi process. The first one is based on approximating exponential matrix in the exact solution using the global extended Krylov method. The second one is based on a low-rank approximation of the solution of the corresponding Sylvester equation using the extended global Arnoldi algorithm. We give some theoretical results and report some numerical experiments to show the effectiveness of the proposed methods compared with the extended block Krylov method given in Hached and Jbilou (Numer Linear Algebra Appl 255:e2187, 2018).

Efficient IC hotspot thermal analysis via low-rank Model Order Reduction

Efficient full-chip thermal simulation is among the most challenging problems facing the EDA industry today, due to the need for solution of very large systems of equations that require unreasonably long computational times. However, in most cases, temperature is not required to be computed at every point of the IC but only at certain hotspots, in order to assess the circuit's compliance with thermal specifications. This makes the thermal analysis problem amenable to Model Order Reduction techniques. System-theoretic techniques like Balanced Truncation offer very reliable bounds for the approximation error, which can be used to control the order and accuracy of the reduced models during creation, at the expense of greater computational complexity to create them. In this paper, we propose a computationally efficient low-rank Balanced Truncation algorithm based on extended Krylov subspace method, which retains all the system-theoretic advantages in the reduction of model order for fast hotspot thermal simulation. Experimental results demonstrate around 97% order reduction and very tight accuracy bounds.

Krylov methods for low‐rank commuting generalized Sylvester equations

SummaryWe consider generalizations of the Sylvester matrix equation, consisting of the sum of a Sylvester operator and a linear operator Π with a particular structure. More precisely, the commutators of the matrix coefficients of the operator Π and the Sylvester operator coefficients are assumed to be matrices with low rank. We show (under certain additional conditions) low‐rank approximability of this problem, that is, the solution to this matrix equation can be approximated with a low‐rank matrix. Projection methods have successfully been used to solve other matrix equations with low‐rank approximability. We propose a new projection method for this class of matrix equations. The choice of the subspace is a crucial ingredient for any projection method for matrix equations. Our method is based on an adaption and extension of the extended Krylov subspace method for Sylvester equations. A constructive choice of the starting vector/block is derived from the low‐rank commutators. We illustrate the effectiveness of our method by solving large‐scale matrix equations arising from applications in control theory and the discretization of PDEs. The advantages of our approach in comparison to other methods are also illustrated.

Computationally enhanced projection methods for symmetric Sylvester and Lyapunov matrix equations

In the numerical treatment of large-scale Sylvester and Lyapunov equations, projection methods require solving a reduced problem to check convergence. As the approximation space expands, this solution takes an increasing portion of the overall computational effort. When data are symmetric, we show that the Frobenius norm of the residual matrix can be computed at significantly lower cost than with available methods, without explicitly solving the reduced problem. For certain classes of problems, the new residual norm expression combined with a memory-reducing device make classical Krylov strategies competitive with respect to more recent projection methods. Numerical experiments illustrate the effectiveness of the new implementation for standard and extended Krylov subspace methods.

A two-sided short-recurrence extended Krylov subspace method for nonsymmetric matrices and its relation to rational moment matching

We present an extended Krylov subspace analogue of the two-sided Lanczos method, i.e., a method which, given a nonsingular matrix A and vectors b, c with \(\left \langle {{\mathbf {b}},{\mathbf {c}}}\right \rangle \neq 0\), constructs bi-orthonormal bases of the extended Krylov subspaces \({\mathcal {E}}_{m}(A,{\mathbf {b}})\) and \({\mathcal {E}}_{m}(A^{T}\!,{\mathbf {c}})\) via short recurrences. We investigate the connection of the proposed method to rational moment matching for bilinear forms c T f(A)b, similar to known results connecting the two-sided Lanczos method to moment matching. Numerical experiments demonstrate the quality of the resulting approximations and the numerical behavior of the new extended Krylov subspace method.

