Model order reduction of large-scale dynamical systems with Jacobi-Davidson style eigensolvers
|
|
- Ami Smith
- 5 years ago
- Views:
Transcription
1 MAX PLANCK INSTITUTE International Conference on Communications, Computing and Control Applications March 3-5, 2011, Hammamet, Tunisia. Model order reduction of large-scale dynamical systems with Jacobi-Davidson style eigensolvers P. Benner 1 M. E. Hochstenbach 2 P. Kürschner 1 1 Max Planck Institute for Dynamics of Complex Technical Systems Computational Methods in Systems and Control Theory 2 Technische Universiteit Eindhoven Centre for Analysis, Scientific computing and Applications FOR DYNAMICS OF COMPLEX TECHNICAL SYSTEMS MAGDEBURG Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 1/15
2 Outline 1 Introduction and scientific background 2 Modal approximation 3 Dominant pole computation of large-scale systems 4 Current and future research, enhancements and generalizations Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 2/15
3 Linear time invariant systems Basic definitions Linear Time Invariant System E ẋ(t) = A x(t) + B u(t) y(t) = C x(t) Coefficient matrices: system matrices A, E R n n, input matrix B R n m, output matrix C R p n. Involved functions: state / descriptor vector x(t) R n with initial condition x(t 0 ) = x 0, input / control u(t) R m, output y(t) R p. Some properties: n is the order of the system, E may be regular (state-space system) or singular (descriptor system). Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 3/15
4 Linear time invariant systems Applications and origins Linear Time Invariant System E ẋ(t) = A x(t) + B u(t) Electrical engineering v1 v2 g(v) g(v) vξ 1 vξ g(v) g(v) y(t) = C x(t) i = u(t) g(v) C C C C C Numerical mechanics Biological and chemical networks, power systems,... Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 4/15
5 Linear time invariant systems Applications and origins Linear Time Invariant System E ẋ(t) = A x(t) + B u(t) Electrical engineering v1 v2 g(v) g(v) vξ 1 vξ g(v) g(v) y(t) = C x(t) i = u(t) g(v) C C C C C Simulation Numerical mechanics Optimization Stabilization Biological and chemical networks, power systems,... Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 4/15
6 Linear time invariant systems Model order reduction Large-Scale LTI System E ẋ(t) = A x(t) + B u(t) y(t) = C x(t) Problem Modern applications lead to large system orders n, e.g., n 10 5 or higher. Computational costs, e.g. for simulation purposes, increase drastically. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 5/15
7 Linear time invariant systems Model order reduction Large-Scale LTI System E ẋ(t) = A x(t) + B u(t) y(t) = C x(t) Model Order Reduction Solution Apply model order reduction to generate approximate system of low order k n with: Ã, Ẽ R k k, B R k m, C R p k, x(t) R k, u(t) R m, and ỹ(t) R p s.t. ỹ(t) y(t). Reduced Order Model Ẽ x(t) = Ã x(t) + B u(t) ỹ(t) = C x(t) Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 5/15
8 Modal approximation Eigenvalues and eigenvectors The right and left eigenvectors corresponding to a selected subset λ 1,..., λ k of eigenvalues of (A, E) satisfy the generalized eigenvalue problems: { Axj = λ j Ex j, x j 0 yj H A = λ j yj H ; j = 1,..., k. E, y j 0 Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 6/15
9 Modal approximation Eigenvalues and eigenvectors The right and left eigenvectors corresponding to a selected subset λ 1,..., λ k of eigenvalues of (A, E) satisfy the generalized eigenvalue problems: { Axj = λ j Ex j, x j 0 yj H A = λ j yj H ; j = 1,..., k. E, y j 0 Modal approximation uses X := [x 1,..., x k ], Y := [y 1,..., y k ] C n k as transformation matrices to get the reduced order model via: Y H E X x(t) = Y H A X x(t) + Y H B u(t); ỹ(t) = C X x(t) Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 6/15
10 Modal approximation Eigenvalues and eigenvectors The right and left eigenvectors corresponding to a selected subset λ 1,..., λ k of eigenvalues of (A, E) satisfy the generalized eigenvalue problems: { Axj = λ j Ex j, x j 0 yj H A = λ j yj H ; j = 1,..., k. E, y j 0 Modal approximation uses X := [x 1,..., x k ], Y := [y 1,..., y k ] C n k as transformation matrices to get the reduced order model via: Y H E X x(t) = Y H A X x(t) + Y H B u(t); ỹ(t) = C X x(t) Ẽ x(t) = Ã x(t) + B u(t); ỹ(t) = C x(t) Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 6/15
11 Modal approximation Dominant poles If (A, E) is regular and nondefective, the transfer function is given for s C by H(s) = C(sE A) 1 B = n f j=1 with residues R j := (Cx j )(yj H B) C p m. If R j s λ j + R R j 2 Re (λ j ) > R i 2 Re (λ i ), i j, i, j = 1,..., n f, then λ j is a dominant pole and (λ j, x j, y j ) a dominant eigentriplet. [Martins/Lima/Pinto 96, Rommes/Martins 06] Dominant poles significantly contribute to the system dynamics and cause peaks near Im (λ j ) in the frequency response plot. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 7/15
12 Modal approximation Dominant poles Transfer function of artificial test system with E = I, blockdiagonal A and random B, C T R n. exact model, n = H(iω) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
13 Modal approximation Dominant poles Transfer function of artificial test system with E = I, blockdiagonal A and random B, C T R n. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
14 Modal approximation Dominant poles Transfer function of artificial test system with E = I, blockdiagonal A and random B, C T R n. 10 exact model, n = 217 Im (λ) of dominant poles H(iω) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
15 Modal approximation Dominant poles Transfer function of artificial test system with E = I, blockdiagonal A and random B, C T R n. 10 exact model, n = 217 Im (λ) of dominant poles reduced, dom. poles, k = 21 H(iω) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
16 Modal approximation Dominant poles Transfer function of artificial test system with E = I, blockdiagonal A and random B, C T R n. 10 exact model, n = 217 Im (λ) of dominant poles reduced, dom. poles, k = 21 reduced, smallest Re (λ), k = 21 H(iω) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
17 Modal approximation Dominant poles Necessary Ingredient Transfer function of artificial test system with E = I, blockdiagonal A Algorithms and random B, for Ccomputing T R n. eigentriplets of large-scale, possibly nonnormal matrices are required. exact model, n = 217 Due to their cubic complexity andim memory (λ) of dominant requirements, poles direct methods (QR / QZ algorithm) reduced, are out dom. of the poles, game. k = reduced, smallest Re (λ), k = 21 One possible way out: Iterative projection based eigenvalue algorithms for the two-sided eigenvalue problem. H(iω) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 8/15
18 Dominant pole computation of large-scale systems Two-sided eigenvalue methods General two-sided scheme Project (A, E) onto low-dimensional subspaces V = colsp(v ), V C n k, W = colsp(w ), W C n k, k n: ( ) W H ( A, E V = S, T ). Compute most dominant eigentriplet (θ, q, z) of (S, T ) and W H B, VC using methods for small matrices. Approximate dominant eigentriplet of (A, E) is (θ, v := Vq, w := Wz) with residuals r 1 := Av θev, r 2 := A T w θe T w. YES: eigentriplet (θ, v, w) converged! Are r 1, r 2 sufficiently small? NO: expand the subspaces V, W by some appropriate new basis vectors s, t and goto 1. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 9/15
19 Dominant pole computation of large-scale systems Two-sided eigenvalue methods Different subspace expansions s, t lead to different methods. Two-sided Jacobi-Davidson, bi-e-orthogonal version [Stathopoulos 02, Hochstenbach/Sleijpen 03] Solve s, t approximately from the correction equations ( I Evw H ) (A θe) ( I vw H E ) s = r 1, ( I E T wv H) (A θe) H ( I wv H E T ) t = r 2. Dominant Pole Algorithm [Martins et al 96, Rommes et al 06/ 08] Solve s, t from (θe A)s = b, (θe A) H t = c. Rayleigh Quotient Iteration [Ostrowski 59, Parlett 74] Solve s, t from (A θe)s = Ev, (A θe) H t = E T w. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 10/15
20 Dominant pole computation of large-scale systems Exact Solution Results for PEEC model of a patch antenna structure from the NICONET benchmark collection. 20 H(iω) 2 (db) exact n = 408 reduced k = ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 11/15
21 Dominant pole computation of large-scale systems Exact Solution Results for PEEC model of a patch antenna structure from the NICONET benchmark collection H(iω) H red (iω) 2 H(iω) 2 Relative error ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 11/15
22 Dominant pole computation of large-scale systems Iterative and inexact solution Assume (A, E) is really large-scale, such that exact solves with A θe are infeasible. Jacobi-Davidson style methods are known to be usually more robust with respect to inexact solves. [Voss 07] Apply, e.g., a limited number of steps of an iterative solver [GMRES, BiCG,...] to the correction equation. Requires appropriately projected preconditioner K p := ( I Evw H) K ( I vw H E ) with K A θe invertible. Application of K p given by: ( Kp 1 = I [K 1 (Ev)]w H ) E w H E[K 1 K 1. (Ev)] [Fokkema/Sleijpen/Van der Vorst 98, Rommes 07] Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 12/15
23 Dominant pole computation of large-scale systems Iterative and inexact solution preconditioning Often desired goal in large-scale eigenvalue computation: find eigenvalues close to τ C. use fixed preconditioner K A τe during whole iteration. Λ(A, E) target τ Im Re Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 13/15
24 Dominant pole computation of large-scale systems Iterative and inexact solution preconditioning Often desired goal in large-scale eigenvalue computation: find eigenvalues close to τ C. use fixed preconditioner K A τe during whole iteration. Dominant poles are often scattered in C. variable preconditioner K A θe might be required. Λ(A, E) target τ Λ(A, E) dom. poles Im Im Re Re Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 13/15
25 Dominant pole computation of large-scale systems Iterative and inexact solution Results for FDM model of semi-discretized heat equation with convection-reaction taken from LyaPack. Approximate solution of correction equation using GMRES until r GMRES < 10 3 or 25 steps are processed. Preconditioner: incomplete LU-factorization K = LŨ A θi with drop tolerance If r GMRES > 10 3 after 25 2 = 13 steps, K is updated for the next iteration of two-sided JD. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 14/15
26 Dominant pole computation of large-scale systems Iterative and inexact solution Results for FDM model of semi-discretized heat equation with convection-reaction taken from LyaPack. 40 H(iω) 2 (db) exact n = reduced k = ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 14/15