Power System Dynamic Model Reduction Based on Extended Krylov Subspace Method

Modern power systems have experienced a significant increase in its complexity, and extremely large-scale system models have to be addressed in the study of stability analysis and control. Model reduction is a technique for developing an approximate system model with lower dimensions that shares similar properties to the original system. This work proposes a computationally efficient approach for linear system model reduction in large-scale power systems that is based on the balanced truncation method using an extended Krylov subspace technique. Key algorithm improvements, including sparsity handling of the linearized power system models and efficient computational techniques for solving dual Lyapunov equations, are discussed in detail. In addition, application of the proposed model reduction is extended to unstable systems, and a power system stabilizer (PSS) parameter optimization method based on reduced system models is used to validate the effectiveness of the proposed method. Case studies show that the resulting reduced models preserve the unique characteristics of the original high-dimensional models, including time and frequency domain responses as well as eigenvalues. Numerical results based on test systems with up to 12 $\thinspace$ 685 buses demonstrate the computational efficiency and validity of the proposed approach.

Monotone convergence of the extended Krylov subspace method for Laplace–Stieltjes functions of Hermitian positive definite matrices

The extended Krylov subspace method is known to be very efficient in many cases in which one wants to approximate the action of a matrix function f(A) on a vector b, in particular when f belongs to the class of Laplace–Stieltjes functions. We prove that the Euclidean norm of the error decreases strictly monotonically in this situation when A is Hermitian positive definite. Similar results are known for the (polynomial) Lanczos method for f(A)b, and we demonstrate how the techniques of proof used in the polynomial Krylov case can be transferred to the extended Krylov case.

A Non-Krylov Subspace Method for Solving Large and Sparse Linear System of Equations

AbstractMost current prevalent iterative methods can be classified into the socalled extended Krylov subspace methods, a class of iterative methods which do not fall into this category are also proposed in this paper. Comparing with traditional Krylov subspace methods which always depend on the matrix-vector multiplication with a fixed matrix, the newly introduced methods (the so-called (progressively) accumulated projection methods, or AP (PAP) for short) use a projection matrix which varies in every iteration to form a subspace from which an approximate solution is sought. More importantly an accelerative approach (called APAP) is introduced to improve the convergence of PAP method. Numerical experiments demonstrate some surprisingly improved convergence behavior. Comparison between benchmark extended Krylov subspace methods (Block Jacobi and GMRES) are made and one can also see remarkable advantage of APAP in some examples. APAP is also used to solve systems with extremely ill-conditioned coefficient matrix (the Hilbert matrix) and numerical experiments shows that it can bring very satisfactory results even when the size of system is up to a few thousands.

Efficient low-rank solution of generalized Lyapunov equations

An iterative method for the low-rank approximate solution of a class of generalized Lyapunov equations is studied. At each iteration, a standard Lyapunov equation is solved using Galerkin projection with an extended Krylov subspace method. This Lyapunov equation is solved inexactly, thus producing a nonstationary iteration. Several theoretical and computational issues are discussed so as to make the iteration efficient. Numerical experiments indicate that this method is competitive vis-a-vis the current state-of-the-art methods, both in terms of computational times and storage needs.

Extended Hamiltonian Hessenberg Matrices arise in Projection based Model Order Reduction

AbstractWe show that extended Hamiltonian Hessenberg matrices arise naturally in projection‐based model order reduction. Therefore we reduce a large dynamical system by projecting it on an extended Krylov subspace. The eigenvalues of the reduced order model can then be computed directly by applying the extended Hamiltonian QR algorithm. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

An Efficient Algorithm to Construct an Orthonormal Basis for the Extended Krylov Subspace

AbstractSubspace projection methods based on the Krylov subspace using powers of a matrix A have often been standard for solving large matrix computations in many areas of application. Recently, projection methods based on the extended Krylov subspace using powers of A and A−1 have attracted attention, particularly for functions of a matrix times a vector and matrix equations. In this article, we propose an efficient algorithm for constructing an orthonormal basis for the extended Krylov subspace. Numerical experiments indicate that this algorithm has less computational cost and approximately the same accuracy as the traditional algorithm.