27 Dominant pole computation of large-scale systems Iterative and inexact solution Results for FDM model of semi-discretized heat equation with convection-reaction taken from LyaPack H(iω) H red (iω) 2 H(iω) 2 Relative error ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 14/15
28 Current and future research, enhancements and generalizations Efficient preconditioning: Which kind of preconditioner (esp. difficult if E is singular)? When should K be renewed / updated? Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
29 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
30 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Generalizations to second order systems Mẍ(t) + Dẋ(t) + Kx(t) = Bu(t), y(t) = Cx(t). Exact solves Quadratic DPA [Martins/Rommes 08] Inexact solves Quadratic JD [Booten et al 96, Hochstenbach/Sleijpen 03] Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
31 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Generalizations to second order systems. Combinations of modal approximation with other MOR approaches: dominant poles as (additional) interpolation points for Krylov subspace methods : j max { X = K q (A σj E) 1 E, (A σ j E) 1 B }, j=1 j max { Y = K q (A σj E) H E T, (A σ j E) H C T }. j=1 [Grimme 97, Rommes 07] Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
32 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Generalizations to second order systems. Combinations of modal approximation with other MOR approaches: dominant poles as (additional) interpolation points for Krylov subspace methods. dominant poles as (additional) shift parameters for ADI method used to solve Lyapunov equations for balanced truncation: Re (p i ) ( Z j = I (pj + p j 1 )(A + p j E) 1 E ) Zj 1 Re (p i 1 ) [See our talk at MODRED 2010, Dec. 2-4, Berlin, available at Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
33 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Generalizations to second order systems. Combinations of modal approximation with other MOR approaches. H(iω) original, n = BT, k = 160, (ritz values) BT, k = 160, (ritz + d.p.) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
34 Current and future research, enhancements and generalizations Efficient preconditioning. Robust handling of occurring oblique projectors I Evw H. Generalizations to second order systems. Combinations of modal approximation with other MOR approaches. relative error BT, k = 160, (ritz values) BT, k = 160, (ritz + d.p.) ω Max Planck Institute Magdeburg P. Kürschner, MOR with Jacobi-Davidson 15/15
Model order reduction of large-scale dynamical systems with Jacobi-Davidson style eigensolvers Benner, P.; Hochstenbach, M.E.; Kürschner, P.
Model order reduction of large-scale dynamical systems with Jacobi-Davidson style eigensolvers Benner, P.; Hochstenbach, M.E.; Kürschner, P. Published: 01/01/2011 Document Version Publisher s PDF, also
More informationTwo-sided Eigenvalue Algorithms for Modal Approximation
Two-sided Eigenvalue Algorithms for Modal Approximation Master s thesis submitted to Faculty of Mathematics at Chemnitz University of Technology presented by: Supervisor: Advisor: B.sc. Patrick Kürschner
More informationNumerical Methods for Large Scale Eigenvalue Problems
MAX PLANCK INSTITUTE Summer School in Trogir, Croatia Oktober 12, 2011 Numerical Methods for Large Scale Eigenvalue Problems Patrick Kürschner Max Planck Institute for Dynamics of Complex Technical Systems
More informationPreconditioned inverse iteration and shift-invert Arnoldi method
Preconditioned inverse iteration and shift-invert Arnoldi method Melina Freitag Department of Mathematical Sciences University of Bath CSC Seminar Max-Planck-Institute for Dynamics of Complex Technical
More informationCOMPUTING DOMINANT POLES OF TRANSFER FUNCTIONS
COMPUTING DOMINANT POLES OF TRANSFER FUNCTIONS GERARD L.G. SLEIJPEN AND JOOST ROMMES Abstract. The transfer function describes the response of a dynamical system to periodic inputs. Dominant poles are
More informationComputing Transfer Function Dominant Poles of Large Second-Order Systems
Computing Transfer Function Dominant Poles of Large Second-Order Systems Joost Rommes Mathematical Institute Utrecht University rommes@math.uu.nl http://www.math.uu.nl/people/rommes joint work with Nelson
More informationSubspace accelerated DPA and Jacobi-Davidson style methods
Chapter 3 Subspace accelerated DPA and Jacobi-Davidson style methods Abstract. This chapter describes a new algorithm for the computation of the dominant poles of a high-order scalar transfer function.
More informationDavidson Method CHAPTER 3 : JACOBI DAVIDSON METHOD
Davidson Method CHAPTER 3 : JACOBI DAVIDSON METHOD Heinrich Voss voss@tu-harburg.de Hamburg University of Technology The Davidson method is a popular technique to compute a few of the smallest (or largest)
More informationLecture 3: Inexact inverse iteration with preconditioning
Lecture 3: Department of Mathematical Sciences CLAPDE, Durham, July 2008 Joint work with M. Freitag (Bath), and M. Robbé & M. Sadkane (Brest) 1 Introduction 2 Preconditioned GMRES for Inverse Power Method
More informationITERATIVE PROJECTION METHODS FOR SPARSE LINEAR SYSTEMS AND EIGENPROBLEMS CHAPTER 11 : JACOBI DAVIDSON METHOD
ITERATIVE PROJECTION METHODS FOR SPARSE LINEAR SYSTEMS AND EIGENPROBLEMS CHAPTER 11 : JACOBI DAVIDSON METHOD Heinrich Voss voss@tu-harburg.de Hamburg University of Technology Institute of Numerical Simulation
More informationOn Dominant Poles and Model Reduction of Second Order Time-Delay Systems
On Dominant Poles and Model Reduction of Second Order Time-Delay Systems Maryam Saadvandi Joint work with: Prof. Karl Meerbergen and Dr. Elias Jarlebring Department of Computer Science, KULeuven ModRed
More informationInexact inverse iteration with preconditioning
Department of Mathematical Sciences Computational Methods with Applications Harrachov, Czech Republic 24th August 2007 (joint work with M. Robbé and M. Sadkane (Brest)) 1 Introduction 2 Preconditioned
More informationEfficient computation of transfer function dominant poles of large second-order dynamical systems
Chapter 6 Efficient computation of transfer function dominant poles of large second-order dynamical systems Abstract This chapter presents a new algorithm for the computation of dominant poles of transfer
More informationDomain decomposition on different levels of the Jacobi-Davidson method
hapter 5 Domain decomposition on different levels of the Jacobi-Davidson method Abstract Most computational work of Jacobi-Davidson [46], an iterative method suitable for computing solutions of large dimensional
More informationAlgorithms for eigenvalue problems arising in model reduction
Algorithms for eigenvalue problems arising in model reduction Joost Rommes Mentor Graphics MorTrans 2015, Berlin May 19, 2015 Introduction Eigenvalue problems Stability analysis and spurious eigenvalues
More information1. Introduction. In this paper we consider the large and sparse eigenvalue problem. Ax = λx (1.1) T (λ)x = 0 (1.2)
A NEW JUSTIFICATION OF THE JACOBI DAVIDSON METHOD FOR LARGE EIGENPROBLEMS HEINRICH VOSS Abstract. The Jacobi Davidson method is known to converge at least quadratically if the correction equation is solved
More informationA Jacobi Davidson Method with a Multigrid Solver for the Hermitian Wilson-Dirac Operator
A Jacobi Davidson Method with a Multigrid Solver for the Hermitian Wilson-Dirac Operator Artur Strebel Bergische Universität Wuppertal August 3, 2016 Joint Work This project is joint work with: Gunnar
More informationEfficient computation of multivariable transfer function dominant poles
Chapter 4 Efficient computation of multivariable transfer function dominant poles Abstract. This chapter describes a new algorithm to compute the dominant poles of a high-order multi-input multi-output
More informationA Jacobi Davidson Method for Nonlinear Eigenproblems
A Jacobi Davidson Method for Nonlinear Eigenproblems Heinrich Voss Section of Mathematics, Hamburg University of Technology, D 21071 Hamburg voss @ tu-harburg.de http://www.tu-harburg.de/mat/hp/voss Abstract.
More informationEigenvalue Problems CHAPTER 1 : PRELIMINARIES
Eigenvalue Problems CHAPTER 1 : PRELIMINARIES Heinrich Voss voss@tu-harburg.de Hamburg University of Technology Institute of Mathematics TUHH Heinrich Voss Preliminaries Eigenvalue problems 2012 1 / 14
More informationHOMOGENEOUS JACOBI DAVIDSON. 1. Introduction. We study a homogeneous Jacobi Davidson variant for the polynomial eigenproblem
HOMOGENEOUS JACOBI DAVIDSON MICHIEL E. HOCHSTENBACH AND YVAN NOTAY Abstract. We study a homogeneous variant of the Jacobi Davidson method for the generalized and polynomial eigenvalue problem. Special
More informationRANA03-02 January Jacobi-Davidson methods and preconditioning with applications in pole-zero analysis
RANA03-02 January 2003 Jacobi-Davidson methods and preconditioning with applications in pole-zero analysis by J.Rommes, H.A. van der Vorst, EJ.W. ter Maten Reports on Applied and Numerical Analysis Department
More informationEIGIFP: A MATLAB Program for Solving Large Symmetric Generalized Eigenvalue Problems
EIGIFP: A MATLAB Program for Solving Large Symmetric Generalized Eigenvalue Problems JAMES H. MONEY and QIANG YE UNIVERSITY OF KENTUCKY eigifp is a MATLAB program for computing a few extreme eigenvalues
More informationA Jacobi-Davidson method for two real parameter nonlinear eigenvalue problems arising from delay differential equations
A Jacobi-Davidson method for two real parameter nonlinear eigenvalue problems arising from delay differential equations Heinrich Voss voss@tuhh.de Joint work with Karl Meerbergen (KU Leuven) and Christian
More informationCONTROLLING INNER ITERATIONS IN THE JACOBI DAVIDSON METHOD
CONTROLLING INNER ITERATIONS IN THE JACOBI DAVIDSON METHOD MICHIEL E. HOCHSTENBACH AND YVAN NOTAY Abstract. The Jacobi Davidson method is an eigenvalue solver which uses the iterative (and in general inaccurate)
More informationMethods for eigenvalue problems with applications in model order reduction
Methods for eigenvalue problems with applications in model order reduction Methoden voor eigenwaardeproblemen met toepassingen in model orde reductie (met een samenvatting in het Nederlands) Proefschrift
More informationMICHIEL E. HOCHSTENBACH
VARIATIONS ON HARMONIC RAYLEIGH RITZ FOR STANDARD AND GENERALIZED EIGENPROBLEMS MICHIEL E. HOCHSTENBACH Abstract. We present several variations on the harmonic Rayleigh Ritz method. First, we introduce
More informationPolynomial Jacobi Davidson Method for Large/Sparse Eigenvalue Problems
Polynomial Jacobi Davidson Method for Large/Sparse Eigenvalue Problems Tsung-Ming Huang Department of Mathematics National Taiwan Normal University, Taiwan April 28, 2011 T.M. Huang (Taiwan Normal Univ.)
More informationAlternative correction equations in the Jacobi-Davidson method
Chapter 2 Alternative correction equations in the Jacobi-Davidson method Menno Genseberger and Gerard Sleijpen Abstract The correction equation in the Jacobi-Davidson method is effective in a subspace
More informationIterative methods for symmetric eigenvalue problems
s Iterative s for symmetric eigenvalue problems, PhD McMaster University School of Computational Engineering and Science February 11, 2008 s 1 The power and its variants Inverse power Rayleigh quotient
More informationControlling inner iterations in the Jacobi Davidson method
Controlling inner iterations in the Jacobi Davidson method Michiel E. Hochstenbach and Yvan Notay Service de Métrologie Nucléaire, Université Libre de Bruxelles (C.P. 165/84), 50, Av. F.D. Roosevelt, B-1050
More informationMODEL REDUCTION BY A CROSS-GRAMIAN APPROACH FOR DATA-SPARSE SYSTEMS
MODEL REDUCTION BY A CROSS-GRAMIAN APPROACH FOR DATA-SPARSE SYSTEMS Ulrike Baur joint work with Peter Benner Mathematics in Industry and Technology Faculty of Mathematics Chemnitz University of Technology
More informationHARMONIC RAYLEIGH RITZ EXTRACTION FOR THE MULTIPARAMETER EIGENVALUE PROBLEM
HARMONIC RAYLEIGH RITZ EXTRACTION FOR THE MULTIPARAMETER EIGENVALUE PROBLEM MICHIEL E. HOCHSTENBACH AND BOR PLESTENJAK Abstract. We study harmonic and refined extraction methods for the multiparameter
More informationof dimension n 1 n 2, one defines the matrix determinants
HARMONIC RAYLEIGH RITZ FOR THE MULTIPARAMETER EIGENVALUE PROBLEM MICHIEL E. HOCHSTENBACH AND BOR PLESTENJAK Abstract. We study harmonic and refined extraction methods for the multiparameter eigenvalue
More informationModel Reduction for Unstable Systems
Model Reduction for Unstable Systems Klajdi Sinani Virginia Tech klajdi@vt.edu Advisor: Serkan Gugercin October 22, 2015 (VT) SIAM October 22, 2015 1 / 26 Overview 1 Introduction 2 Interpolatory Model
More informationA Newton-Galerkin-ADI Method for Large-Scale Algebraic Riccati Equations
A Newton-Galerkin-ADI Method for Large-Scale Algebraic Riccati Equations Peter Benner Max-Planck-Institute for Dynamics of Complex Technical Systems Computational Methods in Systems and Control Theory
More informationLARGE SPARSE EIGENVALUE PROBLEMS. General Tools for Solving Large Eigen-Problems
LARGE SPARSE EIGENVALUE PROBLEMS Projection methods The subspace iteration Krylov subspace methods: Arnoldi and Lanczos Golub-Kahan-Lanczos bidiagonalization General Tools for Solving Large Eigen-Problems
More informationSolution of eigenvalue problems. Subspace iteration, The symmetric Lanczos algorithm. Harmonic Ritz values, Jacobi-Davidson s method
Solution of eigenvalue problems Introduction motivation Projection methods for eigenvalue problems Subspace iteration, The symmetric Lanczos algorithm Nonsymmetric Lanczos procedure; Implicit restarts
More informationJacobi-Davidson methods and preconditioning with applications in pole-zero analysis Rommes, J.; Vorst, van der, H.A.; ter Maten, E.J.W.
Jacobi-Davidson methods and preconditioning with applications in pole-zero analysis Rommes, J.; Vorst, van der, H.A.; ter Maten, E.J.W. Published: 01/01/2003 Document Version Publisher s PDF, also known
More informationLARGE SPARSE EIGENVALUE PROBLEMS
LARGE SPARSE EIGENVALUE PROBLEMS Projection methods The subspace iteration Krylov subspace methods: Arnoldi and Lanczos Golub-Kahan-Lanczos bidiagonalization 14-1 General Tools for Solving Large Eigen-Problems
More informationApproximation of the Linearized Boussinesq Equations
Approximation of the Linearized Boussinesq Equations Alan Lattimer Advisors Jeffrey Borggaard Serkan Gugercin Department of Mathematics Virginia Tech SIAM Talk Series, Virginia Tech, April 22, 2014 Alan
More informationA Domain Decomposition Based Jacobi-Davidson Algorithm for Quantum Dot Simulation
A Domain Decomposition Based Jacobi-Davidson Algorithm for Quantum Dot Simulation Tao Zhao 1, Feng-Nan Hwang 2 and Xiao-Chuan Cai 3 Abstract In this paper, we develop an overlapping domain decomposition
More informationCONTROLLING INNER ITERATIONS IN THE JACOBI DAVIDSON METHOD
CONTROLLING INNER ITERATIONS IN THE JACOBI DAVIDSON METHOD MICHIEL E. HOCHSTENBACH AND YVAN NOTAY Abstract. The Jacobi Davidson method is an eigenvalue solver which uses an inner-outer scheme. In the outer
More informationSolution of eigenvalue problems. Subspace iteration, The symmetric Lanczos algorithm. Harmonic Ritz values, Jacobi-Davidson s method
Solution of eigenvalue problems Introduction motivation Projection methods for eigenvalue problems Subspace iteration, The symmetric Lanczos algorithm Nonsymmetric Lanczos procedure; Implicit restarts
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods; Preconditioning
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods; Preconditioning Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 18 Outline
More informationJacobi-Davidson Algorithm for Locating Resonances in a Few-Body Tunneling System
Jacobi-Davidson Algorithm for Locating Resonances in a Few-Body Tunneling System Bachelor Thesis in Physics and Engineering Physics Gustav Hjelmare Jonathan Larsson David Lidberg Sebastian Östnell Department
More informationADI-preconditioned FGMRES for solving large generalized Lyapunov equations - A case study
-preconditioned for large - A case study Matthias Bollhöfer, André Eppler TU Braunschweig Institute Computational Mathematics Syrene-MOR Workshop, TU Hamburg October 30, 2008 2 / 20 Outline 1 2 Overview
More informationEINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science. CASA-Report November 2013
EINDHOVEN UNIVERSITY OF TECHNOLOGY Department of Mathematics and Computer Science CASA-Report 13-26 November 213 Polynomial optimization and a Jacobi-Davidson type method for commuting matrices by I.W.M.
More informationTowards high performance IRKA on hybrid CPU-GPU systems
Towards high performance IRKA on hybrid CPU-GPU systems Jens Saak in collaboration with Georg Pauer (OVGU/MPI Magdeburg) Kapil Ahuja, Ruchir Garg (IIT Indore) Hartwig Anzt, Jack Dongarra (ICL Uni Tennessee
More informationModel Reduction for Linear Dynamical Systems
Summer School on Numerical Linear Algebra for Dynamical and High-Dimensional Problems Trogir, October 10 15, 2011 Model Reduction for Linear Dynamical Systems Peter Benner Max Planck Institute for Dynamics
More informationA Jacobi Davidson type method for the product eigenvalue problem
A Jacobi Davidson type method for the product eigenvalue problem Michiel E Hochstenbach Abstract We propose a Jacobi Davidson type method to compute selected eigenpairs of the product eigenvalue problem
More informationPreconditioned Locally Minimal Residual Method for Computing Interior Eigenpairs of Symmetric Operators
Preconditioned Locally Minimal Residual Method for Computing Interior Eigenpairs of Symmetric Operators Eugene Vecharynski 1 Andrew Knyazev 2 1 Department of Computer Science and Engineering University
More informationIterative methods for Linear System
Iterative methods for Linear System JASS 2009 Student: Rishi Patil Advisor: Prof. Thomas Huckle Outline Basics: Matrices and their properties Eigenvalues, Condition Number Iterative Methods Direct and
More informationJacobi s Ideas on Eigenvalue Computation in a modern context
Jacobi s Ideas on Eigenvalue Computation in a modern context Henk van der Vorst vorst@math.uu.nl Mathematical Institute Utrecht University June 3, 2006, Michel Crouzeix p.1/18 General remarks Ax = λx Nonlinear
More informationEINDHOVEN UNIVERSITY OF TECHNOLOGY Department ofmathematics and Computing Science
EINDHOVEN UNIVERSITY OF TECHNOLOGY Department ofmathematics and Computing Science RANA03-15 July 2003 The application of preconditioned Jacobi-Davidson methods in pole-zero analysis by J. Rommes, C.W.
More informationAn Arnoldi Method for Nonlinear Symmetric Eigenvalue Problems
An Arnoldi Method for Nonlinear Symmetric Eigenvalue Problems H. Voss 1 Introduction In this paper we consider the nonlinear eigenvalue problem T (λ)x = 0 (1) where T (λ) R n n is a family of symmetric
More informationInexact Solves in Krylov-based Model Reduction
Inexact Solves in Krylov-based Model Reduction Christopher A. Beattie and Serkan Gugercin Abstract We investigate the use of inexact solves in a Krylov-based model reduction setting and present the resulting
More informationA Tuned Preconditioner for Inexact Inverse Iteration for Generalised Eigenvalue Problems
A Tuned Preconditioner for for Generalised Eigenvalue Problems Department of Mathematical Sciences University of Bath, United Kingdom IWASEP VI May 22-25, 2006 Pennsylvania State University, University
More informationarxiv: v3 [math.na] 6 Jul 2018
A Connection Between Time Domain Model Order Reduction and Moment Matching for LTI Systems arxiv:181.785v3 [math.na] 6 Jul 218 Manuela Hund a and Jens Saak a a Max Planck Institute for Dynamics of Complex
More informationA Tuned Preconditioner for Inexact Inverse Iteration Applied to Hermitian Eigenvalue Problems
A Tuned Preconditioner for Applied to Eigenvalue Problems Department of Mathematical Sciences University of Bath, United Kingdom IWASEP VI May 22-25, 2006 Pennsylvania State University, University Park
More informationAlternative correction equations in the Jacobi-Davidson method. Mathematical Institute. Menno Genseberger and Gerard L. G.
Universiteit-Utrecht * Mathematical Institute Alternative correction equations in the Jacobi-Davidson method by Menno Genseberger and Gerard L. G. Sleijpen Preprint No. 173 June 1998, revised: March 1999
More informationSolving Large Nonlinear Sparse Systems
Solving Large Nonlinear Sparse Systems Fred W. Wubs and Jonas Thies Computational Mechanics & Numerical Mathematics University of Groningen, the Netherlands f.w.wubs@rug.nl Centre for Interdisciplinary
More informationBALANCING-RELATED MODEL REDUCTION FOR DATA-SPARSE SYSTEMS
BALANCING-RELATED Peter Benner Professur Mathematik in Industrie und Technik Fakultät für Mathematik Technische Universität Chemnitz Computational Methods with Applications Harrachov, 19 25 August 2007
More informationIterative projection methods for sparse nonlinear eigenvalue problems
Iterative projection methods for sparse nonlinear eigenvalue problems Heinrich Voss voss@tu-harburg.de Hamburg University of Technology Institute of Mathematics TUHH Heinrich Voss Iterative projection
More informationA comparison of solvers for quadratic eigenvalue problems from combustion
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS [Version: 2002/09/18 v1.01] A comparison of solvers for quadratic eigenvalue problems from combustion C. Sensiau 1, F. Nicoud 2,, M. van Gijzen 3,
More information6.4 Krylov Subspaces and Conjugate Gradients
6.4 Krylov Subspaces and Conjugate Gradients Our original equation is Ax = b. The preconditioned equation is P Ax = P b. When we write P, we never intend that an inverse will be explicitly computed. P
More informationPolynomial optimization and a Jacobi Davidson type method for commuting matrices
Polynomial optimization and a Jacobi Davidson type method for commuting matrices Ivo W. M. Bleylevens a, Michiel E. Hochstenbach b, Ralf L. M. Peeters a a Department of Knowledge Engineering Maastricht
More informationLinear Algebra and its Applications
Linear Algebra and its Applications 431 (2009) 471 487 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: www.elsevier.com/locate/laa A Jacobi Davidson type
More informationA JACOBI-DAVIDSON ITERATION METHOD FOR LINEAR EIGENVALUE PROBLEMS. GERARD L.G. SLEIJPEN y AND HENK A. VAN DER VORST y
A JACOBI-DAVIDSON ITERATION METHOD FOR LINEAR EIGENVALUE PROBLEMS GERARD L.G. SLEIJPEN y AND HENK A. VAN DER VORST y Abstract. In this paper we propose a new method for the iterative computation of a few
More informationIterative methods for Linear System of Equations. Joint Advanced Student School (JASS-2009)
Iterative methods for Linear System of Equations Joint Advanced Student School (JASS-2009) Course #2: Numerical Simulation - from Models to Software Introduction In numerical simulation, Partial Differential
More informationA JACOBI DAVIDSON METHOD FOR SOLVING COMPLEX-SYMMETRIC EIGENVALUE PROBLEMS
A JACOBI DAVIDSON METHOD FOR SOLVING COMPLEX-SYMMETRIC EIGENVALUE PROBLEMS PETER ARBENZ AND MICHIEL E. HOCHSTENBACH Abstract. We discuss variants of the Jacobi Davidson method for solving the generalized
More informationA Jacobi Davidson type method for the generalized singular value problem
A Jacobi Davidson type method for the generalized singular value problem M. E. Hochstenbach a, a Department of Mathematics and Computing Science, Eindhoven University of Technology, P.O. Box 513, 5600
More informationIterative Methods for Solving A x = b
Iterative Methods for Solving A x = b A good (free) online source for iterative methods for solving A x = b is given in the description of a set of iterative solvers called templates found at netlib: http
More informationSolving Symmetric Indefinite Systems with Symmetric Positive Definite Preconditioners
Solving Symmetric Indefinite Systems with Symmetric Positive Definite Preconditioners Eugene Vecharynski 1 Andrew Knyazev 2 1 Department of Computer Science and Engineering University of Minnesota 2 Department
More informationLinear and Nonlinear Matrix Equations Arising in Model Reduction
International Conference on Numerical Linear Algebra and its Applications Guwahati, January 15 18, 2013 Linear and Nonlinear Matrix Equations Arising in Model Reduction Peter Benner Tobias Breiten Max
More informationA Chebyshev-based two-stage iterative method as an alternative to the direct solution of linear systems
A Chebyshev-based two-stage iterative method as an alternative to the direct solution of linear systems Mario Arioli m.arioli@rl.ac.uk CCLRC-Rutherford Appleton Laboratory with Daniel Ruiz (E.N.S.E.E.I.H.T)
More informationM.A. Botchev. September 5, 2014
Rome-Moscow school of Matrix Methods and Applied Linear Algebra 2014 A short introduction to Krylov subspaces for linear systems, matrix functions and inexact Newton methods. Plan and exercises. M.A. Botchev
More informationUniversiteit-Utrecht. Department. of Mathematics. The convergence of Jacobi-Davidson for. Hermitian eigenproblems. Jasper van den Eshof.
Universiteit-Utrecht * Department of Mathematics The convergence of Jacobi-Davidson for Hermitian eigenproblems by Jasper van den Eshof Preprint nr. 1165 November, 2000 THE CONVERGENCE OF JACOBI-DAVIDSON
More informationc 2006 Society for Industrial and Applied Mathematics
SIAM J. MATRIX ANAL. APPL. Vol. 28, No. 4, pp. 1069 1082 c 2006 Society for Industrial and Applied Mathematics INEXACT INVERSE ITERATION WITH VARIABLE SHIFT FOR NONSYMMETRIC GENERALIZED EIGENVALUE PROBLEMS
More informationReduction of nonlinear eigenproblems with JD
Reduction of nonlinear eigenproblems with JD Henk van der Vorst H.A.vanderVorst@math.uu.nl Mathematical Institute Utrecht University July 13, 2005, SIAM Annual New Orleans p.1/16 Outline Polynomial Eigenproblems
More informationIs there life after the Lanczos method? What is LOBPCG?
1 Is there life after the Lanczos method? What is LOBPCG? Andrew V. Knyazev Department of Mathematics and Center for Computational Mathematics University of Colorado at Denver SIAM ALA Meeting, July 17,
More informationUniversiteit-Utrecht. Department. of Mathematics. Jacobi-Davidson algorithms for various. eigenproblems. - A working document -
Universiteit-Utrecht * Department of Mathematics Jacobi-Davidson algorithms for various eigenproblems - A working document - by Gerard L.G. Sleipen, Henk A. Van der Vorst, and Zhaoun Bai Preprint nr. 1114
More informationMatrix Algorithms. Volume II: Eigensystems. G. W. Stewart H1HJ1L. University of Maryland College Park, Maryland
Matrix Algorithms Volume II: Eigensystems G. W. Stewart University of Maryland College Park, Maryland H1HJ1L Society for Industrial and Applied Mathematics Philadelphia CONTENTS Algorithms Preface xv xvii
More informationEfficient Implementation of Large Scale Lyapunov and Riccati Equation Solvers
Efficient Implementation of Large Scale Lyapunov and Riccati Equation Solvers Jens Saak joint work with Peter Benner (MiIT) Professur Mathematik in Industrie und Technik (MiIT) Fakultät für Mathematik
More informationA Model-Trust-Region Framework for Symmetric Generalized Eigenvalue Problems
A Model-Trust-Region Framework for Symmetric Generalized Eigenvalue Problems C. G. Baker P.-A. Absil K. A. Gallivan Technical Report FSU-SCS-2005-096 Submitted June 7, 2005 Abstract A general inner-outer
More informationA Jacobi-Davidson method for solving complex symmetric eigenvalue problems Arbenz, P.; Hochstenbach, M.E.
A Jacobi-Davidson method for solving complex symmetric eigenvalue problems Arbenz, P.; Hochstenbach, M.E. Published in: SIAM Journal on Scientific Computing DOI: 10.1137/S1064827502410992 Published: 01/01/2004
More informationIterative Rational Krylov Algorithm for Unstable Dynamical Systems and Generalized Coprime Factorizations
Iterative Rational Krylov Algorithm for Unstable Dynamical Systems and Generalized Coprime Factorizations Klajdi Sinani Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University
More informationMax Planck Institute Magdeburg Preprints
Thomas Mach Computing Inner Eigenvalues of Matrices in Tensor Train Matrix Format MAX PLANCK INSTITUT FÜR DYNAMIK KOMPLEXER TECHNISCHER SYSTEME MAGDEBURG Max Planck Institute Magdeburg Preprints MPIMD/11-09
More informationThe Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications
MAX PLANCK INSTITUTE Elgersburg Workshop Elgersburg February 11-14, 2013 The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications Timo Reis 1 Matthias Voigt 2 1 Department
More informationStudies on Jacobi-Davidson, Rayleigh Quotient Iteration, Inverse Iteration Generalized Davidson and Newton Updates
NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS Numer. Linear Algebra Appl. 2005; 00:1 6 [Version: 2002/09/18 v1.02] Studies on Jacobi-Davidson, Rayleigh Quotient Iteration, Inverse Iteration Generalized Davidson
More information1. Introduction. In this paper we consider variants of the Jacobi Davidson (JD) algorithm [27] for computing a few eigenpairs of
SIAM J. SCI. COMPUT. Vol. 25, No. 5, pp. 1655 1673 c 2004 Society for Industrial and Applied Mathematics A JACOBI DAVIDSON METHOD FOR SOLVING COMPLEX SYMMETRIC EIGENVALUE PROBLEMS PETER ARBENZ AND MICHIEL
More informationc 2009 Society for Industrial and Applied Mathematics
SIAM J. MATRIX ANAL. APPL. Vol. 31, No. 2, pp. 460 477 c 2009 Society for Industrial and Applied Mathematics CONTROLLING INNER ITERATIONS IN THE JACOBI DAVIDSON METHOD MICHIEL E. HOCHSTENBACH AND YVAN
More informationModel Order Reduction of Continuous LTI Large Descriptor System Using LRCF-ADI and Square Root Balanced Truncation
, July 1-3, 15, London, U.K. Model Order Reduction of Continuous LI Large Descriptor System Using LRCF-ADI and Square Root Balanced runcation Mehrab Hossain Likhon, Shamsil Arifeen, and Mohammad Sahadet
More informationChapter 7 Iterative Techniques in Matrix Algebra
Chapter 7 Iterative Techniques in Matrix Algebra Per-Olof Persson persson@berkeley.edu Department of Mathematics University of California, Berkeley Math 128B Numerical Analysis Vector Norms Definition
More informationA Jacobi Davidson-type projection method for nonlinear eigenvalue problems
A Jacobi Davidson-type projection method for nonlinear eigenvalue problems Timo Betce and Heinrich Voss Technical University of Hamburg-Harburg, Department of Mathematics, Schwarzenbergstrasse 95, D-21073
More informationModel order reduction of mechanical systems subjected to moving loads by the approximation of the input
Model order reduction of mechanical systems subjected to moving loads by the approximation of the input Alexander Vasilyev, Tatjana Stykel Institut für Mathematik, Universität Augsburg ModRed 2013 MPI
More informationFluid flow dynamical model approximation and control
Fluid flow dynamical model approximation and control... a case-study on an open cavity flow C. Poussot-Vassal & D. Sipp Journée conjointe GT Contrôle de Décollement & GT MOSAR Frequency response of an
More informationFinding Rightmost Eigenvalues of Large, Sparse, Nonsymmetric Parameterized Eigenvalue Problems
Finding Rightmost Eigenvalues of Large, Sparse, Nonsymmetric Parameterized Eigenvalue Problems AMSC 663-664 Final Report Minghao Wu AMSC Program mwu@math.umd.edu Dr. Howard Elman Department of Computer
More informationDELFT UNIVERSITY OF TECHNOLOGY
DELFT UNIVERSITY OF TECHNOLOGY REPORT -09 Computational and Sensitivity Aspects of Eigenvalue-Based Methods for the Large-Scale Trust-Region Subproblem Marielba Rojas, Bjørn H. Fotland, and Trond Steihaug
More informationMax Planck Institute Magdeburg Preprints
Peter Benner Patrick Kürschner Jens Saak Real versions of low-rank ADI methods with complex shifts MAXPLANCKINSTITUT FÜR DYNAMIK KOMPLEXER TECHNISCHER SYSTEME MAGDEBURG Max Planck Institute Magdeburg Preprints
More information